Unmanned aerial vehicle inhabitation power line optimization method based on magnetoresistive sensor array and related device

Abstract

The invention discloses an unmanned aerial vehicle inhabitation power line optimization method based on a magnetoresistive sensor array and a related device, and relates to the technical field of unmanned aerial vehicle wireless charging, and the method comprises the steps: collecting the magnetic field information of a power line at a target position based on the magnetoresistive sensor array on an unmanned aerial vehicle; based on the Biot-Savart law, establishing an electromagnetic relation matrix between the space magnetic field and the current of the power line; positioning the power line based on a target optimization algorithm according to the magnetic field value acquired by the magnetic sensor array to obtain the current position of the power line; according to the current position and the initial position of the power line, judging whether the power line deviates; when the power line deviates, correcting the electromagnetic relation matrix according to the current position and the initial position of the power line; and controlling the unmanned aerial vehicle to inhabit on the power line according to the corrected electromagnetic relation matrix. According to the invention, the unmanned aerial vehicle can accurately inhabit on the power line.

Description

Technical Field

[0001] The present application relates to the technical field of wireless charging for unmanned aerial vehicles (UAVs), and particularly to an optimization method for UAV perching on power lines based on a magnetoresistive sensor array and related devices. Background Art

[0002] The application of UAVs in the cruising field has received extensive attention and practice. Whether a UAV can be charged in the wild is an important factor restricting the endurance of the UAV. The existing technical routes mainly adopt the mode of deploying charging bases in advance, which is greatly limited under field conditions. However, power transmission lines with different voltage levels are spread all over the wild, providing a feasible power supply source for charging UAVs.

[0003] However, when a UAV flies near a power line, it often faces a complex electromagnetic environment. This poses higher requirements on the navigation and control system of the UAV, especially during the perching period on the power line. How to ensure the precise positioning of the UAV with respect to the power line has become an urgent problem to be solved. Summary of the Invention

[0004] The purpose of the present application is to provide an optimization method for UAV perching on power lines based on a magnetoresistive sensor array and related devices. By measuring the magnetic field around the power line, the position of the power line is corrected, enabling the UAV to accurately perch on the power line.

[0005] To achieve the above purpose, the present application provides the following solutions:

[0006] In a first aspect, the present application provides an optimization method for UAV perching on power lines based on a magnetoresistive sensor array, including:

[0007] Collecting magnetic field information of a power line at a target position based on a magnetoresistive sensor array on the UAV; the magnetoresistive sensor array is an array composed of a plurality of TMR sensors; the magnetic field information includes spatial magnetic field and magnetic field value;

[0008] Establishing an electromagnetic relationship matrix between the spatial magnetic field and the current of the power line based on the Biot - Savart law;

[0009] Positioning the power line based on the magnetic field value collected by the magnetic sensor array using a target optimization algorithm to obtain the current position of the power line;

[0010] Judging whether the power line has a position offset according to the current position and the initial position of the power line;

[0011] When the power line has an offset, correcting the electromagnetic relationship matrix according to the current position and the initial position of the power line;

[0012] Control the drone to perch on the power line according to the corrected electromagnetic relationship matrix.

[0013] Optionally, based on the Biot-Savart law, establish an electromagnetic relationship matrix between the spatial magnetic field and the current in the power line, specifically including:

[0014] According to the formula Integrate the magnetic induction intensity of each current element on the power line at the spatial measurement point M; the spatial measurement point M is the position of the magnetoresistive sensor array on the drone; in the formula, μ is the vacuum permeability, L1 and L2 are the power line intervals for integration; Idl is the current element; dB is the magnetic induction intensity, r is the distance from the current element to the spatial measurement point M, and α is the included angle between the direction vector from the current element to the spatial measurement point M and the current transmission direction;

[0015] Establish an electromagnetic relationship matrix based on the magnetic induction intensity integration of the current element at the spatial measurement point M and the n-phase current of the power line.

[0016] Optionally, the formula expression of the electromagnetic relationship matrix is:

[0017]

[0018] Among them, μ is the vacuum permeability, r mn represents the perpendicular distance from the m-th measurement point to the n-th phase current.

