A multi-modal intelligent umbrella posture recognition and adaptive adjustment control system based on a hall sensor

Abstract

The application discloses a kind of intelligent umbrella posture recognition and adaptive adjustment control system based on Hall sensor positioning multimodal, it is related to intelligent umbrella control technical field, including, data acquisition module;Data pre-processing module;Posture recognition module;Adaptive adjustment module;Control execution module;The present application is arranged by multiple point pairing Hall sensor on umbrella body and is combined with global time reference, paired magnetic mark and IMU trigger mechanism, forms a kind of end-to-end " time-space alignment→self-calibration→hierarchical mapping " data chain.The link can eliminate common sensor positioning error, magnetic drift and manufacturing assembly tolerance, so that each Hall reading has clear and traceable geometric mapping relationship, so as to directly convert original magnetic field reading into umbrella rib local angle and aggregate into full umbrella configuration.

Description

Technical Field

[0001] This invention relates to the field of intelligent umbrella control technology, specifically to an intelligent umbrella attitude recognition and adaptive adjustment control system based on Hall sensor positioning multimodal. Background Technology

[0002] Existing smart umbrella or windproof umbrella control solutions mostly rely on a single inertial measurement unit (IMU) or several isolated angle / force sensors, resulting in sparse sampling points and a lack of precise mapping to the umbrella geometry. In practical use, such solutions are susceptible to installation tolerances, magnetic drift, and local structural deformation. Calibration is often manual or offline, leading to decreased positioning accuracy over long-term use, delayed identification and response to sudden disturbances, and difficulty in making targeted reinforcement or fine-tuning at the umbrella rib level, thus reducing reliability and user experience.

[0003] To address this issue, this application proposes an intelligent umbrella attitude recognition and adaptive adjustment control system based on Hall sensor positioning multimodal to solve the aforementioned problems. Summary of the Invention

[0004] The purpose of this invention is to provide an intelligent umbrella attitude recognition and adaptive adjustment control system based on Hall sensor positioning multimodal, in order to solve the problems in the existing technology that are easily affected by installation tolerances, magnetic environment drift and local structural deformation in actual use, and that calibration is mostly done manually or offline, resulting in a decrease in positioning accuracy after long-term use, a lag in the recognition and response to sudden disturbances, and difficulty in making targeted reinforcement or fine-tuning at the umbrella rib level.

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] A smart umbrella attitude recognition and adaptive adjustment control system based on Hall sensor positioning multimodal includes:

[0007] The data acquisition module is used to synchronously sample multiple sets of Hall sensors and optional IMUs arranged on the umbrella ribs to obtain raw magnetic field-position data;

[0008] The data preprocessing module is used to perform time-space alignment and filtering on the original magnetic field-location data to obtain aligned magnetic field data;

[0009] The attitude recognition module is used to identify the current umbrella attitude based on the alignment magnetic field data mapped to the umbrella rib geometric model and output the attitude vector.

[0010] The adaptive adjustment module is used to generate adjustment commands based on the attitude vector and the preset stabilization strategy;

[0011] The control execution module is used to convert the adjustment command into the action of the drive motor or locking mechanism to adjust the umbrella rib angle or tension, so as to achieve adaptive adjustment.

[0012] Furthermore, the data acquisition module eliminates position errors and magnetic drift by setting paired magnetic markers and Hall sensors in each umbrella rib and synchronizing with a global time reference. The original magnetic field-position data is mapped one-to-one with the corresponding magnetic markers after time-space alignment, resulting in high-precision aligned magnetic field data.

[0013] Furthermore, the data preprocessing module performs self-calibration based on the alignment magnetic field data: during startup or periodic calibration, the system drives the central push rod to perform a preset calibration action, records the Hall readings during calibration, and generates a rib-sensor geometric mapping table. The mapping table is used to accurately convert the subsequent alignment magnetic field data into rib angles and directly output standardized inputs for attitude recognition.

[0014] Furthermore, after receiving the alignment magnetic field data, the attitude recognition module performs the following hierarchical mapping process:

[0015] Sensor readings are mapped to local joint angles, local joint angles converge to the full umbrella rib configuration, and then the attitude vector is derived from the umbrella rib configuration.

[0016] When the IMU detects a sudden acceleration or rotation rate, the attitude recognition module will use a fusion decision based on a short-time stable window to correct the attitude vector, ensuring the robustness of the attitude output under strong disturbances.

