Intelligent curtain charging position compensation method and curtain system

Abstract

The present invention discloses a smart curtain charging position compensation method and a curtain system, wherein the smart curtain charging position compensation method includes: obtaining environmental parameters and operating status data of the curtain system, generating charging environment evaluation information; predicting the motion trajectory of the curtain driving component according to the charging environment evaluation information and the real-time position data of the curtain driving component, and estimating the actual stop position of the electromagnetically attracted component; calculating the position offset amount that needs to be adjusted for the charging magnetic component based on the actual stop position of the electromagnetically attracted component and the current state of the charging component; and according to the position offset amount, controlling the charging magnetic component to adjust its position through an electromagnetic driving mechanism, so that the charging magnetic component docks with the electromagnetically attracted component. The technical solution of the present invention can dynamically adjust the position of the charging component, realize the precise docking between the curtain driving component and the charging component, effectively overcome the position deviation caused by multi-source interference factors, and improve the charging efficiency and system stability.

Description

Technical Field

[0001] The present invention relates to the field of intelligent control technology, and in particular to an intelligent curtain charging position compensation method and a curtain system. Background Art

[0002] The smart curtain system is an important part of modern home automation. It uses electric drive components to automatically open and close the curtains, providing users with a convenient operating experience. The system is mainly composed of curtain drive components and charging components. The curtain drive component is responsible for controlling the movement of the curtains, while the charging component provides a continuous power supply to the drive component. This design not only simplifies the user's daily operation, but also automatically adjusts the curtain position according to preset programs or environmental conditions, thereby optimizing indoor lighting and temperature and improving living comfort.

[0003] However, in actual applications, the curtain system faces the challenge of precise docking for charging. Since the curtain drive component is affected by many factors during operation, such as the force component during movement, moving inertia, and structural wear caused by long-term use, the docking accuracy between the drive component and the charging component is difficult to guarantee. Although simply increasing the docking area can improve the fault tolerance rate, it will also reduce the charging efficiency. Therefore, developing a technical solution that can dynamically adjust and ensure the precise docking of the drive component and the charging component is of great significance to improving the reliability and durability of the smart curtain system. Summary of the invention

[0004] The main purpose of the present invention is to solve the technical problem of low charging docking accuracy of existing smart curtains.

[0005] A first aspect of the present invention provides a smart curtain charging position compensation method, which is applied to a curtain system, wherein the curtain system includes a charging component and a curtain drive component arranged on a curtain rail, wherein the charging component includes a movable charging magnetic element, and the curtain drive component includes an electromagnetically attracted element, wherein the charging magnetic element and the electromagnetically attracted element can be attracted by magnetic force and form an electrical connection, and the curtain drive component can move relative to the curtain rail to move the electromagnetically attracted element to an ideal charging position connected to the charging magnetic element;

[0006] The smart curtain charging position compensation method comprises:

[0007] Acquiring environmental parameters and operating status data of the curtain system, evaluating the charging environment according to the environmental parameters and operating status data, and generating charging environment evaluation information;

[0008] Predicting the motion trajectory of the curtain driving component according to the charging environment assessment information and the real-time position data of the curtain driving component, and estimating the actual stop position of the electromagnetically attracted component based on the predicted motion trajectory;

[0009] Calculating the position offset of the charging magnetic element that needs to be adjusted based on the actual stop position of the magnetic element and the current state of the charging assembly;

[0010] According to the position offset, the charging magnetic attraction component is controlled by an electromagnetic driving mechanism to adjust the position so that the charging magnetic attraction component is docked with the receiving electromagnetic attraction component.

[0011] Optionally, the acquiring environmental parameters and operating status data of the curtain system, evaluating the charging environment according to the environmental parameters and operating status data, and generating charging environment evaluation information includes:

[0012] Acquire environmental parameters and operating status data of the curtain system, wherein the environmental parameters include ambient temperature, ambient humidity and ambient magnetic field strength, and the operating status data includes deformation data of the curtain rail and motion parameters of the curtain drive assembly;

[0013] Calculating a material expansion coefficient according to the ambient temperature and ambient humidity, and calculating an actual deformation amount of the curtain rail based on the material expansion coefficient and the deformation data of the curtain rail;

[0014] Calculating magnetic field interference intensity based on the environmental magnetic field intensity and the motion parameters of the curtain drive assembly;

[0015] According to the actual deformation amount of the curtain rail and the magnetic field interference intensity, charging environment assessment information including rail deformation compensation value and magnetic field intensity adjustment parameter is generated.

[0016] Optionally, calculating the magnetic field interference intensity based on the ambient magnetic field intensity and the motion parameters of the curtain drive assembly includes:

[0017] Acquiring real-time magnetic field distribution data around the charging magnetic attraction component;

[0018] Calculating the instantaneous relative position and speed of the electromagnetically attracted component relative to the charging magnetic component according to the motion parameters of the curtain driving component;

[0019] Calculating the magnetic field gradient and rate of change based on the real-time magnetic field distribution data and the instantaneous relative position and velocity;

[0020] Performing fast Fourier transform on the magnetic field gradient and change rate to obtain magnetic field spectrum characteristics;

[0021] According to a preset magnetic field interference pattern library, pattern matching is performed on the magnetic field spectrum characteristics to identify the current magnetic field interference type;

[0022] Based on the magnetic field interference type and the environmental magnetic field strength, the magnetic field interference strength is determined by searching a preset corresponding relationship table.

[0023] Optionally, predicting the motion trajectory of the curtain drive component according to the charging environment assessment information and the real-time position data of the curtain drive component, and estimating the actual stop position of the electromagnetically attracted component based on the predicted motion trajectory, includes:

[0024] Acquire historical motion data and current motion state parameters of the curtain drive component;

[0025] According to the guide rail deformation compensation value in the charging environment assessment information, the historical motion data is corrected to obtain corrected historical motion data;

[0026] Based on the corrected historical motion data and the current motion state parameters, a preset trajectory prediction algorithm is used to calculate a predicted motion trajectory of the curtain drive assembly;

[0027] Calculating the magnetic force influence factor of each point on the predicted motion trajectory according to the magnetic field strength adjustment parameter in the charging environment assessment information;

[0028] Applying the magnetic influence factor to the predicted motion trajectory to obtain a corrected predicted motion trajectory;

[0029] Based on the corrected predicted motion trajectory and the preset stop condition, the actual stop position of the electromagnetically attracted component is calculated.

[0030] Optionally, the calculating the predicted motion trajectory of the curtain driving component based on the corrected historical motion data and the current motion state parameter using a preset trajectory prediction algorithm includes:

[0031] Performing time series analysis on the corrected historical motion data to extract motion features of the curtain drive component;

[0032] Calculating the instantaneous acceleration of the curtain drive assembly according to the motion characteristics and the current motion state parameters;

[0033] Based on the instantaneous acceleration and the corrected historical motion data, a preliminary predicted trajectory is generated using a preset trajectory prediction algorithm;

[0034] Segmenting the preliminary predicted trajectory to obtain a plurality of trajectory segments;

[0035] For each trajectory segment, calculate its curvature and velocity change rate;

[0036] According to the curvature and the speed change rate, each trajectory segment is smoothed to obtain a smoothed trajectory segment;

[0037] All smoothed trajectory segments are concatenated to obtain the complete predicted motion trajectory.

