Wireless charging method and device of track-hung robot, electronic device, and storage medium

Abstract

The embodiment of the application provides a wireless charging method and device of a hanging rail robot, electronic equipment and a storage medium, and belongs to the technical field of wireless charging of an inspection robot. The method comprises the following steps: collecting a three-dimensional magnetic field signal generated by a charging transmitting end through a multi-axis magnetic field sensor array; calculating the three-dimensional magnetic field signal and a reference magnetic field signal to determine a magnetic field coincidence degree; determining the moving speed of the hanging rail robot and a charging current according to the magnetic field coincidence degree; and controlling the wireless charging of the hanging rail robot according to the moving speed and the charging current. The embodiment of the application can improve the charging efficiency of the hanging rail robot.

Description

Technical Field

[0001] This application relates to the field of wireless charging technology for inspection robots, and in particular to a wireless charging method and device, electronic device and storage medium for a rail-mounted robot. Background Technology

[0002] Rail-mounted inspection robots are widely used in power line inspection, rail transit, and industrial automation, typically employing wireless charging to replenish power at track ends or intermediate stations. The efficiency of wireless charging is highly dependent on the alignment accuracy between the transmitting and receiving coils—when there is lateral or angular misalignment between the two coils, the coupling coefficient decreases, and charging efficiency deteriorates sharply. Therefore, improving the charging efficiency of rail-mounted robots has become a pressing issue. Summary of the Invention

[0003] The main objective of this application is to provide a wireless charging method, device, electronic device, and storage medium for a track-mounted robot, aiming to improve the charging efficiency of the track-mounted robot.

[0004] To achieve the above objectives, a first aspect of this application proposes a wireless charging method for a track-mounted robot, the method comprising: The three-dimensional magnetic field signal generated by the charging transmitter is acquired using a multi-axis magnetic field sensor array. The magnetic field overlap degree is determined by calculating based on the three-dimensional magnetic field signal and the reference magnetic field signal. Based on the magnetic field overlap, the moving speed and charging current of the rail-mounted robot are determined. The robot is wirelessly charged based on its moving speed and the charging current.

[0005] To achieve the above objectives, a second aspect of this application provides a wireless charging device for a track-mounted robot, the device comprising: The signal acquisition module is used to acquire the three-dimensional magnetic field signal generated by the charging transmitter through a multi-axis magnetic field sensor array; The coincidence calculation module is used to calculate and determine the magnetic field coincidence based on the three-dimensional magnetic field signal and the reference magnetic field signal. The data determination module is used to determine the moving speed and charging current of the rail-mounted robot based on the magnetic field overlap. The charging control module is used to wirelessly charge the rail-mounted robot according to the moving speed and the charging current.

[0006] To achieve the above objectives, a third aspect of this application provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the method described in the first aspect.

[0007] To achieve the above objectives, a fourth aspect of the present application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method described in the first aspect.

[0008] This application proposes a wireless charging method, device, electronic equipment, and storage medium for a track-mounted robot. It acquires a three-dimensional magnetic field signal generated by the charging transmitter using a multi-axis magnetic field sensor array. Then, based on this three-dimensional magnetic field signal and a reference magnetic field signal, it calculates and determines the magnetic field overlap, accurately identifying the positional or angular deviation between the two coils. Next, based on the calculated magnetic field overlap, it dynamically determines the robot's movement speed and charging current, achieving adaptive determination of robot position adjustment and charging parameters. Finally, based on the determined movement speed and charging current, it performs precise wireless charging control on the track-mounted robot, guiding it to adjust its position and transmitting high-current energy when the optimal coupling state is reached. This avoids energy loss and heat generation caused by blind charging when not fully aligned, effectively solving the problem of decreased coupling coefficient and drastic deterioration of charging efficiency due to lateral or angular misalignment between the transmitting and receiving coils, significantly improving the charging efficiency of the track-mounted robot. Attached Figure Description

[0009] Figure 1 This is a flowchart of the wireless charging method for a rail-mounted robot provided in an embodiment of this application; Figure 2 This is a flowchart provided in an embodiment of this application; Figure 3 This is a schematic diagram of the operation provided in the embodiments of this application; Figure 4 This is a comparison diagram of the normalized current and magnetic field overlap under different coefficients provided in the embodiments of this application; Figure 5 This is a comparison chart of charging efficiency provided in the embodiments of this application; Figure 6 This is a schematic diagram of the structure of the wireless charging device for the rail-mounted robot provided in the embodiments of this application; Figure 7 This is a schematic diagram of the hardware structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0010] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0011] It should be noted that although functional modules are divided in the device schematic diagram and a logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than the module division in the device or the order in the flowchart. The terms "first," "second," etc., in the specification, claims, and the aforementioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence.

[0012] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0013] Rail-mounted inspection robots are widely used in power line inspection, rail transit, and industrial automation, typically employing wireless charging to replenish power at track ends or intermediate stations. The efficiency of wireless charging is highly dependent on the alignment accuracy between the transmitting and receiving coils—when there is lateral or angular misalignment between the two coils, the coupling coefficient decreases, and charging efficiency deteriorates sharply. Therefore, improving the charging efficiency of rail-mounted robots has become a pressing issue.

[0014] Based on this, embodiments of this application provide a wireless charging method and apparatus, electronic device and storage medium for a rail-mounted robot, aiming to improve the charging efficiency of the rail-mounted robot.