[0017] Furthermore, the adaptive adjustment module implements multiple strategies according to the attitude vector:

[0018] If the attitude deviation is less than the first threshold, a fine-tuning command is output.

[0019] When the attitude deviation is between the first threshold and the second threshold, a damping adjustment command is output and preventive hardening (such as local locking) is enabled.

[0020] If the attitude deviation exceeds the second threshold, a safe retraction or alarm command will be output.

[0021] The threshold can be adaptively adjusted based on historical posture statistics to balance stability and comfort.

[0022] Furthermore, the control execution module includes a motor drive unit and a self-locking worm gear locking unit, and the adjustment command is converted into a screw / linkage action by the motor drive unit to realize the movement of the umbrella ribs;

[0023] Once the target posture is reached and the adjustment is completed, the control execution module achieves mechanical locking without continuous power supply through a self-locking unit to save energy.

[0024] Furthermore, the system also includes:

[0025] The fault diagnosis and safety strategy module is used to detect sensor mismatch or abnormal drift based on continuous alignment magnetic field data and attitude vectors and trigger redundant safety procedures—placing the umbrella in a priority safety state (limit retraction or repeated buffering action) and notifying the user of fault information via Bluetooth or indicator lights.

[0026] Compared with existing technologies, this invention provides an intelligent umbrella attitude recognition and adaptive adjustment control system based on Hall sensor positioning multimodal. By arranging multiple paired Hall sensors on the umbrella body and combining a global time reference, paired magnetic markers, and an IMU triggering mechanism, an end-to-end data chain of "time-space alignment → self-calibration → hierarchical mapping" is formed. This chain can eliminate common sensor positioning errors, magnetic drift, and manufacturing assembly tolerances, ensuring that each Hall reading has a clear and traceable geometric mapping relationship. This allows the original magnetic field readings to be directly converted into local angles of the umbrella ribs and aggregated into the overall umbrella configuration.

[0027] This invention overcomes the limitations of relying solely on a single IMU or isolated Hall readings in terms of spatial resolution and calibration stability, achieving high-resolution attitude recognition at the umbrella rib level. Through initiation / periodic online self-calibration and short-time fusion strategies, the system can maintain positioning accuracy even under long-term use or environmental changes (such as temperature and magnetic environment), reducing the need for manual calibration and improving long-term maintainability. In the event of sudden disturbances, the short-time window fusion correction triggered by the IMU ensures the robustness and continuity of attitude output, avoiding malfunctions or delayed responses caused by misjudgments. Attached Figure Description

[0028] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0029] Figure 1 This is a block diagram of a smart umbrella attitude recognition and adaptive adjustment control system based on Hall sensor positioning multimodal, provided for an embodiment of the present invention. Detailed Implementation

[0030] To enable those skilled in the art to better understand the technical solution of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings.

[0031] As attached Figure 1 As shown:

[0032] Example 1:

[0033] A smart umbrella attitude recognition and adaptive adjustment control system based on Hall sensor positioning multimodal includes:

[0034] The data acquisition module is used to synchronously sample multiple sets of Hall sensors and optional IMUs arranged on the umbrella ribs to obtain raw magnetic field-position data;

[0035] Specifically, the data acquisition module eliminates position errors and magnetic drift by setting paired magnetic markers and Hall sensors in each umbrella rib and synchronizing with a global time reference. The original magnetic field-position data is mapped one-to-one with the corresponding magnetic markers after time-space alignment, resulting in high-precision aligned magnetic field data.

[0036] Furthermore, multi-channel Hall sensors and IMUs are arranged on the umbrella ribs and handle for synchronous sampling to obtain "raw magnetic field-position data" and instantaneous motion information. Possible implementation methods include: installing small Hall elements at two or three locations on each umbrella rib; embedding a 3-axis accelerometer / gyroscope in the handle; the main control MCU providing a unified clock or hardware timestamp (e.g., a 32.768kHz crystal oscillator + timer); the sampling rate being selected from 200–400Hz depending on the application; the sensors being connected to an ADC or digital bus and driven via SPI / I²C or differential lines to ensure noise immunity and electromagnetic compatibility; data being sent to the upper processing unit in a frame structure and written to a circular buffer, supporting local short-term storage and remote debugging and export.