[0038] Optionally, the calculating the position offset amount of the charging magnetic element that needs to be adjusted based on the actual stop position of the magnetic element and the current state of the charging component includes:

[0039] Obtaining the current position coordinates of the charging magnetic attraction component and the actual stop position coordinates of the receiving magnetic attraction component;

[0040] Calculating the initial position difference between the current position coordinates of the charging magnetic attraction component and the actual stop position coordinates of the receiving magnetic attraction component;

[0041] Determining a movable range of the charging magnetic element according to a current state of the charging component;

[0042] Calculating an optimal position adjustment vector based on the initial position difference and the movable range;

[0043] Comparing the optimal position adjustment vector with a preset safety threshold to obtain a corrected position adjustment vector;

[0044] The position offset of the charging magnetic component that needs to be adjusted is calculated based on the corrected position adjustment vector.

[0045] Optionally, controlling the charging magnetic element to adjust its position by an electromagnetic driving mechanism according to the position offset so that the charging magnetic element docks with the receiving electromagnetic element includes:

[0046] Converting the position offset into a driving parameter of an electromagnetic driving mechanism;

[0047] Calculating the excitation current value of each drive unit of the electromagnetic drive mechanism according to the drive parameters;

[0048] According to a preset adjustment sequence, a corresponding excitation current is applied to each driving unit in turn to drive the charging magnetic attraction member to move;

[0049] During the position adjustment process, the position change of the charging magnetic attraction component is monitored in real time;

[0050] According to the position change, dynamically adjust the excitation current value of the remaining drive units to ensure that the charging magnetic attraction member moves according to the position offset;

[0051] When it is detected that the charging magnetic attraction component reaches the target position, the position of the charging magnetic attraction component is finely adjusted until it docks with the receiving magnetic attraction component;

[0052] Verify the docking status of the charging magnetic attraction component and the receiving magnetic attraction component to ensure that the electrical connection is established.

[0053] A second aspect of the present invention provides a curtain system, wherein the curtain system adopts the smart curtain charging position compensation method of the above embodiment.

[0054] The smart curtain charging position compensation method proposed in this application provides an innovative solution to the problem of low charging docking accuracy of smart curtains in the prior art. This method achieves accurate docking between the drive component and the charging component by comprehensively considering environmental factors and system operating status, and effectively overcomes the position deviation caused by multi-source interference factors.

[0055] First, the method obtains the environmental parameters and operating status data of the curtain system and evaluates the charging environment. This step fully considers various factors that affect charging docking, such as environmental parameters such as temperature, humidity, and magnetic field strength, as well as the deformation of the curtain rail and the motion parameters of the drive component. Through the comprehensive analysis of these data, the system can fully understand the current charging environment and provide accurate basic information for subsequent position compensation. Secondly, based on the charging environment evaluation information and the real-time position data of the drive component, the method predicts the motion trajectory of the drive component and estimates the actual stop position of the electromagnetically attracted component. This step uses a prediction algorithm to consider the impact of environmental factors on the motion trajectory, such as rail deformation and magnetic field interference. By accurately predicting the actual stop position of the electromagnetically attracted component, the system can plan the position adjustment strategy of the charging magnetic component in advance, greatly improving the accuracy of docking. Third, the method calculates the position offset that needs to be adjusted for the charging magnetic component based on the predicted stop position of the electromagnetically attracted component and the current state of the charging component. This step makes full use of the information obtained in the first two steps and determines the optimal adjustment amount of the charging magnetic component through precise calculation. This precise calculation takes into account a variety of factors, such as magnetic field strength, guide rail deformation, etc., to ensure the accuracy of the adjustment. Finally, based on the calculated position offset, the electromagnetic drive mechanism controls the charging magnetic suction component to adjust its position to achieve precise docking with the electromagnetic suction component. This step converts the previous analysis and calculation into actual physical actions, and through precise control, ensures that the charging component can accurately move to the optimal docking position.

[0056] The core of this method is to achieve adaptive and precise docking between the charging component and the driving component through real-time environmental perception, precise trajectory prediction and dynamic position adjustment. It not only solves the existing docking accuracy problems caused by environmental factors and structural wear, but also avoids problems such as reduced efficiency caused by simply increasing the docking area through intelligent algorithms and precise control. Through this comprehensive and precise compensation method, this application effectively improves the charging efficiency and stability of the smart curtain system, providing users with a more reliable and lasting smart curtain experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the structures shown in these drawings without paying creative work.

[0058] Figure 1 This is a flow chart of a method for compensating a charging position of a smart curtain in an embodiment of the present invention;

[0059] Figure 2 It is a structural schematic diagram of an embodiment of a curtain system of the present invention;

[0060] Figure 3 It is a structural schematic diagram of another embodiment of the curtain system of the present invention;

[0061] Figure 4 It is a structural schematic diagram of another embodiment of the curtain system of the present invention;

[0062] Figure 5 for Figure 2 A structural diagram from another perspective;

[0063] Figure 6 for Figure 5 A partial enlarged view of the middle A;

[0064] Figure 7 for Figure 5 A cross-sectional diagram with part of the structure omitted.

[0065] Description of Figure Numbers:

[0066] 1. Curtain rail; 2. Charging assembly; 21. Charging magnetic element; 211. Outer cylinder; 212. Inner cylinder; 213. Buffer limit spring; 214. Ring cover; 215. Middle cylinder; 22. Charging cable; 23. Main shell; 3. Curtain drive assembly; 31. Electromagnetic element; 4. Inductor.

[0067] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0068] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0069] It should be noted that if the embodiments of the present invention involve directional indications (such as up, down, left, right, front, back...), the directional indications are only used to explain the relative position relationship, movement status, etc. between the components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication will also change accordingly.

[0070] In addition, the descriptions of "first", "second", etc. in the present invention are only used for descriptive purposes and cannot be understood as indicating or implying their relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In addition, "and / or" in the full text includes three solutions. Taking A and / or B as an example, it includes technical solution A, technical solution B, and technical solution that satisfies both A and B. In addition, the technical solutions between the various embodiments can be combined with each other, which must be based on the ability of ordinary technicians in the field to implement. When the combination of technical solutions is contradictory or cannot be implemented, it should be considered that such combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0071] An embodiment of the present application provides a smart curtain charging position compensation method. The smart curtain charging position compensation method is applied to a curtain system, wherein the curtain system includes a charging component 2 and a curtain drive component 3 arranged on a curtain rail 1, wherein the charging component 2 includes a movable charging magnetic element 21, and the curtain drive component 3 includes an electromagnetically attracted component 31, wherein the charging magnetic element 21 and the electromagnetically attracted component 31 can be attracted by magnetic force and form an electrical connection, and the curtain drive component 3 can move relative to the curtain rail 1 so that the electromagnetically attracted component 31 moves to an ideal charging position connected to the charging magnetic element 21.

[0072] See also Figures 2 to 4 The charging assembly 2 is arranged on the curtain rail 1 and connected to the wall socket through the charging line 22 to provide power for the entire curtain system. Figure 6As shown, the portion where the charging magnetic element 21 and the receiving magnetic element 31 are magnetically connected is cylindrical (that is, the outer cylinder 211 mentioned below), and the charging magnetic element 21 can move within a certain range in its radial direction. It is worth noting that the charging component 2 is connected to the curtain driving component 3 through two charging magnetic elements 21, one of which is positive and the other is negative.

[0073] like Figure 5 , Figure 6 and Figure 7 As shown, the charging assembly 2 includes a main housing 23, and the main housing 23 is fixed to the curtain rail 1 by a hook. Figures 2 to 4 ), the hook design can adapt to different types of rails.