[0015] This application provides a wireless charging method, device, electronic device, and storage medium for a track-mounted robot, which will be described in detail through the following embodiments. First, the wireless charging method for the track-mounted robot in this application embodiment is described.

[0016] The embodiments of this application can acquire and process relevant data based on artificial intelligence technology. Artificial intelligence (AI) refers to the theories, methods, technologies, and application systems that use digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to obtain optimal results.

[0017] Foundational technologies for artificial intelligence generally include sensors, dedicated AI chips, cloud computing, distributed storage, big data processing, operating / interactive systems, and mechatronics. AI software technologies mainly encompass computer vision, robotics, biometrics, speech processing, natural language processing, and machine learning / deep learning.

[0018] The wireless charging method for a rail-mounted robot provided in this application relates to the field of wireless charging technology for inspection robots. This wireless charging method for a rail-mounted robot can be applied to a terminal, a server, or software running on either the terminal or the server. In some embodiments, the terminal can be a smartphone, tablet, laptop, desktop computer, etc.; the server can be configured as an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms; the software can be an application implementing the wireless charging method for the rail-mounted robot, but is not limited to the above forms.

[0019] This application can be used in a wide variety of general-purpose or special-purpose computer system environments or configurations. Examples include: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, and distributed computing environments including any of the above systems or devices. This application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform specific tasks or implement specific abstract data types. This application can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0020] It should be noted that in all specific embodiments of this application, when processing data related to user identity or characteristics, such as user information, user behavior data, user historical data, and user location information, user permission or consent is obtained first. Furthermore, the collection, use, and processing of this data comply with relevant laws, regulations, and standards. In addition, when embodiments of this application require access to sensitive personal information of users, separate permission or consent from the user is obtained through pop-ups or redirection to confirmation pages. Only after obtaining the user's separate permission or consent is the necessary user-related data required for the proper functioning of these embodiments acquired.

[0021] Please see Figure 1 , Figure 1 This is an optional flowchart of the wireless charging method for the rail-mounted robot provided in the embodiments of this application. Figure 1 The method may include, but is not limited to, steps S101 to S104.

[0022] S101: Acquires three-dimensional magnetic field signals generated by the charging transmitter through a multi-axis magnetic field sensor array; S102: Calculate the magnetic field overlap degree based on the three-dimensional magnetic field signal and the reference magnetic field signal; S103: Determine the moving speed and charging current of the rail-mounted robot based on the magnetic field overlap. S104: Wireless charging control of the rail-mounted robot based on the moving speed and charging current.

[0023] This embodiment of the application acquires the three-dimensional magnetic field signal generated by the charging transmitter through a multi-axis magnetic field sensor array. Then, based on this three-dimensional magnetic field signal and a reference magnetic field signal, the magnetic field overlap is calculated to accurately identify the positional or angular deviation between the two coils. Next, based on the calculated magnetic field overlap, the moving speed and charging current of the track-mounted robot are dynamically determined, achieving adaptive determination of robot position adjustment and charging parameters. Finally, based on the determined moving speed and charging current, precise wireless charging control is performed on the track-mounted robot, guiding it to adjust its position and transmitting high-current energy when the optimal coupling state is reached. This avoids energy loss and heat generation caused by blind charging when not fully aligned, effectively solving the problem of decreased coupling coefficient and drastic deterioration of charging efficiency due to lateral or angular misalignment between the transmitting and receiving coils, and significantly improving the charging efficiency of the track-mounted robot.

[0024] For step S101, the multi-axis magnetic field sensor array refers to the magnetic field sensing module installed on the rail-mounted robot body. The multi-axis magnetic field sensor array is composed of magnetic sensors capable of measuring magnetic field components in different dimensions of space, arranged according to a preset structure. For example, the magnetic sensors are triaxial Hall sensors, anisotropic magnetoresistive sensors (AMR), or giant magnetoresistive sensors (GMR), etc., and the preset structure is, for example, a rectangular arrangement, a circular arrangement, or a cross-shaped arrangement.

[0025] A charging transmitter is a wireless energy transmission device deployed at the end of a robot's track, at an intermediate station, or at a specific parking position along an inspection path. It typically includes a transmitting coil and a corresponding inverter drive circuit. In both working and standby states, the charging transmitter radiates an alternating magnetic field for energy transfer into the surrounding space.

[0026] The three-dimensional magnetic field signal refers to the spatial magnetic field vector data captured by the aforementioned sensor array, which includes the magnetic induction intensity components (Bx, By, Bz) of the magnetic field measured by each sensor node in the array in the three orthogonal directions of the X-axis, Y-axis, and Z-axis in space.

[0027] For example, suppose a square transmitting coil, i.e., the charging transmitter, is fixedly installed directly below the intermediate station on the track. At the four vertices of the wireless charging receiving coil of the track-mounted robot, a three-axis Hall sensor is installed. These four sensors together form a multi-axis magnetic field sensor array.

[0028] When the track-mounted robot moves along the track into the charging station and approaches the transmitting coil, the magnetic field emitted by the transmitting coil will cover the robot's sensor array. At this time, sensor 1, located at the front left, measures the three-dimensional magnetic field components at its location as (Bx1, By1, Bz1), sensor 2, located at the front right, measures (Bx2, By2, Bz2)... and so on. The three-dimensional vector data of these four points are combined to form a three-dimensional magnetic field signal.

