Positioning deviation correction method and system based on unmanned aerial vehicle

Abstract

The invention provides a positioning deviation correction method and system based on an unmanned aerial vehicle, and the method comprises the steps: determining a deviation vector of each telegraph pole through obtaining a first historical mark position and an actual position of a first telegraph pole; performing clustering processing according to the first historical mark position to obtain each first clustering center and a corresponding first cluster; respectively evaluating the offset vector of the corresponding telegraph pole in each first cluster to obtain a corresponding comprehensive offset vector; clustering a second historical position of a second telegraph pole needing to be corrected subsequently and the first historical mark position to obtain a second cluster, matching the first cluster with the second cluster, and determining a first cluster where the second telegraph pole planned to be repaired subsequently is located; and according to the comprehensive offset vector corresponding to the first cluster group where the second telegraph pole is located, the second historical position is corrected, so that other maintainers can accurately find the position of the telegraph pole during subsequent telegraph pole maintenance.

Description

Technical Field

[0001] The present invention belongs to the technical field of positioning and error correction, and in particular relates to a positioning and error correction method and system based on an unmanned aerial vehicle (UAV). Background Art

[0002] Drones are often used to assist in the maintenance of utility poles. However, when maintenance personnel bring drones to the location of the utility poles according to the guidance of historical survey maps, they often find that the location of the utility poles is not where the survey maps show them. For example, there is an error of tens of meters between the actual utility poles and the ones on the survey maps; the actual utility poles and the ones on the survey maps are not on the same side of the road. Summary of the Invention

[0003] Based on this, an embodiment of the present invention provides a positioning and correction method and system based on drones, which aims to correct the positioning of utility poles through drones when repairing utility poles, so that other maintenance workers can accurately find the location of utility poles when they repair the utility poles next time.

[0004] A first aspect of an embodiment of the present invention provides a positioning and deviation correction method based on a drone, the method comprising: Obtaining a first historical marker position of a first utility pole, measuring an actual position of the first utility pole using a drone, and determining an offset vector of the first utility pole based on the first historical marker position and the actual position; performing clustering processing on the first historical marker positions to obtain first cluster centers and corresponding first cluster groups; Evaluate the offset vectors of the corresponding electric poles in each first cluster respectively to obtain the corresponding comprehensive offset vector; Obtaining a second historical position of a second utility pole other than the first utility pole, and clustering the first historical marker position and the second historical position according to the number of first cluster centers to obtain each second cluster center and a corresponding second cluster group; Analyzing a positional relationship between the first cluster center and the second cluster center, matching the first cluster group with the second cluster group based on the positional relationship, and determining the first cluster group in which the second utility pole is located; The second historical position is corrected according to the comprehensive offset vector corresponding to the first cluster where the second utility pole is located.

[0005] Further, in the step of respectively evaluating the offset vectors of the corresponding utility poles in each first cluster group to obtain the corresponding comprehensive offset vector, according to the offset vectors of the corresponding utility poles in each first cluster group, the direction correlation and spatial position correlation of the offset vectors are determined, and evaluation is performed according to the direction correlation and spatial position correlation of the offset vectors.

[0006] Further, the step of determining the direction correlation and spatial position correlation of the offset vectors according to the offset vectors of the corresponding utility poles in each first cluster group, and performing evaluation according to the direction correlation and spatial position correlation of the offset vectors includes: Determine a first comprehensive offset vector associated with the direction correlation according to the offset vector of the utility pole and the principal component analysis method; Determine a second comprehensive offset vector associated with the spatial position correlation according to the offset vector of the utility pole and the distance weighting method based on the spatial position; According to the first comprehensive offset vector and the second comprehensive offset vector, the comprehensive offset vector is determined by using the weighted summation method, wherein the weight coefficient of the first comprehensive offset vector is determined by the contribution rate and direction concentration degree of the first principal component, and the weight coefficient of the second comprehensive offset vector is determined by the regression coefficient and Moran's I index.

[0007] Further, the step of determining a first comprehensive offset vector associated with the direction correlation according to the offset vector of the utility pole and the principal component analysis method includes: Regard the offset vector of the utility pole as a data point and calculate the covariance matrix; Perform eigenvalue decomposition on the covariance matrix to obtain the eigenvector corresponding to the maximum eigenvalue; Project all vectors onto the eigenvector corresponding to the maximum eigenvalue to determine the average projection length; Calculate the first comprehensive offset vector according to the average projection length and the eigenvector corresponding to the maximum eigenvalue.