[0037] The data preprocessing module is used to perform time-space alignment and filtering on the original magnetic field-location data to obtain aligned magnetic field data;

[0038] Specifically, the data preprocessing module further performs self-calibration based on the alignment magnetic field data: during startup or periodic calibration, the system drives the central push rod to perform a preset calibration action, records the Hall readings during calibration and generates a rib-sensor geometric mapping table. The mapping table is used to accurately convert the subsequent alignment magnetic field data into rib angles and directly output standardized inputs for attitude recognition.

[0039] Furthermore, the raw readings are time-space aligned and denoised to output aligned magnetic field data. This is achieved through hardware timestamp alignment and inter-channel delay compensation; digital filtering (short-time median despiking + IIR low-pass) to remove impulse noise; compensation for temperature drift and zero bias via self-calibration and periodic correction tables; and the establishment of a mapping table between the Hall channel and the umbrella rib geometric reference points (stored in EEPROM), with interpolation / coordinate transformation performed after each sampling to output aligned data consistent with the geometric model for direct use in attitude recognition.

[0040] The attitude recognition module is used to identify the current umbrella attitude based on the alignment magnetic field data mapped to the umbrella rib geometric model and output the attitude vector.

[0041] Specifically, after receiving the alignment magnetic field data, the attitude recognition module performs a hierarchical mapping process:

[0042] Sensor readings are mapped to local joint angles, local joint angles converge to the full umbrella rib configuration, and then the attitude vector is derived from the umbrella rib configuration.

[0043] When the IMU detects a sudden acceleration or rotation rate, the attitude recognition module will use a fusion decision based on a short-time stable window to correct the attitude vector, ensuring the robustness of the attitude output under strong disturbances.

[0044] Furthermore, the aligned magnetic field data is mapped to local angles of the umbrella ribs, then aggregated into an overall umbrella rib configuration and output as an attitude vector. Specifically, the data is first converted from multiple readings of each rib to local joint angles (angle + confidence level) using a geometric mapping table. Then, these are aggregated into a full umbrella configuration model (including average opening angle, skew, torsion, and local maximum deformation points) according to topological rules. When the IMU reports a sudden change (exceeding the threshold acceleration / angular velocity), a short-time window fusion strategy is used to correct the results with a high-confidence channel, outputting a confidence-based attitude vector for control purposes.

[0045] The adaptive adjustment module is used to generate adjustment commands based on the attitude vector and the preset stabilization strategy;

[0046] Specifically, the adaptive adjustment module implements multiple strategies according to the attitude vector:

[0047] If the attitude deviation is less than the first threshold, a fine-tuning command is output.

[0048] When the attitude deviation is between the first threshold and the second threshold, a damping adjustment command is output and preventive hardening (such as local locking) is enabled.

[0049] If the attitude deviation exceeds the second threshold, a safe retraction or alarm command will be output.

[0050] The threshold can be adaptively adjusted based on historical posture statistics to balance stability and comfort.

[0051] Furthermore, adjustment commands are generated based on attitude vectors and preset strategies. The implementation includes tiered decision logic (fine-tuning / damping adjustment / safe retraction), with thresholds adaptively adjusted based on historical attitude statistics; priority is given to issuing local locking or local tension adjustment commands for local deformation; priority queues and amplitude / rate constraints are used to avoid oscillations; user strategy interfaces (comfort priority / safety priority) are supported to adjust weights, and the results of each execution are written back to the statistics library for online strategy optimization.

[0052] The control execution module is used to convert the adjustment command into the action of the drive motor or locking mechanism to adjust the umbrella rib angle or tension, so as to achieve adaptive adjustment.

[0053] Specifically, the control execution module includes a motor drive unit and a self-locking worm gear locking unit. The adjustment command is converted into a screw / linkage action by the motor drive unit to realize the movement of the umbrella ribs.

[0054] Once the target posture is reached and the adjustment is completed, the control execution module achieves mechanical locking without continuous power supply through a self-locking unit to save energy.

[0055] Furthermore, the adjustment commands are converted into specific drive or mechanical actions, ensuring energy consumption and safety. Possible implementation methods include: a motor drive unit (DC gear / stepper) drives a lead screw or connecting rod to achieve fine-tuning of the umbrella ribs; a locally self-locking worm gear or mechanical pawl achieves zero-power locking after reaching its position; the drive closed loop includes position / current feedback to prevent overload; when sensor mismatch or execution abnormality is detected, redundant safety procedures (limit retraction or buffer action) are triggered and the fault is reported, ensuring safe and stable operation under both execution and power outage conditions.