[0074] See also Figure 7 The main shell 23 is divided into two left and right cavities, each of which is equipped with two charging magnetic suction parts 21 (one positive and one negative). The charging magnetic suction part 21 is composed of an outer column 211, a middle column 215 and an inner cylinder 212 arranged in sequence along the same axis. One end of the outer column 211 is located outside the main shell 23, and is used to be magnetically adsorbed with the electromagnetic suction part 31 and form an electrical connection; the other end passes through the through hole on the main shell 23 and is fixedly connected to one end of the middle column 215. The other end of the middle column 215 is inserted into the inner cylinder 212 fixed in the main shell 23.

[0075] The diameter of the through hole for the outer cylinder 211 to pass through on the main shell 23 is larger than the diameter of the outer cylinder 211, allowing the outer cylinder 211 to move in the diameter direction of the through hole. Similarly, the diameter of the middle cylinder 215 is smaller than the diameter of the inner cylinder 212, allowing the middle cylinder 215 to move in the diameter direction of the inner cylinder 212.

[0076] An annular cover 214 is provided on the peripheral wall of the outer cylinder 211 in the main shell 23 , and its maximum diameter is larger than the diameter of the through hole, so as to prevent the outer cylinder 211 and the middle cylinder 215 from moving along the axis of the through hole and detaching from the main shell 23 .

[0077] The middle column 215 is sleeved with a buffer limit spring 213, one end of which abuts against the annular cover 214 and the other end abuts against the inner wall of the main shell 23. When the outer column 211 is magnetically connected to the electromagnetically attracted member 31, the buffer limit spring 213 provides a buffer force in the length direction of the outer column 211.

[0078] The inner diameter of the buffer limit spring 213 is larger than the outer diameter of the middle column 215, allowing the middle column 215 to move in its radial direction. The middle column 215 is a permanent magnet, and the inductor 4 disposed on the side of the buffer limit spring 213 is energized to generate magnetic attraction, which can drive the middle column 215 to move in its own radial direction, thereby adjusting the position of the outer column 211.

[0079] During the adjustment process, the buffer limit spring 213 not only provides a buffering effect for the outer column 211, but also limits the movement range of the middle column 215 and the outer column 211 to prevent excessive movement.

[0080] The outer column 211 can be electrically connected to a circuit board (not shown) via a conductive wire to achieve electrical connection.

[0081] This design not only ensures the reliability of the charging connection, but also provides sufficient flexibility to effectively cope with various possible position deviations, thereby improving the overall performance and user experience of the smart curtain system.

[0082] See also Figures 2 to 6 The curtain drive assembly 3 is a mobile device installed on the curtain rail 1, which is responsible for driving the curtain to open and close. Under normal circumstances, the curtain system includes two curtain drive assemblies 3, which are located on the left and right sides of the charging assembly 2 respectively. Figures 2 to 6 In the figure, only part of the structure of the curtain driving assembly 3 is shown, and the structure such as the motor of the curtain driving assembly 3 is not shown. Since the curtain driving assembly 3 is a widely used component, it will not be introduced in detail here.

[0083] Figures 2 to 5 In the embodiment, the wires on the curtain drive assembly 3 are used to be electrically connected to the corresponding motor. In the present application, a curtain drive assembly 3 includes two electromagnetically attracted components 31 (one positive pole and one negative pole), and the electromagnetically attracted components 31 are plate-like structures to effectively abut against the end surface of the outer cylinder 211.

[0084] Figure 1 A flow chart of a smart curtain charging position compensation method provided in an embodiment of the present application. In this embodiment, the method includes:

[0085] See also Figure 1 , obtaining environmental parameters and operating status data of the curtain system, evaluating the charging environment according to the environmental parameters and operating status data, and generating charging environment evaluation information;

[0086] In one embodiment of the present invention, the environmental parameters and operating status data of the curtain system are obtained, and the charging environment is evaluated according to the environmental parameters and operating status data to generate charging environment evaluation information, including: obtaining the environmental parameters and operating status data of the curtain system, wherein the environmental parameters include ambient temperature, ambient humidity and ambient magnetic field strength, and the operating status data include deformation data of the curtain rail and motion parameters of the curtain drive component; calculating the material expansion coefficient according to the ambient temperature and ambient humidity, and calculating the actual deformation of the curtain rail based on the material expansion coefficient and the deformation data of the curtain rail; calculating the magnetic field interference intensity based on the ambient magnetic field strength and the motion parameters of the curtain drive component; and generating the charging environment evaluation information including the rail deformation compensation value and the magnetic field strength adjustment parameter according to the actual deformation of the curtain rail and the magnetic field interference intensity.

[0087] Specifically, the implementation of the intelligent curtain charging position compensation method first requires obtaining the environmental parameters and operating status data of the curtain system. Environmental parameters include ambient temperature, ambient humidity, and ambient magnetic field strength, and operating status data include deformation data of the curtain rail and motion parameters of the curtain drive component. These data are obtained through a sensor network installed throughout the curtain system. Specifically, the temperature and humidity sensor measures the ambient temperature and humidity, the Hall effect sensor measures the ambient magnetic field strength, the strain gauge measures the deformation of the curtain rail, and the photoelectric encoder and acceleration sensor are used to obtain motion parameters such as the position and speed of the curtain drive component.

[0088] After acquiring the data, the system first calculates the material expansion coefficient based on the ambient temperature and humidity. This step uses the linear thermal expansion theory in material science to consider the combined effects of temperature and humidity on the material. The calculation formula is: α=α0+βΔT+γΔH, where α is the actual expansion coefficient, α0 is the expansion coefficient under standard conditions, β and γ are the temperature and humidity influencing factors, and ΔT and ΔH are the changes in temperature and humidity, respectively. For example, when the temperature rises from 20°C to 30°C and the humidity rises from 50% to 70%, the system will accurately calculate the actual expansion coefficient of the guide rail material based on these changes.

[0089] Next, the system uses the calculated material expansion coefficient and the deformation data of the curtain rail to calculate the actual deformation of the curtain rail. This step uses the finite element analysis method to divide the rail into multiple tiny units. For each unit, the system calculates its deformation by considering the material expansion and external force. Then, through the numerical integration method, the deformation of each unit is combined to obtain the actual deformation state of the entire rail. For example, if the middle part of the rail is slightly sunk by 2mm due to force, and the whole expands by 1mm due to temperature increase, the system will accurately calculate the final position deviation of each part of the rail.

[0090] At the same time, the system calculates the magnetic field interference intensity based on the ambient magnetic field strength and the motion parameters of the curtain drive component. This step uses the magnetic field superposition principle and dynamic magnetic field analysis technology. First, the system establishes an ambient magnetic field distribution model to record the magnetic field strength and direction at each point in space. Then, based on the motion parameters of the curtain drive component (such as speed and acceleration), the dynamic magnetic field generated during movement is calculated. Using Maxwell's equations, the system simulates the superposition effect of the two magnetic fields to derive the actual magnetic field distribution during the charging process. For example, if the ambient magnetic field strength is 0.5 Gauss and the dynamic magnetic field strength generated by the movement of the drive component is 0.3 Gauss, the system will calculate the vector sum of the two magnetic fields at each point in space to obtain the accurate magnetic field interference intensity.