[0008] Further, in the step of determining a second comprehensive offset vector associated with the spatial position correlation according to the offset vector of the utility pole and the distance weighting method based on the spatial position, the calculation formula is: ; Where is the second comprehensive offset vector, is the offset vector of the i-th utility pole, is the distance from the i-th utility pole to the corresponding first clustering center, is the distance attenuation index, and n is the total number of utility poles in the corresponding first cluster group.

[0009] Further, in the step where the weight coefficient of the first comprehensive offset vector is determined by the contribution rate and direction concentration degree of the first principal component, the calculation formula for the contribution rate of the first principal component is: ; is the maximum eigenvalue, is the j-th eigenvalue, and d is the total number of eigenvalues; The calculation formula for the direction concentration degree is: ; ; is the average direction vector, is the direction concentration degree.

[0010] Further, in the step where the weight coefficient of the second comprehensive offset vector is determined by the regression coefficient and Moran's I index, the calculation formula for linear regression is: ; is the magnitude of the i-th electric pole offset vector, is the intercept term, is the regression coefficient, is the random error term; The calculation formula for Moran's I index is: ; is Moran's I index, is the average offset magnitude, is the spatial weight matrix.

[0011] The second aspect of the embodiments of the present invention provides a positioning and deviation correction system based on an unmanned aerial vehicle, which is used to implement the positioning and deviation correction method based on an unmanned aerial vehicle described in the first aspect. The system includes: An acquisition module, configured to acquire the first historical marked position of the first electric pole, and measure the actual position of the first electric pole on-site by using an unmanned aerial vehicle, and determine the offset vector of the first electric pole according to the first historical marked position and the actual position; A first clustering processing module, configured to perform clustering processing on the first historical marked positions to obtain each first clustering center and the corresponding first cluster; An evaluation module, configured to evaluate the offset vectors of the electric poles corresponding to each first cluster respectively to obtain the corresponding comprehensive offset vectors; The second clustering processing module is used to obtain the second historical positions of the second utility poles except the first utility pole, and perform clustering processing on the first historical marked positions and the second historical positions according to the number of the first clustering centers, so as to obtain each second clustering center and the corresponding second cluster; The matching module is used to analyze the positional relationship between the first clustering center and the second clustering center, and match the first cluster and the second cluster according to the positional relationship to determine the first cluster where the second utility pole is located; The deviation correction module is used to correct the second historical position according to the comprehensive deviation vector corresponding to the first cluster where the second utility pole is located.

[0012] A third aspect of the embodiments of the present invention provides a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, the positioning and deviation correction method based on an unmanned aerial vehicle provided in the first aspect is implemented.

[0013] A fourth aspect of the embodiments of the present invention provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, the positioning and deviation correction method based on an unmanned aerial vehicle provided in the first aspect is implemented.

[0014] A positioning and deviation correction method and system based on an unmanned aerial vehicle provided in the embodiments of the present invention determine the deviation vectors of each utility pole by obtaining the first historical marked positions and the actual positions of the first utility poles; perform clustering processing according to the first historical marked positions to obtain each first clustering center and the corresponding first cluster; respectively evaluate the deviation vectors of the utility poles corresponding to each first cluster to obtain the corresponding comprehensive deviation vectors; then cluster the second historical positions of the second utility poles that need to be corrected later with the first historical marked positions to obtain the second cluster, and match the first cluster and the second cluster to determine the first cluster where the second utility poles planned to be overhauled later are located; correct the second historical positions according to the comprehensive deviation vectors corresponding to the first clusters where the second utility poles are located. Specifically, the positions of the utility poles that have not been overhauled can be corrected by the above method, so that when other maintenance workers perform utility pole maintenance, they can accurately find the positions of the utility poles, and at the same time, the workload of correcting the positions of the utility poles on the exploration map is reduced to a certain extent. Description of the Drawings

[0015] Figure 1 It is a flowchart of the implementation of a positioning and deviation correction method based on an unmanned aerial vehicle provided in Embodiment 1 of the present invention; Figure 2 It is a structural block diagram of a positioning and deviation correction system based on an unmanned aerial vehicle provided in Embodiment 2 of the present invention; Figure 3A structural block diagram of an electronic device provided in Embodiment 3 of the present invention. Detailed implementation manners

[0016] To facilitate the understanding of the present invention, the present invention will be described more comprehensively below with reference to the relevant drawings. Several embodiments of the present invention are shown in the drawings. However, the present invention can be implemented in many different forms and is not limited to the embodiments described herein. On the contrary, these embodiments are provided to make the disclosure of the present invention more thorough and comprehensive.