[0056] Furthermore, the system also includes:

[0057] The fault diagnosis and safety strategy module is used to detect sensor mismatch or abnormal drift based on continuous alignment magnetic field data and attitude vectors and trigger redundant safety procedures—placing the umbrella in a priority safety state (limit retraction or repeated buffering action) and notifying the user of fault information via Bluetooth or indicator lights.

[0058] As shown above, this solution forms an end-to-end data chain of "time-space alignment → self-calibration → hierarchical mapping" by arranging multiple paired Hall sensors on the umbrella body and combining them with a global time reference, paired magnetic markers, and an IMU triggering mechanism. This chain can eliminate common sensor positioning errors, magnetic drift, and manufacturing assembly tolerances, ensuring that each Hall reading has a clear and traceable geometric mapping relationship. This allows the original magnetic field readings to be directly converted into local angles of the umbrella ribs and aggregated into the overall umbrella configuration.

[0059] It overcomes the limitations of relying solely on a single IMU or isolated Hall readings in terms of spatial resolution and calibration stability, achieving high-resolution attitude recognition at the umbrella rib level.

[0060] By employing a strategy of initiating / periodic online self-calibration and short-term fusion, the system can maintain positioning accuracy even under long-term use or environmental changes (such as temperature and magnetic environment), reducing the need for manual calibration and improving long-term maintainability.

[0061] In the event of sudden disturbances, the robustness and continuity of attitude output are ensured by using short-term window fusion correction triggered by the IMU, thus avoiding erroneous actions or delayed responses caused by misjudgment.

[0062] Example 2:

[0063] Smart Umbrella for Urban Pedestrians:

[0064] Objective: Real-time posture recognition and comfort-first adaptive adjustment under everyday wind conditions;

[0065] Overview:

[0066] Data acquisition module: Each of the eight umbrella ribs has a pair of Hall sensors (16 Hall channels in total) near the latch and in the middle section, and a 3-axis IMU (with accelerometer and gyroscope) is built into the handle. Synchronous sampling frequency: 200 Hz (Hall sensors and IMU are synchronized with the same clock). Acquired data: raw magnetic field-position data and instantaneous motion data of the IMU.

[0067] Parameters and acquisition methods: Hall sensor sensitivity is approximately 2 mV / G, range ±1000 G (obtained from component datasheet and factory calibration); IMU device bandwidth is 200 Hz (from device datasheet and confirmed through experimental calibration).

[0068] Data preprocessing module: performs hardware timestamp alignment on the raw channels (based on the 32.768kHz real-time clock of the main control MCU as the global time reference), and performs low-pass filtering (digital IIR, cutoff frequency 25Hz) on the Hall readings to reduce noise, outputting aligned magnetic field data. Simultaneously, it performs a self-calibration once at startup to generate a sensor-umbrella rib mapping table.

[0069] Attitude recognition module: Maps aligned magnetic field data to local joint angles according to umbrella rib hierarchy (two-point readings for each rib), then aggregates them into the full umbrella rib configuration, and finally outputs an attitude vector (including: average opening angle, skew angle, torsion angle, and index of local maximum deformation sites). When the IMU detects a sudden change (acceleration > 2.5 g or angular rate > 200 ° / s), a short-time window fusion judgment is enabled to correct the attitude vector.

[0070] Adaptive adjustment module: Based on the attitude vector and a three-level threshold strategy (parameters below), it determines the adjustment level and generates adjustment commands (fine-tuning / damping adjustment / safe retraction).

[0071] Control execution module: The adjustment command is converted from the drive unit into the motor screw action or triggers the self-locking unit to complete the adjustment of umbrella rib tension or angle and mechanically self-locks after reaching the position to save energy.

[0072] Parameters (all values ​​are obtained from laboratory verification or device datasheets):

[0073] Hall sampling rate: 200 Hz (Acquisition method: MCU timer sampling strategy)

[0074] Low-pass filter cutoff: 25 Hz (selected through spectrum analysis, preserving the gait and wind speed frequency bands).

[0075] Self-calibrated pushrod travel: ±10 mm (determined by mechanical design and executed during calibration).

[0076] Attitude deviation thresholds: First threshold = 3° (fine-tuning), second threshold = 12° (damping / reinforcement), exceeding the second threshold will trigger a safe retraction.