[0091] Finally, the system generates charging environment assessment information including rail deformation compensation value and magnetic field strength adjustment parameters based on the actual deformation of the curtain rail and the calculated magnetic field interference intensity. The rail deformation compensation value directly reflects the position and angle that the charging magnet needs to be adjusted to adapt to the actual deformation state of the rail. For example, if the rail sinks 2mm at a certain point, the compensation value will instruct the charging magnet to move up 2mm. The magnetic field strength adjustment parameter is used to adjust the magnetic field strength of the charging magnet to overcome the environmental magnetic field interference and ensure stable magnetic adsorption. If the calculated magnetic field interference intensity is 0.8 Gauss, the system will increase the magnetic field strength of the charging magnet accordingly to ensure sufficient adsorption force.

[0092] This comprehensive and accurate environmental assessment method has multiple advantages. It can adapt to environmental changes in real time, ensuring that the charging system can maintain high-precision docking under various conditions. By taking into account the deformation of the guide rail, the system can overcome the position deviation caused by long-term use or installation errors, greatly improving the charging success rate. The calculation and compensation of the magnetic field interference intensity ensures the stability of magnetic adsorption during charging and reduces the problem of charging interruptions caused by magnetic field fluctuations. In addition, this method is highly adaptable and is not only suitable for different types of curtain systems, but also maintains good performance in various installation environments.

[0093] Through this series of steps, the smart curtain charging position compensation method can generate highly accurate charging environment assessment information, providing a reliable decision-making basis for subsequent position compensation and charging processes, thereby significantly improving the charging efficiency and overall performance of the smart curtain system.

[0094] In one embodiment of the present invention, the calculation of the magnetic field interference intensity based on the environmental magnetic field strength and the motion parameters of the curtain drive component includes: obtaining real-time magnetic field distribution data around the charging magnetic component; calculating the instantaneous relative position and speed of the electromagnetically attracted component relative to the charging magnetic component according to the motion parameters of the curtain drive component; calculating the magnetic field gradient and change rate based on the real-time magnetic field distribution data and the instantaneous relative position and speed; performing fast Fourier transform on the magnetic field gradient and change rate to obtain magnetic field spectrum characteristics; performing pattern matching on the magnetic field spectrum characteristics according to a preset magnetic field interference pattern library to identify the current magnetic field interference type; and determining the magnetic field interference intensity by searching a preset correspondence table based on the magnetic field interference type and the environmental magnetic field strength.

[0095] Specifically, in the process of implementing the smart curtain charging position compensation method, obtaining real-time magnetic field distribution data around the charging magnet is a crucial step. This step is accomplished by arranging an array of Hall effect sensors around the charging magnet. These sensors can measure the magnetic field strength and direction in three-dimensional space in real time to form a detailed magnetic field distribution map. For example, 16 sensors are arranged around the charging magnet to form a 4 The 4-sensor array, with each sensor sampling 100 times per second, can provide fully detailed magnetic field distribution data. This high-density, high-frequency data acquisition ensures the accuracy and real-time nature of subsequent analysis.

[0096] Next, the system calculates the instantaneous relative position and speed of the electromagnetically attracted component relative to the charging magnetic component based on the motion parameters of the curtain drive component. This step uses the data provided by the position encoder and acceleration sensor on the curtain drive component, combined with the principle of kinematics for calculation. The system first obtains the absolute position and speed of the drive component, and then considers the fixed position of the charging magnetic component to calculate the relative position and speed of the two through coordinate transformation. For example, if the curtain drive component moves toward the charging magnetic component at a speed of 0.5m / s and the current distance is 0.3m, the system will calculate the relative position change and approach speed between them in real time. This precise relative motion calculation provides an important basis for subsequent magnetic field analysis.

[0097] Based on real-time magnetic field distribution data and instantaneous relative position and velocity, the system calculates the magnetic field gradient and rate of change. The magnetic field gradient reflects the rate of change of magnetic field strength in space, while the rate of change describes the change of the magnetic field over time. When calculating the magnetic field gradient, the system uses the finite difference method to compare the difference in magnetic field strength between adjacent sensors. For the rate of change, the system compares the change in magnetic field strength at consecutive time points. For example, if the magnetic field strength changes from 100μT to 90μT within a distance of 0.1m, the system will calculate the magnetic field gradient as 100μT / m. At the same time, if the magnetic field strength changes by 5μT within 0.1 seconds, the system will derive a rate of change of 50μT / s. These calculation results provide important input for subsequent spectrum analysis.

[0098] The next key step is to perform a fast Fourier transform (FFT) on the magnetic field gradient and rate of change, in order to obtain the magnetic field spectrum characteristics. FFT converts time domain signals into frequency domain signals, which can reveal periodic patterns in magnetic field changes. The system applies the FFT algorithm to the time series data of magnetic field gradient and rate of change to obtain their spectral representation. For example, if there is a 50Hz periodic fluctuation in the magnetic field change, the FFT analysis will show an obvious 50Hz peak in the spectrum. This spectral analysis can effectively separate the magnetic field changes caused by environmental noise, power system interference and drive component movement, providing an important basis for subsequent interference identification.

[0099] Then, the system matches the magnetic field spectrum features according to the preset magnetic field interference pattern library to identify the current magnetic field interference type. This pattern library contains the characteristic spectra of various common magnetic field interferences, such as power line interference, motor operation interference, external electronic equipment interference, etc. The system uses pattern recognition algorithms, such as support vector machines (SVM) or convolutional neural networks (CNN), to compare the obtained spectrum features with the patterns in the library. For example, if there are obvious 50Hz and its harmonic peaks in the spectrum, the system may identify it as power line interference. This intelligent recognition method can accurately distinguish magnetic field interference from different sources and provide precise type information for subsequent interference intensity assessment.

[0100] Finally, based on the identified magnetic field interference type and ambient magnetic field strength, the system determines the magnetic field interference strength by looking up a preset correspondence table. This correspondence table is established based on a large amount of experimental data and theoretical analysis. It associates different types of interference, ambient magnetic field strength, and actual interference strength. For example, if medium-intensity power line interference is identified and the ambient magnetic field strength is 40μT, the system may find the corresponding interference strength of 15μT from the table. This method takes into account the combined effects of interference type and background magnetic field, and can provide a more accurate estimate of interference strength.

[0101] The advantage of this series of steps lies in its comprehensiveness and accuracy. By acquiring detailed magnetic field distribution data in real time, the system can capture tiny magnetic field changes. Accurate calculation of relative motion parameters ensures the spatiotemporal accuracy of magnetic field analysis. FFT analysis and pattern matching techniques can effectively identify complex magnetic field interference patterns, rather than relying solely on simple intensity measurements. Finally, the interference intensity is determined by the table lookup method, which ensures both computational efficiency and accuracy. This method can effectively cope with various complex magnetic field environments and provide reliable magnetic field interference assessment for the smart curtain charging system, thereby supporting subsequent charging position compensation decisions and improving charging efficiency and reliability.