[0017] It should be noted that when an element is referred to as being "fixedly provided on" another element, it can be directly on the other element or there can also be an intermediate element. When an element is considered to be "connected" to another element, it can be directly connected to the other element or there may be an intermediate element at the same time. The terms "vertical", "horizontal", "left", "right" and similar expressions used herein are only for the purpose of illustration.

[0018] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which the present invention belongs. The terms used herein in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The term "and / or" used herein includes any and all combinations of one or more of the related listed items.

[0019] Embodiment 1 According to an embodiment of the present invention, an embodiment of a positioning and deviation correction method based on an unmanned aerial vehicle is provided. It should be noted that the steps shown in the flowchart of the drawings can be executed in a computer system such as a set of computer-executable instructions, and although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in a different order than here.

[0020] In Embodiment 1, a positioning and deviation correction method based on an unmanned aerial vehicle is provided, which can be used in an electronic device, such as a computer. Please refer to Figure 1 , Figure 1 which shows an implementation flowchart of a positioning and deviation correction method based on an unmanned aerial vehicle provided in Embodiment 1 of the present invention, specifically including steps S01 to S06.

[0021] Step S01, obtain the first historical marked position of the first utility pole, and actually measure the actual position of the first utility pole through an unmanned aerial vehicle, and determine the deviation vector of the first utility pole according to the first historical marked position and the actual position.

[0022] It can be understood that the first historical marked position is the position marked previously through manual exploration or other devices with lower precision, and there may be a deviation from the actual position. To correct the deviation, first, the offset vector of the first electric pole needs to be determined. It should be noted that the offset vector includes the offset distance and the offset direction. The offset distance is the distance between the first historical marked position and the actual position. The first historical marked position and the actual position can be understood as longitude and latitude positions. The offset direction is the direction indicated by taking the first historical marked position as the starting point and the actual position as the ending point.

[0023] Step S02: Cluster the first historical marked positions to obtain each first cluster center and the corresponding first cluster group.

[0024] It should be noted that only when the number of electric poles meets certain requirements, the comprehensive offset vector analyzed subsequently has certain reference significance. Therefore, when the number of electric poles is small, the actual positions obtained by the drone are used to re-calibrate the original positions in sequence. When the number of calibrations reaches a certain amount, that is, when there is a certain sample size, the evaluation of the comprehensive offset vector is carried out.

[0025] Among them, the k-means clustering algorithm is used for this clustering process, and each first cluster center can be obtained, and the first cluster group represented by each first cluster center can be obtained.

[0026] Step S03: Evaluate the offset vectors of the electric poles corresponding to each first cluster group respectively to obtain the corresponding comprehensive offset vector.

[0027] In the embodiment of the present invention, according to the offset vectors of the electric poles corresponding to each first cluster group, the direction correlation and spatial position correlation of the offset vectors are determined, and an evaluation is carried out according to the direction correlation and spatial position correlation of the offset vectors. Specifically, according to the offset vector of the electric pole and the principal component analysis method, the first comprehensive offset vector associated with the direction correlation is determined. It should be noted that first, the offset vectors of the electric poles are regarded as data points, and the covariance matrix is calculated. The calculation formula is: ; is the covariance matrix, n is the total number of electric poles in the corresponding first cluster group, is the offset vector of the i-th electric pole, is the mean value of the offset vectors; Perform eigenvalue decomposition on the covariance matrix to obtain the eigenvector corresponding to the largest eigenvalue; Project all vectors onto the eigenvector corresponding to the largest eigenvalue to determine the average projection length. The calculation formula is: ; ; is the eigenvector corresponding to the maximum eigenvalue, is the projected length of the i-th utility pole, is the average projected length; Calculate the first comprehensive offset vector according to the average projected length and the eigenvector corresponding to the maximum eigenvalue. The calculation formula is: ; is the first comprehensive offset vector; Determine the second comprehensive offset vector associated with the spatial position correlation according to the offset vector of the utility pole and the distance weighting method based on the spatial position. The calculation formula is: ; where, is the second comprehensive offset vector, is the offset vector of the i-th utility pole, is the distance from the i-th utility pole to the corresponding first cluster center, is the distance attenuation exponent, and n is the total number of utility poles within the corresponding first cluster; Determine the comprehensive offset vector by using the weighted summation method according to the first comprehensive offset vector and the second comprehensive offset vector. Among them, the weight coefficient of the first comprehensive offset vector is determined by the contribution rate and direction concentration degree of the first principal component, and the weight coefficient of the second comprehensive offset vector is determined by the regression coefficient and Moran's I index.