[0019] Optionally, the formula expression of the corrected electromagnetic relationship matrix is:

[0020]

[0021] Among them, H is the perpendicular distance between the sensor array and the transmission line, h1, h2, and h3 are the actual offset values that appear at the positions of the three-phase transmission lines respectively, L is the horizontal distance between adjacent currents, and the subscripts 1, 2, and 3 correspond to the A, B, and C phases in the three-phase current respectively.

[0022] Optionally, the objective function of the target optimization algorithm is specifically:

[0023]

[0024] Among them, is the error of the magnetic field estimation value, I max is the maximum value of the current, h min and h max are the lower and upper limits of the power line position offset respectively, is the estimated value of the magnetic field.

[0025] Optionally, the calculation formula of the estimated value of the magnetic field is:

[0026]

[0027] In the formula, is the estimated value of the current, is the estimated value of the electromagnetic relationship matrix.

[0028] Optionally, the calculation formula for the estimated value of the electromagnetic relationship matrix is:

[0029]

[0030] In the formula, is the estimated value of the electromagnetic relationship matrix, and are the estimated values of the power line position offsets.

[0031] In a second aspect, the present application provides a UAV perching power line optimization device based on a magnetoresistive sensor array, including:

[0032] A data acquisition module, configured to collect magnetic field information of a power line at a target position based on a magnetoresistive sensor array on the UAV; the magnetoresistive sensor array is an array composed of a plurality of TMR sensors; the magnetic field information includes spatial magnetic field and magnetic field value;

[0033] A matrix construction module, configured to establish an electromagnetic relationship matrix between the spatial magnetic field and the current of the power line based on the Biot-Savart law;

[0034] A power line positioning module, configured to position the power line based on a target optimization algorithm according to the magnetic field value collected by the magnetic sensor array, and obtain the current position of the power line;

[0035] A judgment module, configured to judge whether the power line has a position offset according to the current position and the initial position of the power line;

[0036] A correction module, configured to correct the electromagnetic relationship matrix according to the current position and the initial position of the power line when the power line has an offset;

[0037] A control module, configured to control the UAV to perch on the power line according to the corrected electromagnetic relationship matrix.

[0038] In a third aspect, the present application provides a computer device, including: a memory, a processor, and a computer program stored on the memory and executable on the processor, and the processor executes the computer program to implement the method for optimizing a UAV perching power line based on a magnetoresistive sensor array according to any one of the above.

[0039] Fourthly, the present application provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it implements the method for optimizing the power line for an unmanned aerial vehicle to perch based on a magnetoresistive sensor array described in any one of the above.

[0040] According to the specific embodiments provided by the present application, the following technical effects are disclosed in the present application:

[0041] The present application provides a method for optimizing the power line for an unmanned aerial vehicle to perch based on a magnetoresistive sensor array and related devices. First, the magnetic field information of the power line at the target position is collected by the magnetoresistive sensor array on the unmanned aerial vehicle. The magnetoresistive sensor array is composed of several TMR sensors, which can accurately sense the spatial magnetic field and the magnetic field value, providing key data for subsequent power line positioning. Secondly, based on the Biot-Savart law, an electromagnetic relationship matrix between the spatial magnetic field and the current of the power line is established. By establishing a mathematical model between the magnetic field and the position of the power line, a theoretical basis is provided for the unmanned aerial vehicle to locate the power line. Then, according to the magnetic field values collected by the magnetic sensor array, based on the target optimization algorithm, the power line is located to obtain the current position of the power line. Through the collected magnetic field information and combined with the optimization algorithm, the position of the power line is accurately calculated, providing an accurate target for the perching of the unmanned aerial vehicle. Then, according to the current position and the initial position of the power line, it is judged whether the power line has a position offset. By real-time monitoring the position change of the power line, it is ensured that the unmanned aerial vehicle has an accurate understanding of the state of the power line before perching. When the power line has an offset, according to the current position and the initial position of the power line, the electromagnetic relationship matrix is corrected. By dynamically adjusting the electromagnetic relationship matrix to adapt to the change of the power line position, it is ensured that the unmanned aerial vehicle can accurately perch on the power line. Finally, according to the corrected electromagnetic relationship matrix, the unmanned aerial vehicle is controlled to perch on the power line. The present application realizes the accurate perching of the unmanned aerial vehicle on the power line through the positioning of the power line position. Description of the Drawings

[0042] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required to be used in the embodiments. Obviously, the following described drawings are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained according to these drawings.