[0029] In some embodiments, step S102 may include the following steps: S201: Obtain the magnetic field signal of the three-dimensional magnetic field in each dimension to obtain the horizontal axis magnetic field signal, the vertical axis magnetic field signal and the vertical axis magnetic field signal; S202: The magnetic field coincidence degree is calculated based on the corresponding dimensions of the horizontal axis magnetic field signal, vertical axis magnetic field signal, and reference magnetic field signal. The magnetic field coincidence degree is shown in the formula:

[0030] in, O B represents the degree of magnetic field overlap. x B x0 B y By0 B z B z0 These are the magnetic field signals of the corresponding dimensions in the horizontal axis magnetic field signal, the vertical axis magnetic field signal, the vertical axis magnetic field signal, and the reference magnetic field signal, respectively.

[0031] For steps S201 and S202, the acquired horizontal axis magnetic field signal, vertical axis magnetic field signal and vertical axis magnetic field signal are independent magnetic induction intensity components that are projected onto the X-axis, Y-axis and Z-axis of the robot's own coordinate system (or absolute spatial coordinate system) by the three-dimensional spatial magnetic field vector.

[0032] The reference magnetic field signal refers to the reference values ​​of the magnetic field components in each dimension that are pre-calibrated and stored when the wireless charging receiving coil and the charging transmitting end of the track-mounted robot are in an ideal alignment state with no deviation, i.e., the state with the highest physical coupling coefficient.

[0033] This application embodiment first acquires the magnetic field signals of the three-dimensional magnetic field signals in each dimension to obtain the horizontal axis magnetic field signal, the vertical axis magnetic field signal, and the vertical axis magnetic field signal. Then, it calculates the magnetic field overlap degree based on the corresponding dimension magnetic field signals of the horizontal axis magnetic field signal, the vertical axis magnetic field signal, and the reference magnetic field signal representing the ideal alignment state. This allows the position signal to be converted into a magnetic field signal, thereby sensing the position of the track-mounted robot and providing a data basis for subsequent charging. It also allows the robot to be judged to be in the optimal position based on the magnetic field overlap degree.

[0034] In some embodiments, step S102 may further include the following steps: Step S301: Calculate and determine the magnetic field coincidence degree based on the preset weights, the three-dimensional magnetic field signal, and the reference magnetic field signal. The magnetic field coincidence degree is shown in the formula:

[0035] Where O is the magnetic field coincidence degree, w i As preset weights, B i The magnetic field signal corresponding to the three-dimensional magnetic field signal is B. i0 The magnetic field signal is the one corresponding to the reference magnetic field signal.

[0036] For step S301, the preset weight refers to the importance coefficient set in advance to reflect the degree of difference in the impact of magnetic field deviations in different spatial dimensions, such as the X-axis, Y-axis, and Z-axis, on the actual coupling coefficient of wireless charging. Due to the mechanical structure characteristics of the track-mounted robot, the degrees of freedom and fault tolerance in different directions are not the same. For example, when the robot moves on the track, the alignment accuracy along the left and right lateral directions of the X-axis and the forward and backward directions of the Y-axis directly determines the overlapping area of ​​the coils, which has a significant impact on charging efficiency; while the slight deviation in the Z-axis (vertical direction) is often caused by the robot's normal mechanical vibration, suspension system swaying, or track joint bumps, and the small spacing changes in this direction have a relatively small weakening effect on electromagnetic coupling. Therefore, in practical applications, higher preset weights can be assigned to the X-axis and Y-axis, and lower preset weights can be assigned to the Z-axis.

[0037] This application first introduces preset weights that reflect the differences in importance of each dimension, thereby differentiating the actual impact of deviations in each direction in physical space on charging efficiency. Then, relative error calculation with weighted coefficients is performed based on the preset weights, three-dimensional magnetic field signals, and reference magnetic field signals to obtain the magnetic field coincidence. This allows for targeted weakening of interference from certain non-critical directions, such as the unavoidable and relatively minor vertical deviation of the Z-axis during robot vibration and swaying, on the overall evaluation results. This solves the problem of misalignment caused by treating all deviations equally, leading to unreasonable reduction of charging current or interruption of charging when normal mechanical vibration or slight swaying occurs. This significantly improves the anti-interference capability and charging stability of the wireless charging system for rail-mounted robots under complex mechanical operating conditions.

[0038] In some embodiments, step S103 may include the following steps: S401: Compare the magnetic field overlap degree, the preset first overlap degree threshold, and the preset second overlap degree threshold to obtain the comparison result; wherein, the first overlap degree threshold is less than the second overlap degree threshold. S402: If the comparison result indicates that the magnetic field overlap is less than the first overlap threshold, the moving speed is determined to be the first speed; S403: If the comparison result indicates that the magnetic field overlap is greater than the first overlap threshold and less than the second overlap threshold, the moving speed is determined to be the second speed; S404: If the comparison result indicates that the magnetic field overlap is greater than or equal to the second overlap threshold, the moving speed is determined to be the stationary speed.

[0039] For step S401, the first overlap threshold and the second overlap threshold are two determination points used to divide the alignment stage of the rail-mounted robot, and the first overlap threshold is less than the second overlap threshold (for example, the first threshold is 50% and the second threshold is 90%). Specifically, less than the first overlap threshold indicates that the robot is in a "long-distance, large-deviation state"; between the first and second overlap thresholds indicates that the robot is in a "short-distance / small-deviation approximation state"; and greater than or equal to the second overlap threshold indicates that the robot has entered the "ideal high-coupling alignment zone".