[0028] It should be noted that the calculation formula for the contribution rate of the first principal component is: ; is the maximum eigenvalue, is the j-th eigenvalue, and d is the total number of eigenvalues; The calculation formula for the direction concentration degree is: ; ; is the average direction vector, is the direction concentration degree. Among them, if the contribution rate is significantly higher than the threshold value, it is considered that there is an obvious directional trend in the offset, and the first principal component direction is the trend direction. In addition, when R≈1, it means that all vector directions are highly consistent and there is an obvious directional trend. When R≈0, it means that the vector directions are randomly distributed and there is no directional trend. In the embodiment of the present invention, a first mapping relationship is established, and this first mapping relationship is used to match the corresponding weight coefficient according to the contribution rate and direction concentration degree.

[0029] Further, the calculation formula for linear regression is: ; is the magnitude of the offset vector of the i-th utility pole, is the intercept term, is the regression coefficient, is the random error term. Understandably, testing for significance is to determine whether there is a true linear relationship between the independent variable and the dependent variable ; The calculation formula for Moran's I index is: ; is the Moran's I index, is the average offset magnitude, is the spatial weight matrix. Among them, when I > 0, it indicates positive autocorrelation, and the offsets are spatially clustered; when I ≈ 0, it indicates random distribution and no spatial correlation; when I < 0, it indicates negative autocorrelation, and the offset differences at adjacent positions are large.

[0030] Similarly, a second mapping relationship is established, which is used to match the corresponding weight coefficients according to the regression coefficient and Moran's I index.

[0031] Step S04: Obtain the second historical positions of the second utility poles other than the first utility pole, and cluster the first historical marked positions and the second historical positions according to the number of the first cluster centers to obtain each second cluster center and the corresponding second cluster.

[0032] Understandably, the second utility poles are the utility poles to be maintained in the subsequent plan. It should be noted that there are usually multiple voltage-level transmission lines leading out from the substation for the utility poles, and the utility poles are set along the planned paths of these transmission lines, showing a certain regularity. Therefore, clustering the utility poles at the first historical marked positions and the second historical positions according to the number of the first cluster centers, the obtained second cluster centers may have position offsets compared with the first cluster centers, but the impact is not significant.

[0033] Step S05: Analyze the positional relationship between the first cluster center and the second cluster center, and match the first cluster and the second cluster according to the positional relationship to determine the first cluster where the second utility pole is located.

[0034] Specifically, when the distance between the first clustering center and the second clustering center is less than a preset distance, the corresponding first cluster and second cluster are matched, and the first cluster to which each power pole to be overhauled in the subsequent plan belongs is determined.

[0035] Step S06: Correct the second historical position according to the comprehensive offset vector corresponding to the first cluster to which the second power pole belongs.

[0036] Since the comprehensive offset vector of the first cluster is known, and the second historical position of the second power pole to be overhauled in the corresponding second cluster is determined, the actual position of the power pole is deduced based on the second historical position and the corresponding comprehensive offset vector. It should be noted that during the actual overhaul by the overhauler, if the position deduction of the power pole is correct, it does not need to be corrected again. If there is an error in the deduction, it can be manually corrected again.

[0037] In summary, in the above embodiments of the present invention, the positioning and correction method based on an unmanned aerial vehicle. This method determines the offset vector of each power pole by obtaining the first historical marked position and the actual position of the first power pole; performs clustering processing based on the first historical marked position to obtain each first clustering center and the corresponding first cluster; evaluates the offset vectors of the power poles corresponding to each first cluster respectively to obtain the corresponding comprehensive offset vector; then clusters the second historical position of the second power pole that needs to be corrected later with the first historical marked position to obtain the second cluster, and matches the first cluster and the second cluster to determine the first cluster to which the second power pole to be overhauled in the subsequent plan belongs; corrects the second historical position according to the comprehensive offset vector corresponding to the first cluster to which the second power pole belongs. Specifically, through the above method, the position of the power pole that has not been overhauled can be corrected, so that when other overhaulers perform power pole overhaul, they can accurately find the position of the power pole. At the same time, to a certain extent, the workload of correcting the position of the power pole on the exploration map is reduced.