[0077] Threshold acquisition method: determined based on subjective comfort test of 50 subjects and statistical analysis of small sample wind tunnel test.

[0078] Motor specifications: DC geared motor, rated torque 0.6 N·m, no-load speed 60 rpm, peak drive current 1.2 A (calculated and measured based on drive and mechanical load matching).

[0079] Energy consumption: The average energy consumption for a single fine-tuning is 0.3 J, and the average energy consumption for a single damping adjustment is 2.2 J (measurement method: obtained by high-precision current / voltage sampling and integration under actual adjustment action).

[0080] Calibration and data acquisition methods:

[0081] Initial self-calibration process: Upon system power-on for the first time or manual triggering by the user, the central push rod moves the umbrella ribs according to a preset action sequence (1→8), recording the readings of each Hall channel at the known geometric position, and generating an umbrella rib-sensor mapping table (stored in EEPROM). This mapping table is used to convert subsequent magnetic field data into angles. The data is recorded by the main controller and can be exported as a CSV file for verification.

[0082] Runtime data logging: All aligned magnetic field data and attitude vectors are recorded in summary at a rate of 1 Hz (optional full raw stream recording for fault backtracking).

[0083] The experimental results show that this scheme is more effective than ordinary single-IMU threshold control.

[0084] Wind tunnel test conditions: constant gust of 8 m / s (moderate wind) superimposed with random short pulses (peak value 12 m / s).

[0085] Stabilization success rate (maintaining parachute rib angle deviation < 12° before triggering safety retraction): 92% for this scheme; 78% for single IMU threshold control.

[0086] Adjustment time (from sensing effective adjustment to completing mechanical self-locking): 120 ms on average for this scheme; 210 ms for the single IMU scheme.

[0087] User comfort rating (1–5, 5 being the best): This solution averaged 4.3; single IMU 3.8 (blind test with 50 people).

[0088] Example 3:

[0089] Smart windproof umbrellas for outdoor activities / rental:

[0090] Objective: High robustness and safety are prioritized under strong gusts and sudden disturbances;

[0091] Overview:

[0092] Data acquisition module: Each umbrella rib is equipped with a three-point Hall array (root, middle section, and end, totaling 24 channels) and a high-performance IMU (with magnetic field-assisted correction function) inside the handle, with a sampling rate of 400 Hz. This denser sampling supports more precise positioning under strong disturbances. Independent crystal oscillators and time synchronization protocols ensure full-channel clock synchronization.

[0093] The data preprocessing module employs a two-stage filtering process (short-time median filtering to remove spikes + IIR low-pass 40 Hz) and performs online self-calibration correction in real time by comparing it with the mapping table generated at startup (with a cycle of 10 minutes or triggered after each strong disturbance), outputting high-precision aligned magnetic field data.

[0094] Attitude recognition module: hierarchical mapping is local angle → full umbrella configuration → attitude vector; in the event of sudden disturbance (IMU instantaneous threshold), short-time window fusion decision is enabled and a local reinforcement priority strategy is adopted based on the local maximum deformation site.

[0095] Adaptive adjustment module: Three-level strategy, and the second level (damping adjustment) simultaneously triggers a local locking mechanism: the umbrella rib with the most significant deformation is locked first, and then fine-tuning is performed on other umbrella ribs to reduce overall energy consumption and mechanical impact. The threshold is adaptively updated through long-term field testing and rental records.

[0096] Control execution module: Using a larger torque motor (rated 1.5 N·m) and a fast self-locking worm gear, it can complete the partial locking action within 80 ms, and then switch to a low-power self-locking state.

[0097] Fault diagnosis and safety strategy: Add redundant Hall channel cross-validation (if more than 2 channels are mismatched, trigger priority safety state recovery), and report detailed fault logs to the management terminal via Bluetooth.

[0098] HMI module: The rental management APP allows setting two policies, "safety priority" and "comfort priority", and uploading them to the device; the device can visualize its status and push fault alarms in real time.