[0040] For step S402, the first speed is the robot's normal cruising speed or a faster tracking speed, which is used to quickly shorten the physical distance and improve addressing efficiency when the robot is far from the charging transmitter, i.e., "coarse adjustment".

[0041] For step S403, the second speed is a lower creeping or fine-tuning speed. When the robot approaches the optimal alignment point, reducing to the second speed can reduce the effects of inertia, prevent the robot from overshooting due to insufficient braking, and provide operating space for fine alignment, i.e., "fine-tuning".

[0042] For step S404, the stationary speed is zero. At this time, the drive motor of the robot is braked and locked to ensure that it stops stably in the optimal alignment position.

[0043] This embodiment compares the magnetic field overlap with a preset first overlap threshold and a preset second overlap threshold to obtain a comparison result. If the comparison result indicates that the magnetic field overlap is less than the first overlap threshold, the moving speed is determined as the first speed, ensuring the robot can quickly approach the charging area even with large deviations. Then, if the comparison result indicates that the magnetic field overlap is greater than the first overlap threshold but less than the second overlap threshold, the moving speed is determined as the second speed, allowing the robot to smoothly decelerate and finely adjust as it approaches alignment, effectively eliminating the overshoot risk caused by mechanical inertia. Finally, if the comparison result indicates that the magnetic field overlap is greater than or equal to the second overlap threshold, the moving speed is determined as the stationary speed, ensuring the robot remains stationary when it reaches the optimal magnetic field coupling state. This balances the tracking speed and alignment accuracy, significantly improving the accuracy of the charging docking position of the track-mounted robot, and ultimately improving the overall accuracy and efficiency of wireless charging.

[0044] In addition, this embodiment first compares and judges the above-mentioned multi-level overlap thresholds, and actively switches the first speed to a lower second speed and finally transitions to a stationary speed when the robot gradually approaches the optimal charging position. This effectively suppresses the mechanical vibration and pendulum swing amplitude that are easily generated when the robot accelerates, decelerates or travels at high speed on the track. Then, as the swing amplitude of the robot decreases significantly, it ensures that the multi-axis magnetic field sensor array mounted on the robot can sample in a relatively stable physical space environment. Then, this stable running posture greatly reduces the transient spatial position jump caused by violent shaking, and reduces the magnetic field signal fluctuation noise introduced therefrom. Finally, the magnetic field overlap calculated by the system can be smoother, effectively solving the problem of violent fluctuations in magnetic field overlap assessment and alignment judgment failure caused by excessive robot swing amplitude in the prior art, and further improving charging efficiency.

[0045] Following step S403, the following steps may also be included: S501: Controls the movement of the rail-mounted robot according to the second speed; S502: Obtain the vibration frequency and multiple overlap degrees of the rail-mounted robot during its movement, and obtain the rail-mounted vibration frequency and candidate overlap degrees. S503: The dynamic sampling window is determined based on the vibration frequency of the rail. The dynamic sampling window is shown in the following formula:

[0046] in, T For the dynamic sampling window, k This is a preset proportional coefficient. f vib The vibration frequency of the rail; S504: The magnetic field coincidence is updated by performing a moving average filter on the candidate coincidence based on the dynamic sampling window.

[0047] For step S501, when the comparison result falls between the first threshold and the second threshold, the system controls the rail-mounted robot to continue moving towards the optimal alignment point at a lower second speed, that is, a fine-tuning speed. At this time, the robot is in the fine-tracking stage of "walking and exploring".

[0048] For step S502, the track vibration frequency refers to the frequency of periodic mechanical swaying generated in space by the track-mounted robot moving at the second speed on the track due to gravitational swaying or the influence of track joints. The alternative coincidence degree refers to a series of original magnetic field coincidence degree values ​​continuously calculated during dynamic movement accompanied by mechanical swaying, which include transient position jitter noise.

[0049] For step S503, the dynamic sampling window refers to the time span or number of data frames of the "alternate overlap" data stream captured during filtering. Since the oscillation frequency changes with the operating conditions, this window is not fixed, but dynamically and adaptively adjusted with the vibration frequency.

[0050] The preset scaling factor is usually taken as a positive integer (such as 1, 2, etc.) to ensure that the length of the dynamic sampling window T contains exactly an integer number of complete physical oscillation cycles.

[0051] For step S504, using the calculated dynamic sampling window as the length, slide forward on the candidate overlap data stream and calculate the arithmetic mean of all values ​​within the window. The core principle is that the robot's oscillation is usually a symmetrical oscillation around the central axis. When the window length just covers the complete oscillation cycle, the positive and negative deviations will cancel each other out during the averaging process.

[0052] Updating the magnetic field coincidence means replacing the original magnetic field coincidence with the smoothed mean value after filtering, so as to accurately determine whether the optimal alignment point has been reached, which is the second coincidence threshold.

[0053] This embodiment first controls the movement of the track-mounted robot according to a second speed and obtains the vibration frequency of the robot during movement and a series of candidate coincidences. Next, a dynamic sampling window is calculated based on the track vibration frequency, thereby achieving adaptive alignment between the signal processing window length and the actual physical swing cycle. Then, the candidate coincidences are filtered using a moving average based on the dynamic sampling window containing the complete physical vibration cycle. The neutralization principle, where positive and negative positional deviations generated by the robot's pendulum-like swing can cancel each other out within the same cycle, is then used to filter out periodic false offset data introduced by mechanical swing. Finally, the magnetic field coincidence is updated based on the filtering results, solving the problem of drastic jumps in the transient magnetic field signal caused by the robot's swing, ultimately improving the accuracy of alignment judgment and charging control.