[0038] Embodiment 2 Please refer to Figure 2 , Figure 2 which is a structural block diagram of a positioning and correction system based on an unmanned aerial vehicle provided in Embodiment 2 of the present invention. The positioning and correction system 200 based on an unmanned aerial vehicle is used to implement the above embodiments and preferred implementation manners, and those that have been described will not be repeated. As used hereinafter, the term "module" can be a combination of software and / or hardware that realizes a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, implementation in hardware, or a combination of software and hardware is also possible and contemplated.

[0039] Specifically, the UAV-based positioning and deviation correction system 200 includes: an acquisition module 21, a first clustering processing module 22, an evaluation module 23, a second clustering processing module 24, a matching module 25, and a deviation correction module 26, where: The acquisition module 21 is configured to acquire the first historical marked position of the first utility pole, and field-measure the actual position of the first utility pole through a UAV, and determine the deviation vector of the first utility pole according to the first historical marked position and the actual position; The first clustering processing module 22 is configured to perform clustering processing on the first historical marked position to obtain each first clustering center and the corresponding first cluster; The evaluation module 23 is configured to respectively evaluate the deviation vectors of the utility poles corresponding to each first cluster to obtain the corresponding comprehensive deviation vectors, determine the direction correlation and spatial position correlation of the deviation vectors according to the deviation vectors of the utility poles corresponding to each first cluster, and perform evaluation according to the direction correlation and spatial position correlation of the deviation vectors; The second clustering processing module 24 is configured to acquire the second historical position of the second utility pole other than the first utility pole, and perform clustering processing on the first historical marked position and the second historical position according to the number of the first clustering centers to obtain each second clustering center and the corresponding second cluster; The matching module 25 is configured to analyze the positional relationship between the first clustering center and the second clustering center, match the first cluster and the second cluster according to the positional relationship, and determine the first cluster where the second utility pole is located; The deviation correction module 26 is configured to correct the second historical position according to the comprehensive deviation vector corresponding to the first cluster where the second utility pole is located.

[0040] Further, in some optional embodiments of the present invention, the evaluation module 23 includes: The first determination unit is configured to determine the first comprehensive deviation vector associated with the direction correlation according to the deviation vector of the utility pole and the principal component analysis method; The second determination unit is configured to determine the second comprehensive deviation vector associated with the spatial position correlation according to the deviation vector of the utility pole and the distance weighting method based on the spatial position. The calculation formula is: ; where is the second comprehensive deviation vector, is the deviation vector of the i-th utility pole, is the distance from the i-th utility pole to the corresponding first clustering center, is the distance attenuation exponent, and n is the total number of utility poles in the corresponding first cluster; A third determination unit, configured to determine the comprehensive offset vector by using a weighted summation method according to the first comprehensive offset vector and the second comprehensive offset vector, where the weight coefficient of the first comprehensive offset vector is determined by the contribution rate and direction concentration degree of the first principal component, and the weight coefficient of the second comprehensive offset vector is determined by the regression coefficient and Moran's I index. The calculation formula for the contribution rate of the first principal component is: ; is the largest eigenvalue, is the j-th eigenvalue, and d is the total number of eigenvalues; The calculation formula for the direction concentration degree is: ; ; is the average direction vector, is the direction concentration degree; The calculation formula for linear regression is: ; is the magnitude of the i-th electric pole offset vector, is the intercept term, is the regression coefficient, is the random error term; The calculation formula for Moran's I index is: ; is Moran's I index, is the average offset magnitude, is the spatial weight matrix.

[0041] Further, in some alternative embodiments of the present invention, the first determination unit includes: A first calculation subunit, configured to regard the offset vector of the electric pole as a data point and calculate the covariance matrix; A decomposition subunit, configured to perform eigenvalue decomposition on the covariance matrix to obtain the eigenvector corresponding to the largest eigenvalue; A projection subunit, configured to project all vectors onto the eigenvector corresponding to the largest eigenvalue to determine the average projection length; A second calculation subunit, configured to calculate the first comprehensive offset vector according to the average projection length and the eigenvector corresponding to the largest eigenvalue.