[0099] Parameter acquisition methods:

[0100] Hall sampling rate: 400 Hz (supported by high-frequency sampling design and MCU clock)

[0101] Two-stage filtering: median window of 5 samples + IIR cutoff of 40 Hz (determined by the spectrum and actual disturbance characteristics);

[0102] Judgment thresholds: Sudden acceleration threshold 3.5 g; Local maximum deformation triggering lock-up threshold 15° (thresholds are a compromise between large-scale wind field tests and safety regulations);

[0103] Mechanical response: Local locking action time 80 ms (actual measurement), complete umbrella reconstruction time < 500 ms (measured under controlled conditions);

[0104] Energy consumption: Approximately 3.8 J for a single partial locking action and approximately 9.6 J for a full umbrella adjustment (energy consumption is measured by current / voltage integration); the system consumes less than 5 mAh per day in standby mode (battery: 3.7 V, 500 mAh).

[0105] Calibration and Data Acquisition:

[0106] Intensive self-calibration: Upon startup and every 10 minutes, the central push rod performs three standard actions within a ±12 mm travel range, recording the output of all Hall effect channels and updating the mapping table. The mapping table contains the reading of each channel at the standard position and the corresponding umbrella rib angle. The mapping table is stored on the device and backed up to the cloud via Bluetooth.

[0107] Fault Log Acquisition: When channel mismatch, reading drift, or repeated strong disturbances are detected, record the raw flow of the entire channel (ring buffer) before and after 5 seconds and upload it for subsequent analysis.

[0108] Experimental results (compared with existing single-sensor / single-threshold systems).

[0109] Test scenario: Simulated real-world coastal storm (peak gust 15 m / s, lasting 10 min), with 20 cycles of testing.

[0110] Results (average):

[0111] Stabilization success rate (without triggering forced recall and maintaining user safety state): 88% for this solution; 61% for single-threshold system.

[0112] Structural damage rate (permanent deformation of umbrella ribs): 1.5% for this scheme; 6.8% for the single-threshold system.

[0113] Average recovery time (from triggering the disturbance to recovering to an available state): 340 ms for this scheme; 720 ms for a single threshold.

[0114] Data source: Prototype test (each indicator was tested alternately under the same umbrella and the same wind conditions, and the average and standard deviation were given for internal recording).

[0115] The comprehensive comparison is shown in Table 1 below (this solution represents the existing single IMU / single threshold control).

[0116] Table 1

[0117] index Existing single IMU / single threshold (baseline) This solution (Example 2 / 3) Improvement or difference explanation Number of sensor channels 1 IMU (+0–2 Hall effect) 16–24 Hall + IMU Higher spatial resolution Posture recognition accuracy (umbrella rib angle RMSE) 4.6° 1.8° (Example 2) / 1.2° (Example 3) Improved precision facilitates fine-tuning and localized reinforcement. Response latency (sensing → adjustment completed) 210 ms 120 ms (Example 2) / 80–340 ms (Example 3) Faster response, Example 3 demonstrates rapid local locking Stabilization success rate (moderate / strong winds) 61% 92% (Example 2, stroke) / 88% (Example 3, strong wind) Significant improvements, but not revolutionary. Energy consumption (a common adjustment) 2.5 J 0.3–2.2 J (Example 2) / 3.8–9.6 J (Example 3) Example 2: Low energy consumption for daily use; Example 3: Higher energy consumption for greater robustness. Structural damage rate (strong winds) 6.8% 1.5% (Example 3) Significantly reduce the probability of damage User comfort rating 3.8 / 5 4.3 / 5 (Example 2) Quantifiable improvement Maintainability / Calibration Manual small-scale calibration Start self-calibration + periodic online self-calibration Reduce daily maintenance burden Fault diagnosis capability Weak (single point) Redundant channel cross-validation + log reporting Greater security and traceability

[0118] The values ​​in the table are based on prototype measurements and large-scale small-sample statistics, with a sample size of 20 to 50.

[0119] As shown above, this solution proposes a hierarchical control strategy based on attitude vectors: attitude deviations are classified according to multiple threshold levels, and combined with historical statistical information and local deformation identification, priority is given to implementing local locking or damping adjustments on the umbrella ribs with the most significant deformation, followed by fine-tuning or safe retraction of the entire system. Simultaneously, the system employs mechanical self-locking to achieve zero-power shaping after the target is in place, forming a closed-loop energy efficiency optimization from control logic to actuators.

[0120] While ensuring user safety, the system achieves faster local response and lower overall energy consumption: through a local priority strategy, the system can solidify the most critical parts first, avoiding large-scale, energy-intensive overall actions.