[0054] For example, let's measure the current mechanical oscillation frequency of the rail-mounted robot. f vib The value is 1 Hz (meaning one complete physical oscillation of "left-right-right-right" is completed in 1 second). If the preset scaling factor k=1 is set, then the dynamic sampling window T calculated according to the formula is 1 / 1=1 second.

[0055] Assuming the system sampling rate is 4 times / second, and the actual alignment should be 0.85, due to the robot's periodic shaking within 1 second, the four consecutively collected "alternative overlap" values ​​are 0.85, 0.90, 0.85, and 0.80.

[0056] Applying a moving average filter to the above data, the calculation result is: (0.85 + 0.90 + 0.85 + 0.80) / 4 = 0.85.

[0057] It can be seen that the positive and negative deviations caused by periodic mechanical oscillations are canceled and neutralized within a complete dynamic window, avoiding the erroneous judgment that the optimal alignment point has been reached due to a transient sampling as high as 0.90.

[0058] In some embodiments, step S404 may include the following steps: S601: If the comparison result indicates that the magnetic field overlap is greater than or equal to the second overlap threshold, control the rail-mounted robot to continue moving at the second speed, and collect multiple overlap values ​​of the rail-mounted robot during the movement process to obtain candidate overlap values. S602: If the overlap of each candidate is greater than or equal to the second overlap threshold, the moving speed is determined to be the stationary speed.

[0059] For step S601, when the magnetic field overlap is detected to be greater than or equal to the second overlap threshold for the first time, the braking command is not triggered immediately. Instead, the robot is controlled to maintain a low second speed and continue to move slowly forward. Within this very short movement time or distance, multiple sets of overlap data, i.e. candidate overlap, are collected frequently.

[0060] For step S602, when all the candidate overlap measurements of these multiple consecutive measurements meet the condition of being greater than or equal to the second overlap threshold, it is confirmed that the previous initial achievement was not due to accidental sensor glitches or transient environmental noise, but rather that the robot was indeed in the high-coupling alignment region. At this point, the track-mounted robot stops.

[0061] This application embodiment controls the rail-mounted robot to continue moving at a second speed and collect multiple candidate overlaps when the overlap is initially determined to meet the standard. Then, if each candidate overlap is greater than or equal to the second overlap threshold, the moving speed is determined to be the stationary speed. False high overlap signals are identified and eliminated, which solves the problem of misjudgment and premature braking caused by potential environmental noise or transient interference, resulting in false alignment, and significantly improves charging efficiency.

[0062] In one embodiment, step S103 may further include the following steps: S701: If the comparison result indicates that the magnetic field coincidence degree is greater than the first coincidence degree threshold and less than the second coincidence degree threshold, the charging current is determined based on the magnetic field coincidence degree and the preset current, as shown in the formula:

[0063] in, I charge For the charging current, Imax For the preset current, O The degree of magnetic field overlap. α These are preset nonlinear coefficients.

[0064] For step S701, the maximum rated charging current that can be output is preset when the current is in a perfectly aligned state, that is, when the magnetic field overlap is 1.

[0065] max(O, 0 ) is a function, and its value of 0 corresponds to the degree of coincidence with the actual magnetic field. O The maximum value in the exponent is used to ensure that the base number involved in the exponentiation operation is always non-negative, preventing calculation out-of-bounds errors or reports.

[0066] Nonlinear coefficients It is a pre-set adjustment parameter. Due to the coil offset, the decrease in coupling coefficient and the increase in heat generation are not a simple linear relationship, so a nonlinear coefficient is introduced.

[0067] This application's embodiments first adaptively adjust the charging current when the magnetic field overlap is between a first overlap threshold and a second overlap threshold. Specifically, a small current is output when the magnetic field overlap deviates significantly, and the current is increased as the robot approaches the optimal alignment point. This avoids the serious risks of magnetic leakage and coil overheating caused by directly charging at full power when the robot has a large initial deviation or poor coupling environment, or the problem of excessively long charging time due to having to wait for the robot to come to a complete stop, thus improving charging efficiency.

[0068] like Figure 2 As shown in the figure, this embodiment provides a wireless charging alignment system for a rail-mounted inspection robot, including a transmitting coil disposed at the charging station end and a receiving coil disposed at the bottom of the robot body. The transmitting coil and the receiving coil are planar helical coils of the same specification, and their resonant frequencies are matched.

[0069] The robot is equipped with a three-axis Hall sensor array (such as MLX90393 or similar devices) on its bottom to acquire the three-dimensional magnetic field components Bx, By, and Bz generated by the transmitting coil in real time. The reference magnetic field components Bx0, By0, and Bz0 at the ideal alignment position are pre-recorded during system calibration.

[0070] The controller (MCU, such as the STM32F4 series) performs overlap calculation, threshold comparison, movement control, current regulation, and filtering functions. The controller is electrically connected to the robot's walking motor driver and wireless charging power regulation module.

[0071] When the robot needs to be charged, the controller executes the following process: (1) The robot moves to the vicinity of the charging station and the three-axis Hall sensor begins to collect magnetic field signals.

[0072] (2) The controller calculates the real-time overlap O. If O < 30%, the robot moves towards the charging station at a higher speed (coarse adjustment stage).

