A method, system, device and medium for improving alignment accuracy of a Beidou coordinate system

Abstract

The application discloses a method, system, device and medium for improving alignment accuracy of Beidou coordinate system, mainly relates to the technical field of Beidou coordinate system, and aims to solve the problem of low alignment accuracy of Beidou coordinate system of unmanned aerial vehicle caused by environmental data. It comprises the following steps: through the inspection unmanned aerial vehicle carrying the Beidou positioning device, according to the preset inspection frequency N, collecting N times of actual positioning data of each preset observation point on the inspection route; based on the N times of actual positioning data of the preset observation point and the pre-stored positioning data, generating a fitting positioning deviation; recording the preset observation point whose fitting positioning deviation is greater than the preset deviation threshold, and then collecting the corresponding preset observation point again through the unmanned aerial vehicle carrying the camera device to obtain environmental image data; identifying the environmental image data to determine the interference factors of the preset observation point; establishing the corresponding relationship between the environmental image data and the interference factors, and then correcting the Beidou coordinate system of the actual unmanned aerial vehicle through the corresponding relationship.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of Beidou coordinate system technology, and particularly relates to a method, system, device and medium for improving alignment accuracy of a Beidou coordinate system. BACKGROUND

[0002] The Beidou satellite navigation system is a global satellite navigation system and the third mature satellite navigation system after GPS and GLONASS. The Beidou satellite navigation system is composed of a space segment, a ground segment and a user segment, and can provide high-precision and high-reliability positioning, navigation and timing services for various users around the world at all times, and has short message communication capability.

[0003] The system mainly transmits accurate signals by satellites, and the receiver calculates its own position after receiving the signals. However, in the actual process of application in unmanned aerial vehicles, due to the difference of specific environmental data of unmanned aerial vehicles, the influence of the accuracy of the Beidou coordinate system corresponding to different positions in the same area is also different.

[0004] Therefore, there is an urgent need for a method, system, device and medium for improving alignment accuracy of a Beidou coordinate system to solve the problem of low alignment accuracy of the Beidou coordinate system of unmanned aerial vehicles caused by environmental data. SUMMARY

[0005] In view of the above deficiencies of the prior art, the present application provides a method, system, device and medium for improving alignment accuracy of a Beidou coordinate system to solve the problem of low alignment accuracy of the Beidou coordinate system of unmanned aerial vehicles caused by environmental data.

[0006] In a first aspect, the present application provides a method for improving alignment accuracy of a Beidou coordinate system, the method comprising:

[0007] obtaining a positioning area, generating a positioning area inspection route, and issuing the inspection route to an inspection unmanned aerial vehicle; collecting N times of actual positioning data of each preset observation point on the inspection route according to a preset inspection frequency N by the inspection unmanned aerial vehicle carrying a Beidou positioning device; generating a fitting positioning deviation based on the N times of actual positioning data of the preset observation point and pre-stored positioning data; recording the preset observation point whose fitting positioning deviation is greater than a preset deviation threshold, and then collecting the corresponding preset observation point again by the unmanned aerial vehicle carrying a camera to obtain environmental image data; identifying the environmental image data to determine the interference factors of the preset observation point; establishing a corresponding relationship between the environmental image data and the interference factors, and then correcting the Beidou coordinate system of the actual unmanned aerial vehicle through the corresponding relationship.

[0008] The method for improving alignment accuracy of Beidou coordinate system provided by the embodiment of the application can more comprehensively understand the actual situation of the positioning area, thereby reducing positioning error and improving alignment accuracy of the Beidou coordinate system, by acquiring a positioning area and generating an inspection route, and collecting actual positioning data of preset observation points by the inspection unmanned aerial vehicle multiple times. The environment image data of these points is collected by the unmanned aerial vehicle carrying a camera. This helps to accurately identify interference factors affecting positioning accuracy, such as building shielding, multipath effect, electromagnetic interference, etc. Based on these interference factors, the Beidou coordinate system can be corrected in a targeted manner to further improve positioning accuracy. By establishing a corresponding relationship between the environment image data and the interference factors, intelligent coordinate system correction can be realized. This means that when the unmanned aerial vehicle encounters similar environmental conditions again, the system can automatically adjust the coordinate system to reduce error. The preset inspection frequency and inspection route ensure sufficient data collection while avoiding unnecessary repetition. The inspection process is optimized, and resource utilization efficiency is improved.