[0043] Figure 1 It is a schematic flow chart of a method for optimizing the power line for an unmanned aerial vehicle to perch based on a magnetoresistive sensor array in an embodiment of the present application;

[0044] Figure 2 It is a schematic diagram of the energy harvesting circuit structure provided in an embodiment of the present application;

[0045] Figure 3 Schematic diagram of electromagnetic relationship of a long straight wire provided by an embodiment of the present application;

[0046] Figure 4 Schematic diagram of a three-phase current measurement model considering wire position change provided by an embodiment of the present application;

[0047] Figure 5 Schematic diagram of current measurement results considering random error provided by an embodiment of the present application;

[0048] Figure 6 Schematic diagram of current measurement results of conductor position deviation without optimization provided by an embodiment of the present application;

[0049] Figure 7 Schematic diagram of current measurement results of conductor position deviation after optimization provided by an embodiment of the present application;

[0050] Figure 8 Logic diagram of the target optimization algorithm provided by an embodiment of the present application;

[0051] Figure 9 Schematic diagram of the structure of a computer device provided by an embodiment of the present application. Detailed implementation manners

[0052] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying 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 the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.

[0053] In the prior art, a method for monitoring the operating state of underground power cables based on magnetic field sensing is proposed. This method can achieve non-destructive monitoring and positioning of 11KV cables. However, it lacks the analysis of the influence of magnetic field interference on current measurement errors and the analysis of the arrangement of sensor arrays. A differential array based on six magnetic sensors is proposed to achieve the measurement of three-phase current. This research simply analyzes the influence caused by the deviation of the sensitive angle of the sensor and lacks corresponding countermeasures. The TMR sensor is used to measure the current when a single conductor is tilted, and the influence of the number, spacing, and array shape of the sensors on the measurement error is studied. The measurement object is single, and the influence of complex magnetic field interference is not considered. A method for measuring three-phase current of overhead lines based on a three-TMR sensor array is proposed, which can achieve position monitoring and current measurement when the conductor is displaced. It lacks the analysis of the influence of sensor position deviation, and the iterative method has high requirements for equipment.

[0054] The purpose of this application is to provide a method and related device for optimizing the power line for an unmanned aerial vehicle (UAV) to perch based on a magnetoresistive sensor array. By measuring the magnetic field around the power line using the magnetoresistive sensor array, the position of the power line is corrected, enabling the UAV to accurately perch on the power line.

[0055] To make the above objectives, features, and advantages of this application more apparent and understandable, the following further detailed description of this application will be given in conjunction with the accompanying drawings and specific embodiments.

[0056] In an exemplary embodiment, as Figure 1 shown, a method for optimizing the power line for a UAV to perch based on a magnetoresistive sensor array is provided, including the following steps 101 to 106. Among them:

[0057] Step 101: Based on the magnetoresistive sensor array on the UAV, collect the magnetic field information of the power line at the target position; the magnetoresistive sensor array is an array composed of several TMR sensors; the magnetic field information includes the spatial magnetic field and the magnetic field value;

[0058] Step 102: Based on the Biot-Savart law, establish an electromagnetic relationship matrix between the spatial magnetic field and the current of the power line;

[0059] Step 103: According to the magnetic field values collected by the magnetic sensor array, based on the target optimization algorithm, locate the power line to obtain the current position of the power line;

[0060] Step 104: According to the current position and the initial position of the power line, determine whether the power line has a position offset;

[0061] Step 105: When the power line has an offset, correct the electromagnetic relationship matrix according to the current position and the initial position of the power line;

[0062] Step 106: According to the corrected electromagnetic relationship matrix, control the UAV to perch on the power line.