[0102] Please continue reading Figure 1 , predicting the motion trajectory of the curtain driving component according to the charging environment evaluation information and the real-time position data of the curtain driving component, and estimating the actual stop position of the electromagnetically attracted component based on the predicted motion trajectory;

[0103] In one embodiment of the present invention, the motion trajectory of the curtain drive component is predicted according to the charging environment assessment information and the real-time position data of the curtain drive component, and the actual stop position of the electromagnetically attracted component is estimated based on the predicted motion trajectory, including: obtaining historical motion data and current motion state parameters of the curtain drive component; correcting the historical motion data according to the guide rail deformation compensation value in the charging environment assessment information to obtain corrected historical motion data; calculating the predicted motion trajectory of the curtain drive component using a preset trajectory prediction algorithm based on the corrected historical motion data and the current motion state parameters; calculating the magnetic influence factor of each point on the predicted motion trajectory according to the magnetic field strength adjustment parameter in the charging environment assessment information; applying the magnetic influence factor to the predicted motion trajectory to obtain a corrected predicted motion trajectory; and calculating the actual stop position of the electromagnetically attracted component based on the corrected predicted motion trajectory and a preset stop condition.

[0104] Specifically, historical motion data usually includes information such as the position, velocity, and acceleration of the curtain drive component over a period of time in the past (such as the last 24 hours), and these data are stored in the system's database. The current motion state parameters are obtained in real time, including the instantaneous position, velocity, acceleration, and direction of motion of the drive component. These data are obtained through a high-precision sensor network installed on the curtain drive component. Typical sensors include photoelectric encoders (for precise position measurement), Hall effect sensors (for speed measurement), and microelectromechanical systems (MEMS) accelerometers (for acceleration measurement). For example, a photoelectric encoder may record 100 pulses per millimeter, providing sub-millimeter position accuracy, while a MEMS accelerometer can detect acceleration changes of 0.01g. This high-precision data acquisition lays a solid foundation for subsequent trajectory prediction.

[0105] Next, the system corrects the historical motion data based on the rail deformation compensation value in the charging environment assessment information. This step is crucial because the deformation of the curtain rail will directly affect the actual motion trajectory of the drive assembly. The correction process uses coordinate transformation and interpolation technology. First, the system constructs a deformation mapping function based on the rail deformation compensation value, which describes the degree of deformation of the rail at different positions. Then, the system applies this mapping function to the historical motion data and corrects the position of each data point. For example, if a 2mm sinking is detected at a certain section of the rail, all historical position data passing through that section will be adjusted up by 2mm accordingly. This correction ensures that the historical data can accurately reflect the actual movement of the drive assembly under the current rail state, providing more reliable input for subsequent trajectory predictions.

[0106] Based on the corrected historical motion data and current motion state parameters, the system uses a preset trajectory prediction algorithm to calculate the predicted motion trajectory of the curtain drive component. A hybrid prediction model combining Kalman filtering and long short-term memory network (LSTM) is used here. Kalman filtering is used for short-term prediction. It can effectively handle measurement noise and system uncertainty and provide smooth short-term trajectory estimates. The LSTM network is responsible for capturing long-term motion patterns, such as user usage habits and timing settings. The system first uses the Kalman filter to process the recent motion data to obtain an immediate state estimate and short-term prediction. These short-term prediction results are then input into the LSTM network together with historical data to generate trajectory predictions for a longer time scale. For example, the system may predict the detailed motion trajectory of the curtain drive component in the next 30 seconds, including position, velocity, and acceleration every 100 milliseconds. This hybrid approach ensures both real-time and accuracy of predictions while taking into account long-term motion patterns and trends.

[0107] Subsequently, the system calculates the magnetic influence factor of each point on the predicted motion trajectory based on the magnetic field strength adjustment parameters in the charging environment assessment information. This step takes into account the influence of the ambient magnetic field and the magnetic field of the charging magnetic suction part on the movement of the curtain drive assembly. The system first establishes a magnetic field intensity distribution model based on the position and strength of the charging magnetic suction part, as well as the measurement results of the ambient magnetic field. Then, for each point on the predicted trajectory, the system calculates the synthetic magnetic field strength and direction at that point. The magnetic influence factor is calculated based on the magnetic field strength, the speed of the drive assembly, and the magnetization characteristics, and it reflects the degree of influence of the magnetic field on the movement of the drive assembly. For example, if the magnetic field strength at a certain point is large and the direction is opposite to the direction of movement, then the magnetic influence factor at that point will be large, indicating that the drive assembly may be hindered by a strong magnetic force here.

[0108] The magnetic influence factor is applied to the predicted motion trajectory, and the system obtains a corrected predicted motion trajectory. This step is achieved by adjusting the speed and position of each point on the predicted trajectory. Specifically, the system uses the magnetic influence factor to correct the acceleration of each point, and then recalculates the speed and position. For example, if the magnetic influence factor of a point indicates that there will be a 10% slowdown, the system will adjust the speed and position prediction of this point and subsequent points accordingly. This correction takes into account the actual impact of the magnetic field on the movement of the drive component, making the predicted trajectory more consistent with the actual situation.

[0109] Finally, based on the corrected predicted motion trajectory and the preset stop conditions, the system calculates the actual stop position of the electromagnetically attracted part. The stop conditions usually include speed thresholds, position thresholds, and time thresholds. The system checks point by point along the corrected predicted trajectory until the stop conditions are met. For example, the stop condition may be set to a speed less than 0.01m / s and lasting for 0.5 seconds, or a distance less than 5mm from the charging magnetic part. Once these conditions are met, the point is determined to be the actual stop position of the electromagnetically attracted part.

[0110] The advantage of this series of steps is its comprehensiveness and accuracy. By considering historical data, real-time status, environmental factors and magnetic field effects, the system is able to generate highly accurate motion trajectory predictions. Correction of rail deformation ensures that the prediction is based on the current actual physical state. The hybrid prediction algorithm takes into account both short-term fluctuations and long-term trends. Consideration of magnetic effects further improves the accuracy of the prediction, especially when approaching the charging position. This approach can effectively cope with complex motion environments and external interference, and provide reliable stop position predictions for the smart curtain charging system, thereby supporting subsequent charging position compensation decisions and improving charging efficiency and reliability.

[0111] In one embodiment of the present invention, the predicted motion trajectory of the curtain drive component is calculated based on the corrected historical motion data and the current motion state parameters using a preset trajectory prediction algorithm, including: performing time series analysis on the corrected historical motion data to extract motion characteristics of the curtain drive component; calculating the instantaneous acceleration of the curtain drive component based on the motion characteristics and the current motion state parameters; generating a preliminary predicted trajectory based on the instantaneous acceleration and the corrected historical motion data using a preset trajectory prediction algorithm; segmenting the preliminary predicted trajectory to obtain multiple trajectory segments; calculating the curvature and velocity change rate of each trajectory segment; smoothing each trajectory segment based on the curvature and velocity change rate to obtain a smoothed trajectory segment; and splicing all smoothed trajectory segments to obtain a complete predicted motion trajectory.

[0112] Specifically, time series analysis of the corrected historical motion data is a key step to extract the motion characteristics of the curtain drive component. This step uses time series analysis techniques such as autoregressive integrated moving average model (ARIMA) and wavelet transform. The ARIMA model can capture trends and periodicity in the data, while the wavelet transform helps to identify features at different time scales. The system first divides the historical data into multiple time windows and then applies these analysis techniques to each window. For example, the system may find that the curtain drive component has frequent opening and closing movements during a specific time period every day (such as 7 to 8 in the morning), or more frequent activities on certain days of the week. This analysis can not only identify regular motion patterns, but also discover abnormal motion behaviors, providing important basis for subsequent trajectory prediction.