[0009] In an implementation manner of the application, the positioning area is acquired, the positioning area inspection route is generated, and the positioning area inspection route is sent to the inspection unmanned aerial vehicle, specifically including:

[0010] The positioning area is acquired, the positioning area is divided into a plurality of square regions of n*n, fixed reference objects of each square region are obtained, each fixed reference object is determined as a preset observation point, a positioning area inspection route passing through each fixed reference object is generated, and the positioning area inspection route is sent to the inspection unmanned aerial vehicle; wherein n represents a preset length.

[0011] In an implementation manner of the application, environment image data recognition is performed to determine interference factors of the preset observation points, specifically including:

[0012] The environment image data is input into a preset trained image recognition algorithm, and the interference factors and accuracy rate output by the preset trained image recognition algorithm are determined; wherein the output interference factors are at least building shielding and multipath effect.

[0013] When the accuracy rate is less than a preset accuracy threshold, the interference factor is determined to be electromagnetic interference.

[0014] In an implementation manner of the application, a corresponding relationship between the environment image data and the interference factors is established, and then the Beidou coordinate system of the actual unmanned aerial vehicle is corrected through the corresponding relationship, specifically including:

[0015] The correspondence between the environmental image data and the interference factors is uploaded to the unmanned aerial vehicle data uploading system; real-time collected images are uploaded to the unmanned aerial vehicle data uploading system through the current unmanned aerial vehicle; the real-time collected images are compared with the environmental image data in the correspondence through the unmanned aerial vehicle data uploading system to determine the similarity between the real-time collected images and the environmental image data; when the similarity is greater than a preset similarity threshold, the corresponding interference factor is obtained; when the interference factor is building obstruction, the current unmanned aerial vehicle increases the flight height by a preset height, and the original Beidou coordinate system is covered by the Beidou coordinate system at the current height; when the interference factor is multipath effect, a signal filter carried by the current unmanned aerial vehicle is started to collect the Beidou coordinate system again to correct the original Beidou coordinate system; when the interference factor is electromagnetic interference, an antenna gain device carried by the current unmanned aerial vehicle is started to collect the Beidou coordinate system again to correct the original Beidou coordinate system.

[0016] In a second aspect, the present application provides a system for improving the alignment accuracy of the Beidou coordinate system, which comprises:

[0017] The issuing module is configured to obtain a positioning area, generate a positioning area inspection route, and issue the inspection route to an inspection unmanned aerial vehicle. The collecting module is configured to collect N times of actual positioning data of each preset observation point on the inspection route by the inspection unmanned aerial vehicle carrying a Beidou positioning device according to a preset inspection frequency N. The fitting module is configured to generate a fitting positioning deviation based on the N times of actual positioning data of the preset observation point and pre-stored positioning data. The obtaining module is configured to record the preset observation point with a fitting positioning deviation greater than a preset deviation threshold, and then collect the corresponding preset observation point again by the unmanned aerial vehicle carrying a camera to obtain environmental image data. The correction module is configured to identify the environmental image data, determine the interference factors of the preset observation point, establish a correspondence between the environmental image data and the interference factors, and then correct the Beidou coordinate system of the actual unmanned aerial vehicle through the correspondence.

[0018] In an implementation manner of the present application, the issuing module comprises an issuing unit configured to obtain a positioning area, divide the positioning area into a plurality of n*n square areas, obtain fixed reference objects of each square area, determine each fixed reference object as a preset observation point, generate a positioning area inspection route passing through each fixed reference object, and issue the positioning area inspection route to an inspection unmanned aerial vehicle; wherein n represents a preset length.