[0063] In this embodiment, the magnetoresistive sensor array, as a sensor based on the magnetoresistive effect, can accurately sense and measure the intensity and direction of the electromagnetic field. These sensors have advantages such as small volume, fast response, good stability, and low cost, and are therefore widely used in fields such as magnetic field detection and position recognition. In the application scenario of a UAV perching on a power line, the magnetoresistive sensor array can real-time sense the magnetic field changes around the power line, helping the UAV to perform more precise flight control and positioning, and improving the flight stability and safety of the UAV in a complex electromagnetic environment.

[0064] Among them, in an exemplary embodiment, when performing steps 101-106, specifically, it can be as follows:

[0065] An array of sensors is established using TMR (Tunnel Magnetoresistance) sensors. Based on the Biot-Savart law, the precise position of the power line is determined by utilizing the spatial magnetic field distribution generated by a charged power transmission line.

[0066] Specifically, as Figure 3 shown, from the electromagnetic relationship, it can be known that a magnetic field is generated around the transmitted current, and the magnetic field information at the measurement element is the integral of the magnetic field information of all current elements on the power transmission line at this measurement element.

[0067] The Biot-Savart law is as follows: For a certain current element Idl, the magnetic induction intensity dB formed by it at any measurement point M in the surrounding space is directly proportional to both its own numerical value and the sine of the angle α between the direction vector from itself to the space measurement point M and the current transmission direction, and is inversely proportional to the square of the distance r from itself to the space measurement point M. Its equation expression is formula (1):

[0068]

[0069] where μ represents the vacuum magnetic permeability, μ = 4π×10 -7 N / A 2 ; Idl is the current element; dB is the magnetic induction intensity, r is the distance from the current element to the space measurement point M, and α is the angle between the direction vector from the current element to the space measurement point M and the current transmission direction.

[0070] Then, the magnetic induction intensity of all current elements on the power line at the space measurement point M is integrated to obtain formula (2):

[0071]

[0072] In the formula, L1 and L2 are the power line intervals for integration, and the magnitude integration of the magnetic induction intensity at point M gives formula (3):

[0073]

[0074] where, for an infinitely long straight wire, α1 = -180°, α2 = 180°. According to Figure 3 it can be known that the magnetic induction intensity at the space measurement point M can be determined in the two-dimensional plane of XOY, and its expression is (4):

[0075]

[0076] For a semi-infinite long straight wire, its magnitude can be obtained from formula (4) as formula (5):

[0077]

[0078] For a multi-circuit transmission line on the same tower, the magnetic induction intensity at a certain point in space is jointly determined by its n-phase transmission currents, which is the vector sum of the magnetic fields generated by all single-phase transmission currents at this point.

[0079] Non-contact current measurement needs to infer the current information from the magnetic field information measured by a magnetic sensor. For a determined transmission line, its coefficients are fixed, and the current information is only related to the magnetic field information. According to mathematical knowledge, when there are n-phase currents in the transmission line, at least m magnetic sensors need to be set to obtain the n-phase current information. The n-phase currents of the transmission line are respectively set as: I1, I2, …, I n , and a total of n electromagnetic relation equations (6) can be written:

[0080]

[0081] Among them, represents the vector sum of the transmission currents of n-phase conductors on the magnetic induction intensity of the m-th magnetic field sensor point, and r ij represents the perpendicular distance from the i-th measurement point to the j-th phase current. Let: the electromagnetic relation matrix The electromagnetic relation equation set (7) can be obtained:

[0082]

[0083] Then, using the direct substitution solution method in the inverse problem calculation method, solve the electromagnetic relation equation set to obtain the current amplitude and phase parameters of the power line.

[0084] Specifically, solving the current information from the magnetic field information is actually solving the inverse problem of the equation set. There are two ideas for solving the inverse problem: one is to directly substitute into the equation set to solve the equation, and the other is to transform the equation into a minimum value problem and solve the optimal solution of the equation through multiple forward iterations, which is actually transformed into an optimization problem.

[0085] In this application, n magnetic measurement points are set for the n-phase currents, and the influence of actual errors is considered, so the method of solving equations is directly used to solve the current information. The alternating current uses a frequency of 50 Hz. When solving the phase amplitude parameters of the current, multiple solved current values can be taken within a period T = 0.02 s, and the current curve can be obtained by fitting. The accuracy of the current waveform is affected by the sampling frequency. The higher the sampling frequency, the more accurate the obtained current information.