[0113] Based on the extracted motion features and the current motion state parameters, the system calculates the instantaneous acceleration of the curtain drive component. This step takes into account the historical motion pattern and the current state, using numerical differentiation and Kalman filtering techniques. First, the system calculates a preliminary acceleration estimate based on the current position and velocity data using the central difference method. This estimate is then combined with the motion features extracted from the historical data and optimized using a Kalman filter. For example, if the historical data shows that the curtains usually have a slow acceleration process when they start to move, then the system will give a smoother acceleration estimate even if the current velocity data shows a sudden velocity change. This method can effectively filter out noise and provide a more accurate and stable acceleration estimate.

[0114] Based on the calculated instantaneous acceleration and the corrected historical motion data, the system uses a preset trajectory prediction algorithm to generate a preliminary predicted trajectory. A hybrid prediction method combining physical models and machine learning is used here. The physical model is based on Newton's laws of motion and takes into account factors such as the mass and friction of the curtain drive components to accurately predict short-term motion trajectories. Machine learning models, such as recurrent neural networks (RNNs), are used to capture long-term motion patterns and user habits. The system first uses a physical model to generate an accurate trajectory in the short term (such as the next 5 seconds), and then inputs this short-term prediction result into the RNN model to generate a trajectory prediction for a longer time range (such as the next 30 seconds). This hybrid method not only ensures the physical accuracy of short-term predictions, but also takes into account long-term motion trends and possible user intervention.

[0115] The preliminary predicted trajectory is segmented to more finely analyze and optimize different parts of the trajectory. The system uses change point detection algorithms, such as Bayesian change point detection or sliding window variance analysis, to identify key turning points in the trajectory. These turning points may represent acceleration, deceleration, or direction changes in the curtain movement. For example, in a typical curtain opening and closing process, there may be an initial acceleration segment, a constant speed segment, and a final deceleration segment. The system segments the trajectory at these feature points so that each segment can be analyzed and processed more accurately.

[0116] For each trajectory segment, the system calculates its curvature and rate of change of velocity. Curvature reflects the degree of curvature of the trajectory, while the rate of change of velocity describes the change in acceleration. The curvature calculation uses formulas from differential geometry, which considers the shape of the trajectory in three-dimensional space. The rate of change of velocity is obtained by numerically differentiating the velocity data. For example, if a segment of the trajectory shows high curvature, it may mean that the curtain drive assembly encountered an obstacle or made a directional adjustment at this point. A high rate of change of velocity may indicate a sharp acceleration or deceleration, which is more common during the starting or stopping phases.

[0117] Based on the calculated curvature and velocity change rate, the system smoothes each trajectory segment. This step aims to eliminate unreasonable fluctuations in the predicted trajectory and make it more consistent with the actual motion characteristics of the curtain drive assembly. Smoothing uses techniques such as local weighted regression (LOESS) or spline interpolation. For areas with higher curvature or higher velocity change rate, the system applies a stronger smoothing effect to eliminate possible prediction errors. For example, if a segment of the trajectory shows a sharp turn that does not conform to physical laws, smoothing will adjust it to a more reasonable curve. This processing not only improves the physical plausibility of the trajectory, but also helps to reduce oscillations and instabilities in subsequent control processes.

[0118] Finally, the system stitches all the smoothed segments together to get the complete predicted motion trajectory. During the stitching process, the system pays special attention to the continuity and smoothness between segments. Techniques such as spline functions or Bezier curves are used to ensure smooth transitions between adjacent segments and avoid mutation points. For example, at the junction of two trajectory segments, the system adjusts the tangent direction and curvature of the curve to ensure the continuity of the overall trajectory. This carefully processed stitching process ensures the naturalness and coherence of the final predicted trajectory.

[0119] The advantage of this series of steps lies in its comprehensiveness and accuracy. By extracting motion features through time series analysis, the system can make full use of information in historical data. The accurate calculation of instantaneous acceleration provides a reliable basis for short-term prediction. The hybrid prediction algorithm combines the accuracy of the physical model with the adaptability of machine learning. Segment processing and smoothing techniques further optimize the predicted trajectory to make it more consistent with the actual motion characteristics. This method can effectively handle complex motion patterns and external interference, and provide highly accurate motion trajectory prediction for the smart curtain charging system, thereby supporting accurate charging position compensation, improving charging efficiency and system reliability.

[0120] Please continue reading Figure 1 , based on the actual stop position of the electromagnetically attracted component and the current state of the charging component, calculating the position offset amount of the charging magnetic component that needs to be adjusted;

[0121] In one embodiment of the present invention, the position offset of the charging magnetic element that needs to be adjusted is calculated based on the actual stop position of the electromagnetically attracted element and the current state of the charging component, including: obtaining the current position coordinates of the charging magnetic element and the actual stop position coordinates of the electromagnetically attracted element; calculating the initial position difference between the current position coordinates of the charging magnetic element and the actual stop position coordinates of the electromagnetically attracted element; determining the movable range of the charging magnetic element according to the current state of the charging component; calculating the optimal position adjustment vector based on the initial position difference and the movable range; comparing the optimal position adjustment vector with a preset safety threshold to obtain a corrected position adjustment vector; and calculating the position offset of the charging magnetic element that needs to be adjusted according to the corrected position adjustment vector.

[0122] Specifically, obtaining the current position coordinates of the charging magnet and the actual stop position coordinates of the magnet receiving magnet is the key first step. This step is completed by a high-precision positioning system. For the charging magnet, its position is usually fixed, but considering possible minor movements or installation errors, the system will recalibrate its position regularly. This calibration may use laser ranging or capacitive position sensors with an accuracy of up to microns. For example, a laser ranging system can perform 100 measurements per second to ensure the real-time accuracy of the position of the charging magnet. For the magnet receiving magnet, its actual stop position is calculated based on the aforementioned trajectory prediction and stop conditions. The system converts these two position coordinates into the same reference coordinate system, usually a three-dimensional coordinate system based on the curtain rail.

[0123] Calculating the initial position difference between the current position coordinates of the charging magnetic element and the actual stop position coordinates of the electromagnetic element is the next key step. This calculation uses vector subtraction, taking into account the three directions of x, y, and z in three-dimensional space. The system not only calculates the straight-line distance, but also analyzes the deviation in each direction. For example, if the coordinates of the charging magnetic element are (100, 50, 10) mm, and the predicted stop position of the electromagnetic element is (105, 52, 11) mm, then the system will calculate the initial position difference vector as (5, 2, 1) mm. This precise difference calculation provides basic data for subsequent position adjustments.

[0124] Determining the movable range of the charging magnet based on the current state of the charging component is an important constraint setting step. The state of the charging component includes factors such as its structural limitations, the current power supply, and the surrounding environment. The system takes into account the mechanical structural limitations of the charging magnet, such as the maximum moving distance and angle range. At the same time, the current power supply capacity is also evaluated to ensure that the adjustment process does not exceed the power limit of the system. For example, if the maximum movement range of the charging magnet is ±10mm in each direction, and the current power supply can only support 80% of the maximum movement distance, the system will set the movable range to ±8mm. This dynamic range setting ensures the safety and reliability of the adjustment process.

[0125] Based on the initial position difference and the movable range, the system calculates the optimal position adjustment vector. This step uses an optimization algorithm, such as gradient descent or genetic algorithm, to find an adjustment solution that minimizes the position difference while satisfying the movable range constraint. The optimization process considers multiple objective functions, including minimizing the adjustment distance, maximizing matching accuracy, minimizing energy consumption, etc. For example, if the initial position difference is (5,2,1) mm and the movable range is ±8 mm, the system may calculate the optimal adjustment vector to be (5,2,1) mm, which completely compensates for the difference. However, if the movable range is limited, the system may give a compromise solution, such as (4,2,1) mm, to maximize the matching effect within a limited range.