[0019] In an implementation manner of the present application, the correction module comprises a determination unit configured to determine the interference factors of the preset observation point through a preset trained image recognition algorithm, and determine the accuracy of the output interference factors through the preset trained image recognition algorithm; wherein the output interference factors include at least building obstruction and multipath effect.

[0020] The environmental image data is input into a preset trained image recognition algorithm, the interference factors and the accuracy of the output are determined through the preset trained image recognition algorithm; wherein the output interference factors include at least building obstruction and multipath effect.

[0021] When the accuracy is less than the preset accuracy threshold, the interference factor is determined as electromagnetic interference.

[0022] In an implementation form of the application, the correction module comprises a running unit.

[0023] The correspondence between the environmental image data and the interference factor is uploaded to the unmanned aerial vehicle data uploading system.

[0024] The real-time collected image is uploaded to the unmanned aerial vehicle data uploading system.

[0025] The real-time collected image is compared with the environmental image data in the correspondence through the unmanned aerial vehicle data uploading system to determine the similarity between the real-time collected image and the environmental image data.

[0026] When the similarity is greater than a preset similarity threshold, the corresponding interference factor is obtained.

[0027] When the interference factor is building obstruction, the current unmanned aerial vehicle increases the flight height by a preset height, and the original Beidou coordinate system is covered by the Beidou coordinate system at the current height.

[0028] When the interference factor is multipath effect, a signal filter carried by the current unmanned aerial vehicle is started to collect the Beidou coordinate system to correct the original Beidou coordinate system.

[0029] When the interference factor is electromagnetic interference, an antenna gain device carried by the current unmanned aerial vehicle is started to collect the Beidou coordinate system to correct the original Beidou coordinate system.

[0030] In a third aspect, the application provides a device for improving the alignment accuracy of the Beidou coordinate system, the device comprising:

[0031] a processor;

[0032] and a memory having executable code stored thereon, when the executable code is executed, causing the processor to perform the method for improving the alignment accuracy of the Beidou coordinate system according to any one of the above.

[0033] In a fourth aspect, the application provides a non-volatile computer storage medium having computer instructions stored thereon, when the computer instructions are executed, implementing the method for improving the alignment accuracy of the Beidou coordinate system according to any one of the above.

[0034] Those skilled in the art can understand that the application has at least the following beneficial effects:

[0035] The application discloses a method, system, device and medium for improving alignment accuracy of a Beidou coordinate system. The method comprises the following steps: acquiring a positioning area and generating an inspection route, and collecting actual positioning data of preset observation points by using an inspection unmanned aerial vehicle multiple times, so that the actual situation of the positioning area can be comprehensively understood, the positioning error is reduced, and the alignment accuracy of the Beidou coordinate system is improved. The unmanned aerial vehicle carrying a camera device collects environmental image data of these points. This helps to accurately identify interference factors affecting positioning accuracy, such as building shielding, multipath effect, electromagnetic interference, etc. Based on these interference factors, the Beidou coordinate system can be corrected in a targeted manner to further improve the positioning accuracy. By establishing a corresponding relationship between the environmental image data and the interference factors, intelligent coordinate system correction can be realized. When the unmanned aerial vehicle encounters similar environmental conditions again, the system can automatically adjust the coordinate system to reduce errors. The preset inspection frequency and inspection route ensure sufficient data collection while avoiding unnecessary repetitive work. The inspection process is optimized, and the resource utilization efficiency is improved. BRIEF DESCRIPTION OF DRAWINGS

[0036] Some embodiments of the present disclosure will be described below with reference to the accompanying drawings, in which:

[0037] Figure 1 is a method flowchart for improving alignment accuracy of a Beidou coordinate system provided by an embodiment of the application.

[0038] Figure 2 is a system internal structure schematic diagram for improving alignment accuracy of a Beidou coordinate system provided by an embodiment of the application.