[0073] (3) When O≥30%, the robot decelerates and performs fine-tuning movement. At the same time, the charging current starts to increase in gradient according to Icharge=Imax×O^α (example value Imax=1.2C, α=2.0), with an initial current of about 0.09C (when O=0.3).

[0074] (4) During the fine-tuning process, the sliding average filter is enabled, and the window length T is dynamically set according to the real-time vibration frequency.

[0075] (5) When O≥90%, the robot stops moving and locks its position. At this time, the charging current has reached about 0.81C (0.9²×Imax when O=0.9), which is close to the maximum value. The system remains in the locked charging state until charging is complete.

[0076] like Figure 2 As shown, in another embodiment, an additional accelerometer (such as an MPU6050) is mounted on the robot chassis to monitor the track's vibration frequency in real time. .

[0077] The window length T of the moving average filter is dynamically adjusted based on the measured vibration frequency of the rail. For typical 5Hz vibration, T≈0.2~0.3 seconds, k=1.0~1.5; for 15Hz vibration, T≈0.07~0.1 seconds. The window length always ensures that it covers at least one complete vibration cycle, thereby effectively suppressing vibration noise.

[0078] Meanwhile, the tolerance range of the overlap threshold T2 is dynamically adjusted according to the vibration frequency of the rail: when the vibration amplitude is large, the equivalent judgment condition of T2 is appropriately relaxed (such as changing the single judgment to a logical AND of N consecutive judgments) to avoid incorrect locking / unlocking switching due to instantaneous vibration spikes.

[0079] Additionally, it should be noted that: The calculation of magnetic field coincidence is not limited to triaxial Hall sensors. For low-cost solutions equipped only with biaxial Hall sensors, the coincidence formula can be reduced to a two-dimensional form: For solutions equipped with higher-dimensional sensor arrays (such as 5-axis or 9-axis), the overlap formula can be extended to... in N This represents the number of sensor channels.

[0080] In addition, different weighting coefficients can be assigned to each magnetic field component. To reflect the different sensitivities to alignment accuracy in different directions: For example, in a scenario involving a rail-mounted robot moving on a horizontal plane, a lower weight can be assigned to the vertical direction and a higher weight to the horizontal direction.

[0081] It should be noted that this application can be widely applied to various rail-mounted inspection robot systems, and is particularly suitable for scenarios such as power inspection (substations, distribution rooms), rail transit (tunnels, platforms), and industrial automation (factory workshops, warehousing and logistics). Through the technical solution of this invention, the alignment reliability and charging efficiency of the wireless charging system under vibration conditions can be significantly improved, reducing the manual intervention and maintenance costs caused by alignment failures, and it has good market application prospects.

[0082] Please see Figure 3 In one embodiment, firstly, step S1 is initiated and proceeds to real-time acquisition of the three-dimensional magnetic field signal generated by the charging transmitter using a three-axis Hall sensor array mounted on the rail-mounted robot. Next, step S2 is initiated to calculate the magnetic field overlap at the current position. Subsequently, a first-level judgment (coarse adjustment stage judgment) is performed: determining whether the currently calculated magnetic field overlap is less than a preset first overlap threshold. If the judgment result is "yes," it indicates that the robot is currently far from the charging station or has a large lateral deviation. At this point, the "coarse adjustment stage" is initiated, controlling the robot to move at a relatively fast first speed to quickly shorten the physical distance to the charging station, and continuously returning to step S1 along the loop arrow for cyclical signal acquisition and judgment. If the first-level judgment result is "No", it indicates that the robot has approached the charging area and immediately enters the fine-tuning stage. In this stage, the robot's movement speed is drastically reduced to a second speed, which is lower than the first speed. During the fine-tuning movement, to combat the robot's mechanical shaking noise, a dynamic sampling window is activated to perform a moving average filter on the continuously collected overlap data to obtain a smooth and accurate true overlap value. Then, based on the filtered overlap data, the second-level judgment (locking stage judgment) is entered: determining whether the current magnetic field overlap is greater than or equal to a preset second overlap threshold. If the judgment result is "No", it indicates that the optimal high-coupling alignment point has not yet been reached, and the robot will continue fine-tuning movement as indicated by the dashed arrow, cyclically performing filtering and judgment. If the second-level judgment result is "Yes", it indicates that the robot has accurately reached the optimal charging position. At this time, a control command is issued: stop movement and lock the physical position; simultaneously, the gradient charging mode is immediately activated, dynamically outputting the charging current. Because the magnetic field overlap is extremely high at this point, energy will be safely and efficiently transferred at a high power close to the maximum rated current until final charging is complete.

[0083] Please see Figure 4The horizontal axis of the graph represents the calculated magnetic field overlap, and the vertical axis represents the normalized charging current, which is the ratio of the actual charging current to the preset maximum current, ranging from 0 to 1.0. Two key state switching points are marked by vertical dashed lines: the first overlap threshold T1 (30% in the example) and the second overlap threshold T2 (90% in the example). This graph illustrates the current regulation characteristics under different nonlinear coefficients α. As can be seen from the graph, different α values ​​can be configured to adapt to different heat control requirements: when α=1.0 (as shown by the solid line in the example), the charging current is linearly proportional to the magnetic field overlap. The current increases smoothly with the overlap, reaching only 30% of the maximum value near T1 (30%) and 90% near T2 (90%). When α>1 (as shown by α=1.5 and α=2.0 in the figures), the curve exhibits a concave exponential growth pattern. Based on the control strategy description in the upper right corner of the figure, the current control mechanism of this application can achieve the following technical effects: In the initial stage of the fine-tuning alignment of the rail-mounted robot (i.e., when the magnetic field overlap has just crossed the T1 threshold and the initial position deviation is large), regardless of the configuration used, the normalized charging current output is at a low level. The characteristic of extremely small current when the initial deviation is large cuts off the leakage inductance loss generated by large current in low coupling state from the physical source, greatly reducing the risk of coil overheating; as the rail-mounted robot continues to move, the alignment accuracy gradually improves, the slope of the curve rises accordingly, and the charging current rises smoothly and rapidly; when the overlap reaches a high-precision alignment state, the current is very close to the maximum rated value (1.0).