[0121] The adaptive threshold relies on historical posture statistics, enabling the control strategy to self-adjust according to the usage scenario (such as daily urban use or strong wind coastal scenario), improving the balance between stability and user comfort.

[0122] Compared with traditional protection logic based on fixed thresholds or overall action, this solution has significant advantages in avoiding false triggering, reducing mechanical impact and extending structural life; at the same time, it combines fault diagnosis and redundancy verification to improve the overall safety and traceability of the machine.

[0123] The foregoing has only described certain exemplary embodiments of the present invention by way of illustration. Undoubtedly, those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the foregoing drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

Claims

1. A smart umbrella attitude recognition and adaptive adjustment control system based on Hall sensor positioning multimodal, characterized in that, include: The data acquisition module is used to synchronously sample multiple sets of Hall sensors and optional IMUs arranged on the umbrella ribs to obtain raw magnetic field-position data; The data preprocessing module is used to perform time-space alignment and filtering on the original magnetic field-location data to obtain aligned magnetic field data; The attitude recognition module is used to identify the current umbrella attitude based on the alignment magnetic field data mapped to the umbrella rib geometric model and output the attitude vector. The adaptive adjustment module is used to generate adjustment commands based on the attitude vector and the preset stabilization strategy; The control execution module is used to convert the adjustment command into the action of the drive motor or locking mechanism to adjust the umbrella rib angle or tension, so as to achieve adaptive adjustment.

2. The intelligent umbrella attitude recognition and adaptive adjustment control system based on Hall sensor positioning multimodal as described in claim 1, characterized in that, The data acquisition module eliminates position errors and magnetic drift by setting paired magnetic markers and Hall sensors on each umbrella rib and synchronizing with a global time reference. The original magnetic field-position data is mapped one-to-one with the corresponding magnetic markers after time-space alignment, resulting in high-precision aligned magnetic field data.

3. The intelligent umbrella attitude recognition and adaptive adjustment control system based on Hall sensor positioning multimodal as described in claim 1, characterized in that, The data preprocessing module further performs self-calibration based on the alignment magnetic field data: during startup or periodic calibration, the system drives the central push rod to perform a preset calibration action, records the Hall readings during calibration and generates a rib-sensor geometric mapping table. The mapping table is used to accurately convert the subsequent alignment magnetic field data into rib angles and directly output standardized inputs for attitude recognition.

4. The intelligent umbrella attitude recognition and adaptive adjustment control system based on Hall sensor positioning multimodal as described in claim 1, characterized in that, After receiving the alignment magnetic field data, the attitude recognition module follows a hierarchical mapping process: Sensor readings are mapped to local joint angles, local joint angles converge to the full umbrella rib configuration, and then the attitude vector is derived from the umbrella rib configuration. When the IMU detects a sudden acceleration or rotation rate, the attitude recognition module will use a fusion decision based on a short-time stable window to correct the attitude vector, ensuring the robustness of the attitude output under strong disturbances.

5. The intelligent umbrella attitude recognition and adaptive adjustment control system based on Hall sensor positioning multimodal as described in claim 1, characterized in that, The adaptive adjustment module implements multiple strategies according to the attitude vector: If the attitude deviation is less than the first threshold, a fine-tuning command is output. When the attitude deviation is between the first threshold and the second threshold, a damping adjustment command is output and preventive hardening is enabled; If the attitude deviation exceeds the second threshold, a safe retraction or alarm command will be output. The threshold can be adaptively adjusted based on historical posture statistics to balance stability and comfort.

6. The intelligent umbrella attitude recognition and adaptive adjustment control system based on Hall sensor positioning multimodal as described in claim 1, characterized in that, The control execution module includes a motor drive unit and a self-locking worm gear locking unit. The adjustment command is converted into a screw / linkage action by the motor drive unit to realize the movement of the umbrella ribs. Once the target posture is reached and the adjustment is completed, the control execution module achieves mechanical locking without continuous power supply through a self-locking unit to save energy.

7. The intelligent umbrella attitude recognition and adaptive adjustment control system based on Hall sensor positioning multimodal as described in claim 1, characterized in that, The system also includes: The fault diagnosis and safety strategy module is used to detect sensor mismatch or abnormal drift based on continuous alignment magnetic field data and attitude vectors, and trigger redundant safety procedures to put the umbrella in a priority safety state and notify the user of the fault information via Bluetooth or indicator lights.

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