[0039] Figure 3 is a device internal structure schematic diagram for improving alignment accuracy of a Beidou coordinate system provided by an embodiment of the application. DETAILED DESCRIPTION

[0040] Those skilled in the art should understand that the embodiments described below are only preferred embodiments of the present disclosure, and do not represent that the present disclosure can only be implemented by the preferred embodiments. The preferred embodiments are only used to explain the technical principles of the present disclosure, and are not used to limit the protection scope of the present disclosure. Based on the preferred embodiments provided by the present disclosure, all other embodiments obtained by those of ordinary skill in the art without creative labor should fall within the protection scope of the present disclosure.

[0041] It should also be noted that the terms "comprising", "containing", or any other similar term are intended to encompass the inclusion of one or more elements, features, or steps without excluding the presence of other elements, features, or steps. In other words, the terms "comprising", "containing", or any other similar term are intended to be equivalent to the term "including" or "having". In addition, the term "comprising" is intended to mean that the process, method, article, or apparatus that includes the recited elements, features, or steps can also include other elements, features, or steps not expressly listed or inherent to such process, method, article, or apparatus.

[0042] The technical solutions of the embodiments of the present application will be described in detail below with reference to the accompanying drawings.

[0043] The embodiments provide a method for improving the alignment accuracy of the Beidou coordinate system, as shown in Figure 1 The method provided by the embodiments of the present application mainly includes the following steps:

[0044] Step 110: obtaining a positioning area, generating a positioning area inspection route, and issuing the inspection route to an inspection unmanned aerial vehicle.

[0045] It should be noted that the positioning area is a training area for training the unmanned aerial vehicle, and in addition, the positioning area is not limited to a fixed area. The scheme for generating the positioning area inspection route can be an existing scheme.

[0046] In some embodiments, this step can be specifically: obtaining a positioning area, dividing the positioning area into a plurality of n*n square areas, obtaining fixed reference objects of each square area; determining each fixed reference object as a preset observation point, generating a positioning area inspection route passing through each fixed reference object, and issuing the positioning area inspection route to an inspection unmanned aerial vehicle; wherein n represents a preset length.

[0047] It should be noted that the specific position of the fixed reference object is known in advance, that is, the pre-stored positioning data of the preset observation point below.

[0048] Step 120: acquiring N times of actual positioning data of each preset observation point on the inspection route by the inspection unmanned aerial vehicle carrying the Beidou positioning device according to a preset inspection frequency N.

[0049] It should be noted that the inspection frequency can be determined according to actual conditions.

[0050] Step 130: generating a fitting positioning deviation based on the N times of actual positioning data of the preset observation point and the pre-stored positioning data.

[0051] It should be noted that this step can be specifically:

[0052] First, collect N times of actual positioning data of the preset observation points. These data usually include coordinate information such as longitude, latitude, and height, as well as possible time stamps and other related metadata.

[0053] At the same time, obtain pre-stored positioning data, which are usually reference positioning information considered to be relatively accurate stored in the system in advance.

[0054] Clean the collected actual positioning data to remove outliers, repeated values, or obviously incorrect data points.

[0055] For each preset observation point, calculate the deviation between its N times of actual positioning data and the pre-stored positioning data. This usually involves calculating the difference between the two sets of data on the coordinate axes such as longitude, latitude, and height.

[0056] Statistical methods such as mean, standard deviation, etc. can be used to summarize these deviations to obtain the average deviation (fitted positioning deviation) of each observation point.

[0057] Step 140, record the preset observation points with fitted positioning deviation greater than the preset deviation threshold, and then collect the corresponding preset observation points of the preset observation points again by the unmanned aerial vehicle carrying the camera to obtain environmental image data.

[0058] It should be noted that the preset observation points are the preset observation points corresponding to the preset observation points with fitted positioning deviation greater than the preset deviation threshold.

[0059] Step 150, perform environmental image data recognition to determine the interference factors of the preset observation points; establish a corresponding relationship between the environmental image data and the interference factors, and then correct the Beidou coordinate system of the actual unmanned aerial vehicle through the corresponding relationship.

[0060] Among them, the environmental image data recognition determines the interference factors of the preset observation points, which can be specifically:

[0061] Input the environmental image data into the preset trained image recognition algorithm to determine the output interference factors and accuracy through the preset trained image recognition algorithm; wherein the output interference factors are at least building shielding and multipath effect; when the accuracy is less than the preset accuracy threshold, the interference factor is determined to be electromagnetic interference.