[0084] Please see Figure 5 The horizontal axis represents the magnetic field overlap range between the receiving coil of the rail-mounted robot and the transmitting coil of the charging station, and the vertical axis represents the actual energy transfer charging efficiency (percentage). The white bars in the figure represent the traditional "alignment equals full power" fixed current charging scheme, while the gray bars represent the gradient control charging scheme based on dynamic calculation of magnetic field overlap adopted in the embodiments of this application.

[0085] When the magnetic field overlap is in the low to medium range (e.g., 30%-50% and 50%-70%), traditional solutions, lacking precise perception of spatial coupling, blindly output a fixed large current, resulting in a large amount of electrical energy being converted into leakage inductance heat, causing a sharp drop in charging efficiency to only 35% and 55%. In contrast, this application suppresses reactive power loss and heat generation when not fully aligned, allowing the charging efficiency in this range to remain at a relatively high level of 65% and 82%.

[0086] Furthermore, even in the ideal alignment range (90%-100%) with extremely high overlap, this application can still further increase the charging efficiency from 90% to 98% through precise locking by magnetic field sensing.

[0087] Under actual operating conditions covering various alignment deviations, the average charging efficiency of conventional solutions is only about 62%, while the average charging efficiency of the embodiments of this application jumps to about 89%.

[0088] Please see Figure 6 This application also provides a wireless charging device for a rail-mounted robot, which can realize the above-mentioned wireless charging method for the rail-mounted robot. The device includes: Signal acquisition module 601 is used to acquire the three-dimensional magnetic field signal generated by the charging transmitter through a multi-axis magnetic field sensor array; The coincidence calculation module 602 is used to calculate and determine the magnetic field coincidence based on the three-dimensional magnetic field signal and the reference magnetic field signal. The data determination module 603 is used to determine the moving speed and charging current of the rail-mounted robot based on the magnetic field overlap. The charging control module 604 is used to wirelessly control the charging of the rail-mounted robot based on the moving speed and charging current.

[0089] The specific implementation of the wireless charging device for the rail-mounted robot is basically the same as the specific implementation of the wireless charging method for the rail-mounted robot described above, and will not be repeated here.

[0090] This application also provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the aforementioned wireless charging method for the track-mounted robot. This electronic device can be any smart terminal, including tablet computers, in-vehicle computers, etc.

[0091] Please see Figure 7 , Figure 7 The hardware structure of an electronic device according to another embodiment is illustrated. The electronic device includes: The processor 701 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this application. The memory 702 can be implemented as a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 702 can store the operating system and other applications. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 702 and is called and executed by the processor 701 to execute the wireless charging method for the rail-mounted robot according to the embodiments of this application. The input / output interface 703 is used to implement information input and output; The communication interface 704 is used to enable communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, Ethernet cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.). Bus 705 transmits information between various components of the device (e.g., processor 701, memory 702, input / output interface 703, and communication interface 704); The processor 701, memory 702, input / output interface 703, and communication interface 704 are connected to each other within the device via bus 705.

[0092] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the aforementioned wireless charging method for a track-mounted robot.

[0093] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0094] The wireless charging method, wireless charging device, electronic device, and storage medium for a track-mounted robot provided in this application embodiment acquire three-dimensional magnetic field signals generated by the charging transmitter through a multi-axis magnetic field sensor array. Then, based on the three-dimensional magnetic field signal and a reference magnetic field signal, the magnetic field overlap is calculated to accurately identify the positional or angular deviation between the two coils. Next, based on the calculated magnetic field overlap, the moving speed and charging current of the track-mounted robot are dynamically determined, achieving adaptive determination of robot position adjustment and charging parameters. Finally, based on the determined moving speed and charging current, precise wireless charging control is performed on the track-mounted robot, guiding it to adjust its position and transmitting high-current energy when the optimal coupling state is reached. This avoids energy loss and heat generation caused by blind charging when not fully aligned, effectively solving the problem of decreased coupling coefficient and drastic deterioration of charging efficiency due to lateral or angular misalignment between the transmitting and receiving coils, and significantly improving the charging efficiency of the track-mounted robot.

[0095] The embodiments described in this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided by the embodiments of this application. As those skilled in the art will know, with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of this application are also applicable to similar technical problems.

[0096] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of this application, and may include more or fewer steps than shown, or combine certain steps, or different steps.

[0097] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0098] Those skilled in the art will understand that all or some of the steps in the methods disclosed above, as well as the functional modules / units in the systems and devices, can be implemented as software, firmware, hardware, or suitable combinations thereof.