[0126] The optimal position adjustment vector is compared with the preset safety threshold to obtain the corrected position adjustment vector. This step is intended to further ensure the safety and reliability of the adjustment process. The safety threshold is set based on the physical limitations of the system and operating experience, and usually includes the maximum adjustment distance, maximum adjustment speed, etc. The system checks each component of the optimal adjustment vector one by one to ensure that they do not exceed the corresponding safety threshold. If a component exceeds the threshold, the system will scale it to within the threshold range. For example, if the maximum safe adjustment distance is 6mm, and a component of the optimal adjustment vector is 7mm, the system will correct it to 6mm. This safety check mechanism effectively prevents the risks that may be caused by over-adjustment.

[0127] Finally, based on the corrected position adjustment vector, the system calculates the position offset that the charging magnet needs to adjust. This step converts the corrected vector into a specific execution instruction. The system takes into account the current position and target position of the charging magnet and calculates the specific moving distance in the x, y, and z directions. At the same time, a smooth motion path is planned, including acceleration, constant speed, and deceleration stages to ensure the stability of the adjustment process. For example, if the corrected adjustment vector is (4,2,1) mm, the system may generate a series of intermediate points, such as (2,1,0.5) mm, (3,1.5,0.75) mm, and (4,2,1) mm, to form a smooth adjustment trajectory.

[0128] The advantages of this series of steps are its accuracy, safety and adaptability. Through high-precision positioning and precise difference calculation, the system can accurately identify the amount that needs to be adjusted. Dynamic determination of the movable range ensures that the adjustment process is always within a safe and controllable range. The use of optimization algorithms ensures that the best adjustment solution can be found under various constraints. The introduction of safety thresholds further enhances the reliability of the system. Finally, smooth adjustment path planning ensures the stability of the execution process. This method can effectively cope with various charging scenarios, accurately compensate for position deviations, and greatly improve the docking success rate and charging efficiency of the smart curtain charging system.

[0129] Please continue reading Figure 1 According to the position offset, the charging magnetic attraction component is controlled by an electromagnetic driving mechanism to adjust the position so that the charging magnetic attraction component is docked with the receiving electromagnetic attraction component.

[0130] In one embodiment of the present invention, the position adjustment of the charging magnetic element is controlled by an electromagnetic drive mechanism according to the position offset so that the charging magnetic element docks with the receiving electromagnetic element, including: converting the position offset into a drive parameter of the electromagnetic drive mechanism; calculating the excitation current value of each drive unit of the electromagnetic drive mechanism according to the drive parameter; applying a corresponding excitation current to each drive unit in turn according to a preset adjustment sequence to drive the charging magnetic element to move; during the position adjustment process, real-time monitoring of the position change of the charging magnetic element; dynamically adjusting the excitation current values ​​of the remaining drive units according to the position change to ensure that the charging magnetic element moves according to the position offset; when it is detected that the charging magnetic element has reached the target position, fine-tuning the position of the charging magnetic element until it docks with the receiving electromagnetic element; verifying the docking state of the charging magnetic element and the receiving electromagnetic element to ensure that the electrical connection is established.

[0131] Specifically, in the implementation process of the smart curtain charging position compensation method, converting the position offset into the driving parameters of the electromagnetic drive mechanism is the key first step. This step involves complex coordinate transformation and physical model calculation. The system first decomposes the position offset in three-dimensional space into the motion of each axis of the electromagnetic drive mechanism. Then, based on the physical properties of the electromagnetic drive mechanism (such as the geometry of the electromagnetic coil, the strength of the magnet, etc.), a relationship model between displacement and driving force is established. For example, if the position offset is (2mm, 1mm, 0.5mm), the system may convert it into an x-axis driving force of 0.5N, a y-axis driving force of 0.25N, and a z-axis driving force of 0.1N. This precise conversion ensures the accuracy of the subsequent driving process.

[0132] The next important step is to calculate the excitation current value of each drive unit of the electromagnetic drive mechanism based on the drive parameters. This calculation is based on the principle of electromagnetics and takes into account the relationship between the resistance, inductance, and magnetic field strength of the coil and the current. The system uses the finite element analysis method to simulate the electromagnetic field distribution, and then determines the required current value by reverse solution. For example, in order to generate a driving force of 0.5N on the x-axis, the system may calculate that a current of 1.2A is required. This precise current calculation not only ensures the accuracy of the driving force, but also optimizes energy efficiency.

[0133] According to the preset adjustment sequence, the system applies the corresponding excitation current to each drive unit in turn to drive the charging magnetic suction parts to move. This sequence is usually based on kinematic analysis to ensure smooth and precise adjustment. For example, the system may adjust the z-axis (height) first, then the y-axis (left and right), and finally the x-axis (front and back). The adjustment of each axis uses precise current control, and the fine adjustment of the current is achieved through pulse width modulation (PWM) technology. This step-by-step adjustment strategy helps to reduce mutual interference during the adjustment process and improve overall accuracy.

[0134] During the position adjustment process, the system monitors the position change of the charging magnetic element in real time. This monitoring is achieved through high-precision position sensors, such as optical encoders or magnetic position sensors. These sensors can provide micron-level position resolution and can perform thousands of samples per second. For example, an optical encoder may generate a pulse every 0.1 micron, thereby achieving extremely high position tracking accuracy. Real-time monitoring not only provides accurate position feedback, but also detects any abnormalities or deviations in a timely manner.

[0135] Based on the monitored position changes, the system dynamically adjusts the excitation current value of the remaining drive units to ensure that the charging magnetic suction parts move accurately according to the position offset. This process uses a closed-loop control strategy such as PID (proportional-integral-differential) control or a more advanced model predictive control (MPC). The system compares the difference between the actual position and the target position in real time, and dynamically adjusts the current value based on this difference. For example, if it is detected that the movement speed in the x-axis direction is slightly faster, the system will appropriately reduce the current of the x-axis drive unit. This real-time adjustment ensures the accuracy and smoothness of the entire movement process.

[0136] When the system detects that the charging magnetic part has reached the target position, it will make fine adjustments to achieve precise docking with the receiving magnetic part. This fine-tuning process uses smaller current increments and higher frequency position feedback. The system may use an iterative method to make very small adjustments each time until the predetermined accuracy requirements are met. For example, the final fine-tuning may be performed in units of 10 microns until the position error is less than 50 microns. This fine fine-tuning ensures high accuracy of the final docking.

[0137] Finally, the system verifies the docking status of the charging magnet and the receiving magnet to ensure that the electrical connection is established. This verification process includes multiple aspects: first, the relative position of the two magnets is confirmed by the position sensor; second, the magnetic force is measured to ensure that the magnetic adsorption reaches the expected strength; finally, an electrical connection test is performed to check whether the circuit is closed and whether the resistance is within the normal range. For example, the system may send a weak test signal to detect the transmission of the signal to verify the quality of the connection. Only when all these checks are passed will the system confirm that the docking is successfully completed.

[0138] The advantage of this series of steps is its high accuracy, reliability and adaptability. Through precise parameter conversion and current control, the system can achieve extremely high-precision position adjustment. Real-time monitoring and dynamic adjustment ensure the stability and accuracy of the entire process. Fine-tuning and verification steps further improve the success rate of docking. This method can effectively deal with various possible interferences and errors, ensuring that the smart curtain charging system can achieve reliable charging docking in different environments and conditions, thereby greatly improving the overall performance of the system and user experience.