[0062] It should be noted that the training process of the preset trained image recognition algorithm is to input a number of images labeled with specific interference factors as sample data into the initial image recognition network to obtain the trained image recognition algorithm; in the actual application process, input the environmental image data into an odd number of trained image recognition algorithms to obtain the output results, and determine the accuracy of the output interference factors according to the proportion of each specific output interference factor.

[0063] The corresponding relationship between the environment image data and the interference factors is established, and then the Beidou coordinate system of the actual unmanned aerial vehicle is corrected through the corresponding relationship, which can be specifically:

[0064] The corresponding relationship between the environment image data and the interference factors is uploaded to the unmanned aerial vehicle data uploading system; real-time collected images are uploaded to the unmanned aerial vehicle data uploading system through the current unmanned aerial vehicle; the real-time collected images are compared with the environment image data in the corresponding relationship through the unmanned aerial vehicle data uploading system to determine the similarity between the real-time collected images and the environment image data.

[0065] When the similarity is greater than the preset similarity threshold, the interference factors corresponding to the environment image data with the similarity greater than the preset similarity threshold are obtained; when the interference factors are building occlusion, the current unmanned aerial vehicle increases the flight height by a preset height, and the original Beidou coordinate system is covered by the Beidou coordinate system at the current height; when the interference factors are multipath effect, a signal filter carried by the current unmanned aerial vehicle is started to collect the Beidou coordinate system to correct the original Beidou coordinate system again; when the interference factors are electromagnetic interference, an antenna gain device carried by the current unmanned aerial vehicle is started to collect the Beidou coordinate system to correct the original Beidou coordinate system again.

[0066] In addition, the present application Figure 2 A system for improving the alignment accuracy of the Beidou coordinate system is provided in the embodiments of the present application. As shown in Figure 2 The system provided by the embodiments of the present application mainly includes:

[0067] The issuing module 210 is configured to obtain a positioning area, generate a positioning area inspection route, and issue the inspection route to an inspection unmanned aerial vehicle.

[0068] The issuing module 210 includes an issuing unit configured to obtain a positioning area, divide the positioning area into a plurality of n*n square areas, obtain fixed reference objects in each square area, determine each fixed reference object as a preset observation point, generate a positioning area inspection route passing through each fixed reference object, and issue the positioning area inspection route to an inspection unmanned aerial vehicle; wherein n represents a preset length.

[0069] The collecting module 220 is configured to collect N times of actual positioning data of each preset observation point on the inspection route by an inspection unmanned aerial vehicle carrying a Beidou positioning device according to a preset inspection frequency N.

[0070] The fitting module 230 is configured to generate a fitting positioning deviation based on the N times of actual positioning data of the preset observation point and pre-stored positioning data.

[0071] The obtaining module 240 is configured to record the preset observation point with the fitting positioning deviation greater than a preset deviation threshold, and then collect the corresponding preset observation point again by an unmanned aerial vehicle carrying a camera to obtain environment image data.

[0072] The correction module 250 is configured to identify the environmental image data, determine the interference factors of the preset observation point, establish the correspondence between the environmental image data and the interference factors, and correct the Beidou coordinate system of the actual unmanned aerial vehicle through the correspondence.

[0073] The correction module 250 includes a determination unit configured to input the environmental image data into a preset trained image recognition algorithm, determine the output interference factors and the accuracy through the preset trained image recognition algorithm, wherein the output interference factors are at least the building blockage and the multipath effect, and when the accuracy is less than a preset accuracy threshold, the interference factor is determined to be electromagnetic interference.