[0099] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0100] It should be understood that in this application, "at least one (item)" means one or more, and "more than" means two or more. "And / or" is used to describe the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent three cases: only A exists, only B exists, and both A and B exist simultaneously, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one (item) of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one (item) of a, b, or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0101] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of the units described above is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0102] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0103] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0104] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes multiple instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing programs, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0105] The preferred embodiments of the present application have been described above with reference to the accompanying drawings, but this does not limit the scope of the claims of the present application. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and substance of the embodiments of the present application shall be within the scope of the claims of the present application.

Claims

1. A wireless charging method for a track-mounted robot, characterized in that, The method includes: The three-dimensional magnetic field signal generated by the charging transmitter is acquired using a multi-axis magnetic field sensor array. The magnetic field overlap degree is determined by calculating based on the three-dimensional magnetic field signal and the reference magnetic field signal. Based on the magnetic field overlap, the moving speed and charging current of the rail-mounted robot are determined. The robot is wirelessly charged based on its moving speed and the charging current.

2. The method according to claim 1, characterized in that, The step of determining the moving speed and charging current of the rail-mounted robot based on the magnetic field overlap includes: The comparison results are obtained by comparing the magnetic field overlap degree, a preset first overlap degree threshold, and a preset second overlap degree threshold; wherein the first overlap degree threshold is less than the second overlap degree threshold. If the comparison result indicates that the magnetic field overlap is less than the first overlap threshold, the moving speed is determined to be the first speed; If the comparison result indicates that the magnetic field overlap is greater than the first overlap threshold and less than the second overlap threshold, the moving speed is determined to be the second speed; If the comparison result indicates that the magnetic field overlap is greater than or equal to the second overlap threshold, the moving speed is determined to be a stationary speed.

3. The method according to claim 2, characterized in that, The step of determining the moving speed and charging current of the rail-mounted robot based on the magnetic field overlap also includes: If the comparison result indicates that the magnetic field overlap is greater than the first overlap threshold and less than the second overlap threshold, the charging current is determined based on the magnetic field overlap and a preset current, as shown in the following formula: in, I charge For the charging current, I max For the preset current, O The degree of magnetic field overlap. α These are preset nonlinear coefficients.

4. The method according to claim 2, characterized in that, After determining the moving speed as the second speed if the comparison result indicates that the magnetic field overlap is greater than the first overlap threshold and less than the second overlap threshold, the method further includes: The rail-mounted robot is controlled to move according to the second speed; The vibration frequency and multiple overlap degrees of the rail-mounted robot during movement are obtained to obtain the rail-mounted vibration frequency and candidate overlap degrees. The dynamic sampling window is determined based on the vibration frequency of the rail, as shown in the following formula: in, T For the dynamic sampling window, k This is a preset proportional coefficient. f vib The vibration frequency of the rail; The magnetic field overlap is updated by performing a moving average filter on the candidate overlap based on the dynamic sampling window.

5. The method according to claim 2, characterized in that, If the comparison result indicates that the magnetic field overlap is greater than or equal to the second overlap threshold, determining the moving speed as a stationary speed includes: If the comparison result indicates that the magnetic field overlap is greater than or equal to the second overlap threshold, the rail-mounted robot is controlled to continue moving at the second speed, and multiple overlaps of the rail-mounted robot during the movement process are collected to obtain candidate overlaps. If all of the candidate overlap degrees are greater than or equal to the second overlap degree threshold, the moving speed is determined to be the stationary speed.

6. The method according to claim 1, characterized in that, The calculation based on the three-dimensional magnetic field signal and the reference magnetic field signal to determine the magnetic field overlap includes: The magnetic field signal of the three-dimensional magnetic field signal in each dimension is obtained to obtain the horizontal axis magnetic field signal, the vertical axis magnetic field signal and the vertical axis magnetic field signal; The magnetic field overlap degree is calculated based on the corresponding dimensions of the horizontal axis magnetic field signal, the vertical axis magnetic field signal, the vertical axis magnetic field signal, and the reference magnetic field signal. The magnetic field overlap degree is shown in the following formula: in, O B represents the degree of magnetic field overlap. x B x0 B y B y0 B z B z0 These are the magnetic field signals of the corresponding dimensions in the horizontal axis magnetic field signal, the vertical axis magnetic field signal, the vertical axis magnetic field signal, and the reference magnetic field signal, respectively.

7. The method according to claim 1, characterized in that, The calculation based on the three-dimensional magnetic field signal and the reference magnetic field signal to determine the magnetic field overlap includes: The magnetic field overlap degree is determined by calculation based on preset weights, the three-dimensional magnetic field signal, and the reference magnetic field signal, as shown in the following formula: Where O is the magnetic field coincidence degree, w i As preset weights, B i The magnetic field signal corresponding to the three-dimensional magnetic field signal is B. i0 The magnetic field signal is the one corresponding to the reference magnetic field signal.

8. A wireless charging device for a track-mounted robot, characterized in that, The device includes: The signal acquisition module is used to acquire the three-dimensional magnetic field signal generated by the charging transmitter through a multi-axis magnetic field sensor array; The coincidence calculation module is used to calculate and determine the magnetic field coincidence based on the three-dimensional magnetic field signal and the reference magnetic field signal. The data determination module is used to determine the moving speed and charging current of the rail-mounted robot based on the magnetic field overlap. The charging control module is used to wirelessly charge the rail-mounted robot according to the moving speed and the charging current.

9. An electronic device, characterized in that, The electronic device includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the wireless charging method for the rail-mounted robot according to any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the wireless charging method for the rail-mounted robot according to any one of claims 1 to 7.

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