[0086] Specifically, in engineering practice, the current measurement target is often a system composed of multiple lines. In this embodiment, a common three-phase current system is taken as an example for analysis, and a linear magnetic field sensor matrix placed under the line is used. The measurement system model is as Figure 4 shown.

[0087] Among them, Ii (i = 1, 2, 3) is the target current, B ix and B iy are the magnetic field intensities sensed by the horizontal and vertical sensors respectively, h is the vertical distance between the sensor and the current, and L is the horizontal distance between adjacent currents.

[0088] Considering the power line as a long straight line, Figure 4 when the power line is in the initial position, the measurement results of the sensor group can be expressed by Equation (8):

[0089] B = A0I (8).

[0090] Among them, the electromagnetic relationship in the initial position is Equation (9):

[0091]

[0092] Among them, the magnetic field vector current vector

[0093] When the power line undergoes a position offset, the electromagnetic relationship matrix calculated according to the actual power line position offset value is Equation (10):

[0094]

[0095] Among them, H is the vertical distance between the sensor array and the transmission line, h1, h2, and h3 are the actual offset values that occur in the positions of the three-phase transmission lines respectively, L is the horizontal distance between adjacent currents, and the subscripts 1, 2, and 3 correspond to phases A, B, and C in the three-phase current respectively.

[0096] Therefore, for the actual situation, it is necessary to confirm the position of the power line. As Figure 8 shown, first, define the population size, dimension, number of iterations, objective function, and solution space. Input the parameters of the model in this embodiment. Then, initialize the position offset of the power line and solve the model using the optimization algorithm.

[0097] Specifically, the magnetic sensor array on the unmanned aerial vehicle inputs the measurement values into the signal processing device. Solve for the offset position and current of the power line through the target optimization algorithm, and rewrite and construct the electromagnetic relationship matrix, which is expressed as (11):

[0098]

[0099] Among them, is the estimated value of the electromagnetic relationship matrix, and are the estimated values of the power line position offset.

[0100] In the signal processing system, the estimated value of the magnetic field is expressed as Equation (12):

[0101]

[0102] where is the estimated value of the magnetic field, is the estimated value of the current.

[0103] Set the objective function as Equation (13):

[0104]

[0105] where is the error of the magnetic field estimated value, I max is the maximum value of the current, h min and h max are respectively the lower limit and the upper limit of the power line position offset.

[0106] The smaller the error of the magnetic field estimated value, the better the effect of the method on the position correction of the power line. What this embodiment focuses on is the error of the power line position correction amount.

[0107] Finally, according to the corrected electromagnetic relationship matrix, control the drone to perch on the power line.

[0108] Specifically, the schematic diagram of the energy acquisition circuit structure of the drone is as Figure 2 shown. The hybrid energy acquisition circuit includes a protection circuit module, a synchronous rectification module, a DC-DC circuit module, a power management module and a measurement unit module, and obtains energy through power line magnetic field induction to charge the battery in the drone. The battery is connected to the voltage stabilization circuit module of the transmitting end circuit, and after passing through the inverter circuit, the transformer, the rectifier circuit and the voltage stabilization circuit in sequence, it charges the on-board battery.

[0109] In addition, in order to test the measurement accuracy of this method in practical applications, normal random errors with a variance of 1.6×10 -9 T 2 and a mean value of 0 are respectively added to the current signal and the sensor measurement. The amplitude of the three-phase current is 1000 A. Simulate the two-dimensional measurement method, and the result is as Figure 5 shown. The root mean square error of the current is 0.77%.

[0110] When the conductor is displaced, set y1 = 0.02 m, y2 = 0.02 m, y3 = 0.02 m. The current measurement result is as Figure 6 shown. The root mean square error of the current is 10.59%.

[0111] After optimization by the optimization method in this embodiment, compared with the situation without optimization, the root mean square error of the optimized measurement result is reduced to 0.81%, improving the accuracy of current measurement. The results are as Figure 7 . Among them, the measured values are y1 = 0.0104m, y2 = 0.0197m, y3 = 0.0199m, and the root mean square error is 1.40%.