[0074] The correction module 250 includes a running unit configured to upload the correspondence between the environmental image data and the interference factors to an unmanned aerial vehicle data uploading system, upload the real-time collected image of the current unmanned aerial vehicle to the unmanned aerial vehicle data uploading system, compare the real-time collected image with the environmental image data in the correspondence through the unmanned aerial vehicle data uploading system to determine the similarity between the real-time collected image and the environmental image data, obtain the corresponding interference factor when the similarity is greater than a preset similarity threshold, increase the flight height of the current unmanned aerial vehicle by a preset height when the interference factor is the building blockage, cover the original Beidou coordinate system by the Beidou coordinate system at the current height, start the signal filter carried by the current unmanned aerial vehicle to collect the Beidou coordinate system to correct the original Beidou coordinate system again when the interference factor is the multipath effect, and start the antenna gain device carried by the current unmanned aerial vehicle to collect the Beidou coordinate system to correct the original Beidou coordinate system again when the interference factor is the electromagnetic interference.

[0075] The above is a method embodiment in the present application. Based on the same inventive concept, the present application embodiment also provides a device for improving the alignment accuracy of the Beidou coordinate system. As shown in the figure, the device includes a processor and a memory having executable code stored thereon, when the executable code is executed, the processor executes a method for improving the alignment accuracy of the Beidou coordinate system as described above. Figure 3

[0076] ​Specifically, the server obtains a positioning area, generates a positioning area inspection route, and sends the inspection route to an inspection unmanned aerial vehicle; the inspection unmanned aerial vehicle carrying a Beidou positioning device collects N times of actual positioning data of each preset observation point on the inspection route according to a preset inspection frequency N; a fitting positioning deviation is generated based on the N times of actual positioning data of the preset observation point and pre-stored positioning data; a preset observation point with a fitting positioning deviation greater than a preset deviation threshold is recorded, and then the corresponding preset observation point is collected again by the unmanned aerial vehicle carrying a camera to obtain environmental image data; environmental image data recognition is performed to determine the interference factors of the preset observation point; a corresponding relationship between the environmental image data and the interference factors is established, and then the Beidou coordinate system of the actual unmanned aerial vehicle is corrected through the corresponding relationship.

[0077] In addition, the embodiment of the present application further provides a non-volatile computer storage medium, which stores executable instructions, and when the executable instructions are executed, a method for improving the Beidou coordinate system alignment accuracy is realized.

[0078] So far, the technical solutions of the present disclosure have been described in combination with the foregoing embodiments, but those skilled in the art can easily understand that the protection scope of the present disclosure is not limited to these specific embodiments. Without deviating from the technical principles of the present disclosure, those skilled in the art can split and combine the technical solutions in the above-described embodiments, or make equivalent changes or replacements to related technical features, and any changes, equivalent replacements, improvements, etc. made within the technical concept and / or technical principles of the present disclosure will fall within the protection scope of the present disclosure.

Claims

1. A method for improving the alignment accuracy of the BeiDou coordinate system, characterized in that, The method includes: Obtain the location area, generate the inspection route for the location area, and send the inspection route to the inspection drone; By using an inspection drone equipped with a Beidou positioning device, N actual positioning data of each preset observation point on the inspection route are collected according to the preset number of inspections N. Based on N actual positioning data and pre-stored positioning data of preset observation points, a fitting positioning deviation is generated; Record the preset observation points where the fitting positioning deviation is greater than the preset deviation threshold, and then use a drone carrying a camera device to collect the corresponding preset observation points again to obtain environmental image data; Environmental image data identification is performed to determine interference factors at preset observation points; specifically including: Environmental image data is input into a pre-trained image recognition algorithm. The algorithm determines the output interference factors and accuracy. The output interference factors include building occlusion and multipath effect. When the accuracy is less than a preset accuracy threshold, the interference factor is determined to be electromagnetic interference. Establish the correspondence between environmental image data and interference factors, and then correct the BeiDou coordinate system of the actual UAV through the correspondence; specifically including: The system uploads the correspondence between environmental image data and interference factors to the UAV data upload system; it also uploads real-time acquired images from the current UAV to the UAV data upload system; the UAV data upload system compares the real-time acquired images with the environmental image data in the correspondence to determine the similarity between the real-time acquired images and the environmental image data; when the similarity is greater than a preset similarity threshold, the corresponding interference factor is obtained; when the interference factor is building obstruction, the current UAV increases its flight altitude to a preset altitude, using the BeiDou coordinate system at the current altitude to cover the original BeiDou coordinate system; when the interference factor is multipath effect, the signal filter carried by the current UAV is activated, and the BeiDou coordinate system is re-acquired to correct the original BeiDou coordinate system; when the interference factor is electromagnetic interference, the antenna gain device carried by the current UAV is activated, and the BeiDou coordinate system is re-acquired to correct the original BeiDou coordinate system.