[0139] The above description is only a preferred embodiment of the present invention, and does not limit the patent scope of the present invention. All equivalent structural changes made by using the contents of the present invention specification and drawings under the inventive concept of the present invention, or directly / indirectly applied in other related technical fields are included in the patent protection scope of the present invention.

Claims

1. A method for compensating the charging position of a smart curtain, characterized in that: The intelligent curtain charging position compensation method is applied to a curtain system, wherein the curtain system comprises a charging component and a curtain driving component arranged on a curtain rail, wherein the charging component comprises a movable charging magnetic element, and the curtain driving component comprises an electromagnetic receiving element, wherein the charging magnetic element and the electromagnetic receiving element can be attracted and electrically connected by magnetic force, and the curtain driving component can move relative to the curtain rail so that the electromagnetic receiving element moves to an ideal charging position connected to the charging magnetic element; The smart curtain charging position compensation method comprises: Acquiring environmental parameters and operating status data of the curtain system, evaluating the charging environment according to the environmental parameters and operating status data, and generating charging environment evaluation information; Predicting the motion trajectory of the curtain driving component according to the charging environment assessment information and the real-time position data of the curtain driving component, and estimating the actual stop position of the electromagnetically attracted component based on the predicted motion trajectory; Calculating the position offset of the charging magnetic element that needs to be adjusted based on the actual stop position of the magnetic element and the current state of the charging assembly; According to the position offset, the charging magnetic attraction component is controlled by an electromagnetic driving mechanism to adjust the position so that the charging magnetic attraction component is docked with the receiving electromagnetic attraction component.

2. The smart curtain charging position compensation method according to claim 1, characterized in that: The acquiring of environmental parameters and operating status data of the curtain system, evaluating the charging environment according to the environmental parameters and operating status data, and generating charging environment evaluation information includes: Acquire environmental parameters and operating status data of the curtain system, wherein the environmental parameters include ambient temperature, ambient humidity and ambient magnetic field strength, and the operating status data includes deformation data of the curtain rail and motion parameters of the curtain drive assembly; Calculating a material expansion coefficient according to the ambient temperature and ambient humidity, and calculating an actual deformation amount of the curtain rail based on the material expansion coefficient and the deformation data of the curtain rail; Calculating magnetic field interference intensity based on the environmental magnetic field intensity and the motion parameters of the curtain drive assembly; According to the actual deformation amount of the curtain rail and the magnetic field interference intensity, charging environment assessment information including rail deformation compensation value and magnetic field intensity adjustment parameter is generated.

3. The smart curtain charging position compensation method according to claim 2, characterized in that: The calculating the magnetic field interference intensity based on the environmental magnetic field intensity and the motion parameter of the curtain driving component comprises: Acquiring real-time magnetic field distribution data around the charging magnetic attraction component; Calculating the instantaneous relative position and speed of the electromagnetically attracted component relative to the charging magnetic component according to the motion parameters of the curtain driving component; Calculating the magnetic field gradient and rate of change based on the real-time magnetic field distribution data and the instantaneous relative position and velocity; Performing fast Fourier transform on the magnetic field gradient and change rate to obtain magnetic field spectrum characteristics; According to a preset magnetic field interference pattern library, pattern matching is performed on the magnetic field spectrum characteristics to identify the current magnetic field interference type; Based on the magnetic field interference type and the environmental magnetic field strength, the magnetic field interference strength is determined by searching a preset corresponding relationship table.

4. The smart curtain charging position compensation method according to claim 1, characterized in that: The method of predicting the motion trajectory of the curtain driving component according to the charging environment assessment information and the real-time position data of the curtain driving component, and estimating the actual stop position of the electromagnetically attracted component based on the predicted motion trajectory, includes: Acquire historical motion data and current motion state parameters of the curtain drive component; According to the guide rail deformation compensation value in the charging environment assessment information, the historical motion data is corrected to obtain corrected historical motion data; Based on the corrected historical motion data and the current motion state parameters, a preset trajectory prediction algorithm is used to calculate a predicted motion trajectory of the curtain drive assembly; Calculating the magnetic force influence factor of each point on the predicted motion trajectory according to the magnetic field strength adjustment parameter in the charging environment assessment information; Applying the magnetic influence factor to the predicted motion trajectory to obtain a corrected predicted motion trajectory; Based on the corrected predicted motion trajectory and the preset stop condition, the actual stop position of the electromagnetically attracted component is calculated.

5. The smart curtain charging position compensation method according to claim 4, characterized in that: The method of calculating the predicted motion trajectory of the curtain driving component based on the corrected historical motion data and the current motion state parameters using a preset trajectory prediction algorithm includes: Performing time series analysis on the corrected historical motion data to extract motion features of the curtain drive component; Calculating the instantaneous acceleration of the curtain drive assembly according to the motion characteristics and the current motion state parameters; Based on the instantaneous acceleration and the corrected historical motion data, a preliminary predicted trajectory is generated using a preset trajectory prediction algorithm; Segmenting the preliminary predicted trajectory to obtain a plurality of trajectory segments; For each trajectory segment, calculate its curvature and velocity change rate; According to the curvature and the speed change rate, each trajectory segment is smoothed to obtain a smoothed trajectory segment; All smoothed trajectory segments are concatenated to obtain the complete predicted motion trajectory.

6. The smart curtain charging position compensation method according to claim 1, characterized in that: The calculating the position offset amount of the charging magnetic element that needs to be adjusted based on the actual stop position of the magnetic element and the current state of the charging component includes: Obtaining the current position coordinates of the charging magnetic attraction component and the actual stop position coordinates of the receiving magnetic attraction component; Calculating the initial position difference between the current position coordinates of the charging magnetic attraction component and the actual stop position coordinates of the receiving magnetic attraction component; Determining a movable range of the charging magnetic element according to a current state of the charging component; Calculating an optimal position adjustment vector based on the initial position difference and the movable range; Comparing the optimal position adjustment vector with a preset safety threshold to obtain a corrected position adjustment vector; The position offset of the charging magnetic component that needs to be adjusted is calculated based on the corrected position adjustment vector.

7. The smart curtain charging position compensation method according to claim 1, characterized in that: According to the position offset, the charging magnetic element is controlled by an electromagnetic driving mechanism to adjust the position so that the charging magnetic element is docked with the receiving electromagnetic element, including: Converting the position offset into a driving parameter of an electromagnetic driving mechanism; Calculating the excitation current value of each drive unit of the electromagnetic drive mechanism according to the drive parameters; According to a preset adjustment sequence, a corresponding excitation current is applied to each driving unit in turn to drive the charging magnetic attraction member to move; During the position adjustment process, the position change of the charging magnetic attraction component is monitored in real time; According to the position change, dynamically adjust the excitation current value of the remaining drive units to ensure that the charging magnetic attraction member moves according to the position offset; When it is detected that the charging magnetic attraction component reaches the target position, the position of the charging magnetic attraction component is finely adjusted until it docks with the receiving magnetic attraction component; Verify the docking status of the charging magnetic attraction component and the receiving magnetic attraction component to ensure that the electrical connection is established.

8. A curtain system, characterized in that: The curtain system adopts the smart curtain charging position compensation method as described in any one of claims 1 to 7.

Images