[0112] Based on the same inventive concept, an embodiment of the present application also provides a video tag processing device for implementing an optimization method of an unmanned aerial vehicle perching on a power line based on a magnetoresistive sensor array as described above. The solution provided by this device to solve the problem is similar to the solution described in the above method. Therefore, the specific limitations in one or more embodiments of the following video tag processing device can refer to the limitations on an optimization method of an unmanned aerial vehicle perching on a power line in the above text, and will not be elaborated here.

[0113] In an exemplary embodiment, an optimization device for an unmanned aerial vehicle perching on a power line based on a magnetoresistive sensor array is provided, including:

[0114] A data acquisition module for collecting magnetic field information of a power line at a target position based on a magnetoresistive sensor array on the unmanned aerial vehicle; the magnetoresistive sensor array is an array composed of a plurality of TMR sensors; the magnetic field information includes spatial magnetic field and magnetic field value;

[0115] A matrix construction module for establishing an electromagnetic relationship matrix between the spatial magnetic field and the current of the power line based on the Biot - Savart law;

[0116] A power line positioning module for positioning the power line based on the magnetic field values collected by the magnetic sensor array and a target optimization algorithm to obtain the current position of the power line;

[0117] A judgment module for judging whether the power line has a position offset according to the current position and the initial position of the power line;

[0118] A correction module for correcting the electromagnetic relationship matrix according to the current position and the initial position of the power line when the power line has an offset;

[0119] A control module for controlling the unmanned aerial vehicle to perch on the power line according to the corrected electromagnetic relationship matrix.

[0120] In an exemplary embodiment, a computer device is provided. This computer device can be a server or a terminal, and its internal structure diagram can be as Figure 9As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, abbreviated as I / O), and a communication interface. Among them, the processor, the memory, and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store the power line positions. The input / output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with external terminals through a network connection. When the computer program is executed by the processor, it implements an optimization method for a drone perching on a power line based on a magnetoresistive sensor array.

[0121] Those skilled in the art can understand that Figure 9 the structure shown in is only a block diagram of some structures related to the solution of this application, and does not constitute a limitation on the computer device to which the solution of this application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine some components, or have different component arrangements.

[0122] In an exemplary embodiment, a computer device is further provided, including a memory and a processor. A computer program is stored in the memory, and when the processor executes the computer program, the steps in the above method embodiments are implemented.

[0123] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, and when the computer program is executed by the processor, the steps in the above method embodiments are implemented.

[0124] 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 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 regulations.

[0125] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, database, or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memories. Non-volatile memory can include Read-Only Memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0126] In the embodiments provided in the present application, the databases involved can include at least one of relational databases and non-relational databases. Non-relational databases can include distributed databases based on blockchain, etc., without limitation. In the embodiments provided in the present application, the processors involved can be general-purpose processors, central processors, graphics processors, digital signal processors, programmable logic devices, data processing logics based on quantum computing, etc., without limitation.

[0127] In summary, the present application has the following technical effects:

[0128] Through the method of extracting and identifying power line characteristics and the application of precise positioning technology, the drone can achieve more accurate positioning in the complex magnetic field environment around the power line and stably perch on the target power line.

[0129] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope described in this specification.

[0130] In this article, specific examples are used to elaborate on the principles and implementation manners of the present application. The description of the above embodiments is only used to help understand the method and its core idea of the present application; at the same time, for those of ordinary skill in the art, according to the idea of the present application, there will be changes in the specific implementation manners and application scopes. In summary, the content of this specification should not be construed as a limitation on the present application.

Claims

1. An optimization method for an unmanned aerial vehicle (UAV) to perch on a power line based on a magnetoresistive sensor array, characterized in that, Including: Collecting the magnetic field information of the power line at the target position based on the magnetoresistive sensor array on the unmanned aerial vehicle; The magnetoresistive sensor array is an array composed of a plurality of TMR sensors; The magnetic field information includes the spatial magnetic field and the magnetic field value; Based on the Biot-Savart law, establishing an electromagnetic relationship matrix between the spatial magnetic field and the current of the power line; Positioning the power line based on the magnetic field value collected by the magnetic sensor array and based on the target optimization algorithm to obtain the current position of the power line; Judging whether the power line has a position offset according to the current position and the initial position of the power line; When the power line has an offset, correcting the electromagnetic relationship matrix according to the current position and the initial position of the power line; Controlling the unmanned aerial vehicle to perch on the power line according to the corrected electromagnetic relationship matrix.