[0042] Embodiment III On the other hand, the present invention also provides an electronic device. Please refer to Figure 3, which shows the electronic device in the third embodiment of the present invention, including a memory 20, a processor 10, and a computer program 30 stored in the memory and executable on the processor. When the processor 10 executes the computer program 30, the above-mentioned positioning and deviation correction method based on the drone is implemented.

[0043] Among them, in some embodiments, the processor 10 may be a central processing unit (CPU), a controller, a microcontroller, a microprocessor, or other data processing chips, and is used to run the program code stored in the memory 20 or process data, such as executing an access restriction program, etc.

[0044] Among them, the memory 20 includes at least one type of readable storage medium, and the readable storage medium includes flash memory, a hard disk, a multimedia card, a card-type memory (such as an SD or DX memory, etc.), a magnetic memory, a magnetic disk, an optical disk, etc. The memory 20 may be an internal storage unit of the electronic device in some embodiments, such as the hard disk of the electronic device. The memory 20 may also be an external storage device of the electronic device in other embodiments, such as a plug-in hard disk equipped on the electronic device, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. Further, the memory 20 may also include both the internal storage unit and the external storage device of the electronic device. The memory 20 can be used not only to store the application software and various data of the electronic device, but also to temporarily store the data that has been output or will be output.

[0045] It should be noted that Figure 3 The shown structure does not constitute a limitation on the electronic device. In other embodiments, the electronic device may include fewer or more components than shown, or combine certain components, or have different component arrangements.

[0046] The embodiment of the present invention also proposes a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, the above-mentioned positioning and deviation correction method based on the drone is implemented.

[0047] Those skilled in the art will understand that the logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a definite sequence list of executable instructions for implementing logical functions, and can be specifically implemented in any computer-readable medium for use by an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processor, or other systems that can fetch and execute instructions from the instruction execution system, apparatus, or device), or used in combination with these instruction execution systems, apparatuses, or devices. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.

[0048] More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection part with one or more wirings (electronic device), a portable computer disk cartridge (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other media, followed by editing, interpretation, or other appropriate processing as necessary, and then stored in a computer memory.

[0049] It should be understood that various parts of the present invention can be implemented by hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, any one or a combination of the following techniques well known in the art can be used: discrete logic circuits with logic gate circuits for implementing logical functions on data signals, application-specific integrated circuits with suitable combinational logic gate circuits, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0050] In the description of this specification, the description with reference to terms such as "one embodiment", "some embodiments", "example", "specific example", or "some examples", etc., means that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in a suitable manner in any one or more embodiments or examples.

[0051] The above embodiments merely represent several implementation manners of the present invention. The description thereof is relatively specific and detailed, but it should not be construed as a limitation on the scope of the patent for the present invention. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present invention, several modifications and improvements can still be made, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the patent for the present invention shall be subject to the appended claims.

Claims

1. A positioning and deviation correction method based on an unmanned aerial vehicle, characterized in that, The method includes: Obtaining the first historical marked position of the first utility pole, and field-measuring the actual position of the first utility pole by using a drone, and determining the offset vector of the first utility pole according to the first historical marked position and the actual position; Performing clustering processing on the first historical marked position to obtain each first clustering center and the corresponding first cluster; Evaluating the offset vectors of the corresponding utility poles within each first cluster respectively to obtain the corresponding comprehensive offset vectors; Obtaining the second historical positions of the second utility poles other than the first utility pole, and performing clustering processing on the first historical marked position and the second historical positions according to the number of the first clustering centers to obtain each second clustering center and the corresponding second cluster; Analyzing the positional relationship between the first clustering center and the second clustering center, and matching the first cluster and the second cluster according to the positional relationship to determine the first cluster where the second utility pole is located; Correcting the second historical position according to the comprehensive offset vector corresponding to the first cluster where the second utility pole is located.

2. The positioning and deviation correction method based on an unmanned aerial vehicle according to claim 1, wherein In the step of evaluating the offset vectors of the corresponding utility poles within each first cluster respectively to obtain the corresponding comprehensive offset vectors, the direction correlation and the spatial position correlation of the offset vectors are determined according to the offset vectors of the corresponding utility poles within each first cluster, and the evaluation is performed according to the direction correlation and the spatial position correlation of the offset vectors.