2. The method for improving the alignment accuracy of the BeiDou coordinate system according to claim 1, characterized in that, Obtain the location area, generate an inspection route for the location area, and send the inspection route to the inspection drone. Specifically, this includes: The positioning area is obtained and divided into several n*n square areas. Fixed reference objects are obtained for each square area. Each fixed reference object is determined as a preset observation point. A positioning area inspection route passing through each fixed reference object is generated and the positioning area inspection route is sent to the inspection drone. Here, n represents the preset length.

3. A system for improving the alignment accuracy of the BeiDou coordinate system, characterized in that, The system includes: The distribution module is used to obtain the positioning area, generate the inspection route for the positioning area, and distribute the inspection route to the inspection drone. The data acquisition module is used to collect N actual positioning data of each preset observation point on the inspection route by using an inspection drone equipped with a Beidou positioning device, according to a preset number of inspections N. The fitting module is used to generate a fitting positioning deviation based on N actual positioning data and pre-stored positioning data of preset observation points. The module is used to record preset observation points where the fitting positioning deviation is greater than a preset deviation threshold, and then the corresponding preset observation points are collected again by a drone carrying a camera device to obtain environmental image data. The correction module is used to identify environmental image data, determine interference factors at preset observation points, establish the correspondence between environmental image data and interference factors, and then correct the BeiDou coordinate system of the actual UAV through the correspondence. The correction module includes a determination unit, which inputs environmental image data into a pre-trained image recognition algorithm and determines the output interference factors and accuracy through the pre-trained image recognition algorithm. The output interference factors include building occlusion and multipath effect. When the accuracy is less than a preset accuracy threshold, the interference factor is determined to be electromagnetic interference. The correction module includes an operation unit; it uploads the correspondence between environmental image data and interference factors to the UAV data upload system; it uploads real-time acquired images from the current UAV to the UAV data upload system; it compares the real-time acquired images with the environmental image data in the correspondence through the UAV data upload system to determine the similarity between the real-time acquired images and the environmental image data; when the similarity is greater than a preset similarity threshold, it acquires the corresponding interference factor; when the interference factor is building obstruction, the current UAV increases its flight altitude to a preset altitude, covering the original BeiDou coordinate system with the BeiDou coordinate system at the current altitude; when the interference factor is multipath effect, it activates the signal filter carried by the current UAV and re-acquires the BeiDou coordinate system to correct the original BeiDou coordinate system; when the interference factor is electromagnetic interference, it activates the antenna gain device carried by the current UAV and re-acquires the BeiDou coordinate system to correct the original BeiDou coordinate system.

4. The system for improving the alignment accuracy of the BeiDou coordinate system according to claim 3, characterized in that, The distribution module includes a distribution unit, which is used to obtain the positioning area, divide the positioning area into several n*n square areas, obtain fixed reference objects for each square area, determine each fixed reference object as a preset observation point, generate a positioning area inspection route passing through each fixed reference object, and distribute the positioning area inspection route to the inspection drone; where n represents the preset length.

5. A device for improving the alignment accuracy of the BeiDou coordinate system, characterized in that, The device includes: processor; And a memory storing executable code, which, when executed, causes the processor to perform a method for improving the alignment accuracy of the BeiDou coordinate system as described in any one of claims 1-2.

6. A non-volatile computer storage medium, characterized in that, It stores computer instructions, which, when executed, implement a method for improving the alignment accuracy of the BeiDou coordinate system as described in any one of claims 1-2.

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