2. The optimized method for a drone to perch on a power line based on a magnetoresistive sensor array according to claim 1, characterized in that Based on the Biot-Savart law, establishing an electromagnetic relationship matrix between the spatial magnetic field and the current of the power line, specifically including: According to the formula Integrate the magnetic induction intensity of each current element on the power line at the spatial measurement point M; the spatial measurement point M is the position of the magnetoresistive sensor array on the unmanned aerial vehicle; where μ is the vacuum permeability, L1 and L2 are the power line intervals for integration; Idl is the current element; dB is the magnetic induction intensity, r is the distance from the current element to the spatial measurement point M, and α is the angle between the direction vector from the current element to the spatial measurement point M and the current transmission direction; Establishing an electromagnetic relationship matrix according to the magnetic induction intensity integral of the current element at the spatial measurement point M and the n-phase current of the power line.

3. The optimized method for a drone perching on a power line based on a magnetoresistive sensor array according to claim 2, wherein, The formula expression of the electromagnetic relationship matrix is: where μ is the permeability of vacuum, and r mn represents the perpendicular distance from the m-th measurement point to the n-th phase current.

4. The optimized method for an unmanned aerial vehicle to perch on a power line based on a magnetoresistive sensor array according to claim 3, wherein The formula expression of the corrected electromagnetic relationship matrix is: Where, H is the vertical distance between the sensor array and the transmission line, h1, h2, and h3 are the actual offset values of the positions of the three-phase transmission lines respectively, L is the horizontal distance between adjacent currents, and the subscripts 1, 2, and 3 respectively correspond to the A, B, and C phases in the three-phase current.

5. The method for optimizing the power line for the drone to perch based on the magnetoresistive sensor array according to claim 4, wherein The objective function of the target optimization algorithm is specifically: Among them, is the error of the magnetic field estimation value, I max is the maximum value of the current, h min and h max are respectively the lower limit and the upper limit of the power line position offset, is the estimated value of the magnetic field.

6. The optimized method for a drone to perch on a power line based on a magnetoresistive sensor array according to claim 5, characterized in that, The calculation formula of the estimated value of the magnetic field is: In the formula, is the estimated value of the current, is the estimated value of the electromagnetic relationship matrix.

7. The optimized method for an unmanned aerial vehicle to perch on a power line based on a magnetoresistive sensor array according to claim 6, wherein The calculation formula of the estimated value of the electromagnetic relationship matrix is: In the formula, is the estimated value of the electromagnetic relationship matrix, and are the estimated values of the power line position offset.

8. An optimized device for a drone to perch on a power line based on a magnetoresistive sensor array, characterized in that, Including: A data acquisition module for collecting the magnetic field information of the power line at the target position based on the magnetoresistive sensor array on the unmanned aerial vehicle; The magnetoresistive sensor array is an array composed of a plurality of TMR sensors; the magnetic field information includes the spatial magnetic field and the magnetic field value; A matrix construction module for establishing an electromagnetic relationship matrix between the spatial magnetic field and the current of the power line based on the Biot-Savart law; A power line positioning module for positioning the power line based on the magnetic field value collected by the magnetic sensor array and based on the target optimization algorithm to obtain the current position of the power line; A judgment module for judging whether the power line has a position offset according to the current position and the initial position of the power line; A correction module for correcting the electromagnetic relationship matrix according to the current position and the initial position of the power line when the power line has an offset; A control module for controlling the unmanned aerial vehicle to perch on the power line according to the corrected electromagnetic relationship matrix.

9. A computer device, comprising: A memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor executes the computer program to implement a method for optimizing the perching of an unmanned aerial vehicle on a power line based on a magnetoresistive sensor array according to any one of claims 1-7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements a method for optimizing the perching of an unmanned aerial vehicle on a power line based on a magnetoresistive sensor array according to any one of claims 1-7.

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