3. The positioning and deviation correction method based on an unmanned aerial vehicle according to claim 2, wherein The step of determining the direction correlation and the spatial position correlation of the offset vectors according to the offset vectors of the corresponding utility poles within each first cluster and performing the evaluation according to the direction correlation and the spatial position correlation of the offset vectors includes: Determining a first comprehensive offset vector associated with the direction correlation according to the offset vector of the utility pole and the principal component analysis method; Determining a second comprehensive offset vector associated with the spatial position correlation according to the offset vector of the utility pole and the distance weighting method based on the spatial position; Determining the comprehensive offset vector by using the weighted summation method according to the first comprehensive offset vector and the second comprehensive offset vector, wherein the weight coefficient of the first comprehensive offset vector is determined by the contribution rate and the direction concentration degree of the first principal component, and the weight coefficient of the second comprehensive offset vector is determined by the regression coefficient and the Moran's I index.

4. The method for positioning and deviation correction based on an unmanned aerial vehicle according to claim 3, wherein The step of determining a first comprehensive offset vector associated with the direction correlation according to the offset vector of the utility pole and the principal component analysis method includes: Regarding the offset vector of the utility pole as a data point and calculating the covariance matrix; Performing eigenvalue decomposition on the covariance matrix to obtain the eigenvector corresponding to the maximum eigenvalue; Projecting all vectors onto the eigenvector corresponding to the maximum eigenvalue to determine the average projection length; Calculating the first comprehensive offset vector according to the average projection length and the eigenvector corresponding to the maximum eigenvalue.

5. The positioning and deviation correction method based on an unmanned aerial vehicle according to claim 3, wherein, In the step of determining a second comprehensive offset vector associated with the spatial position correlation according to the offset vector of the utility pole and the distance weighting method based on the spatial position, the calculation formula is: ； Among them, is the second comprehensive offset vector, is the offset vector of the i-th utility pole, is the distance from the i-th utility pole to the corresponding first clustering center, is the distance attenuation exponent, and n is the total number of utility poles within the corresponding first cluster.

6. The method for positioning and deviation correction based on an unmanned aerial vehicle according to claim 5, wherein In the step where the weight coefficient of the first comprehensive offset vector is determined by the contribution rate and direction concentration degree of the first principal component, the calculation formula for the contribution rate of the first principal component is: ； is the maximum eigenvalue, is the j-th eigenvalue, and d is the total number of eigenvalues; The calculation formula for the direction concentration degree is: ； ； is the average direction vector, is the direction concentration degree.

7. The method for positioning and deviation correction based on an unmanned aerial vehicle according to claim 6, wherein In the step where the weight coefficient of the second comprehensive offset vector is determined by the regression coefficient and Moran's I index, the calculation formula for linear regression is: ； is the magnitude of the offset vector of the i-th utility pole, is the intercept term, is the regression coefficient, is the random error term; The calculation formula for Moran's I index is: ； is the Moran's I index, is the average offset size, is the spatial weight matrix.

8. An unmanned aerial vehicle-based positioning and deviation correction system, characterized in that, To implement the UAV-based positioning and deviation correction method according to any one of claims 1-7, the system includes: An acquisition module, configured to acquire the first historical marked position of the first electric pole, and field-measure the actual position of the first electric pole by using a UAV, and determine the offset vector of the first electric pole according to the first historical marked position and the actual position; A first clustering processing module, configured to perform clustering processing on the first historical marked position to obtain each first clustering center and the corresponding first cluster; An evaluation module, configured to evaluate the offset vectors of the electric poles corresponding to each first cluster respectively to obtain the corresponding comprehensive offset vectors; A second clustering processing module, configured to acquire the second historical positions of the second electric poles other than the first electric pole, and perform clustering processing on the first historical marked position and the second historical positions according to the number of the first clustering centers to obtain each second clustering center and the corresponding second cluster; A matching module, configured to analyze the positional relationship between the first clustering center and the second clustering center, and match the first cluster and the second cluster according to the positional relationship to determine the first cluster where the second electric pole is located; A deviation correction module, configured to correct the second historical position according to the comprehensive offset vector corresponding to the first cluster where the second electric pole is located.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by a processor, it implements the UAV-based positioning and deviation correction method according to any one of claims 1-7.

10. An electronic device, characterized in that, It includes a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, it implements the UAV-based positioning and deviation correction method according to any one of claims 1-7.

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