Magnetic angle intelligent gate opening detection system

Abstract

This invention discloses a magnetic angle intelligent gate opening detection system, belonging to the field of gate opening detection technology. It includes: a data acquisition module configured to acquire a sequence of raw angle register code values ​​reflecting the real-time gate opening at a predetermined sampling frequency using a magnetic angle sensor, and simultaneously acquire ambient temperature, magnetic field strength reference values, and system vibration acceleration values; and a data preprocessing module configured to execute a physical constraint-based multi-parameter cross-verification adaptive filtering algorithm on the raw angle register code value sequence to generate valid sampled code values. This invention, by combining a magnetic angle sensor with a multi-parameter cross-verification adaptive filtering algorithm, effectively solves long-standing technical problems in traditional gate opening detection, such as susceptibility to temperature drift, magnetic field distortion, and vibration interference leading to measurement inaccuracies; loss of data during multi-turn counting; inability to directly read the absolute gate position upon power-up; and poor versatility due to the lack of a unified deceleration mechanism.

Description

Technical Field

[0001] This invention relates to the field of gate opening detection technology, specifically a magnetic angle intelligent gate opening detection system. Background Technology

[0002] In applications such as irrigation districts, river flood control, and reservoir regulation, intelligent gates rely on real-time, continuous, and accurate opening data to perform key functions such as automatic control, flow regulation, and safety limit protection. As a core sensing component of water conservancy automation systems, the gate opening detection device's measurement accuracy, anti-interference capability, and long-term operational reliability directly impact the scheduling efficiency and safety level of the entire water conservancy project. With the continuous advancement of smart water conservancy construction, complex field conditions place higher demands on gate position detection equipment for versatility, maintenance-free operation, and unattended operation.

[0003] Currently, the mainstream gate position detection solutions in the industry mainly include Hall effect sensors and mechanical limit switches, pull-wire resistance gate position gauges, operation timing and motor interruption current judgment methods, multi-turn magnetic angle sensors, traditional magnetic angle detection deceleration mechanisms, and traditional single-turn magnetic angle detection equipment. Among these, Hall effect sensors and mechanical limit switches can only identify two extreme positions: fully open and fully closed. They cannot output continuous opening values ​​for the intermediate stroke, and the sensors are exposed outdoors, making them susceptible to sensor failure or contact breakage due to mud, rain, and impacts. Pull-wire resistance gate position gauges rely on steel wire rope traction to collect displacement data. In outdoor environments, the steel wire rope is prone to corrosion, tangling, and even breakage. Furthermore, long-term friction of the resistive diaphragm can cause resistance drift, and temperature and humidity changes further amplify measurement errors. The operation timing and motor interruption current judgment method relies on the opening and closing time and motor load current to indirectly calculate the gate position. Voltage fluctuations, gate mud and sand obstruction, and changes in water flow resistance can all introduce significant deviations. It lacks a fixed measurement benchmark and can only provide limited information. A rough estimate suggests that while multi-turn magnetic angle sensors can calculate the total stroke by accumulating the number of rotations, strong electromagnetic interference such as frequency converters, high-power motors, and lightning strikes can easily cause data packet loss or missed rotations. Once a missed rotation occurs, the accumulated count becomes completely disordered, and manual recalibration is required. Existing magnetic angle detection deceleration mechanisms lack a unified standard reduction ratio, requiring different gearboxes to be customized for different gate specifications, resulting in a wide variety of accessories with poor versatility. Traditional single-turn magnetic angle detection equipment requires factory-preset lead screw pitch and gate stroke parameters, or manual calculation and input on-site, which has a high debugging threshold. At the same time, it lacks standardized digital signal filtering, temperature drift and magnetic field dynamic compensation, and software limit and other supporting signal processing algorithms.

[0004] The common problem with existing technologies is that none of the traditional detection methods can simultaneously meet the comprehensive requirements of absolute positioning, strong anti-interference, no reset required, universal adaptability, no parameter input required, and long lifespan. Specifically, these shortcomings include: susceptibility to damage, lack of continuous opening output, easy loss of counts in multi-turn counting, permanent inaccuracy after electromagnetic interference, inability to directly read the actual gate position upon power-up, lack of universal standards for deceleration mechanisms, the need for customized deceleration boxes for different gates, the requirement to pre-enter the mechanical parameters of the lead screw and gate, and the lack of digital signal processing and two-point self-calibration conversion algorithms. These deficiencies result in numerous measurement uncertainties in the complex field conditions of hydraulic engineering, making it difficult to meet the practical requirements of automated, precise control and long-term stable unattended operation. Summary of the Invention

[0005] Based on this, the purpose of this invention is to provide a magnetic angle intelligent gate opening detection system to solve the technical problems of existing detection methods that cannot simultaneously meet the comprehensive use requirements of absolute positioning, strong anti-interference, no reset, universal adaptability, no parameter input, and long life. These problems result in inaccurate measurements due to temperature drift, magnetic field distortion, and vibration interference. Multi-turn counting is prone to data loss and cannot directly read the actual gate position upon power-up. Furthermore, the deceleration mechanism lacks a unified standard, has a high debugging threshold, and lacks systematic digital signal processing and self-calibration algorithm support.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a data acquisition module configured to acquire the original angle register code value sequence reflecting the real-time opening degree of the gate through a magnetic angle sensor at a predetermined sampling frequency, and simultaneously acquire ambient temperature value, magnetic field strength reference value and system vibration acceleration value;

[0007] The data preprocessing module is configured to perform a physically constrained multi-parameter cross-verification adaptive filtering algorithm on the original angle register code value sequence to generate valid sampled code values. The multi-parameter cross-verification adaptive filtering algorithm specifically includes:

[0008] A state parameter vector is constructed. Based on the gate kinematic model, the original angle register code value, ambient temperature value, magnetic field strength reference value and system vibration acceleration value at the current moment are mapped to a unified evaluation dimension to establish the gate opening state space equation.

[0009] Set physical constraints for data validity, and construct multidimensional confidence interval boundaries based on the maximum physical movable angle change of the magnetic angle sensor within a unit time window, the maximum magnetic field drift rate caused by temperature change, and the vibration noise amplitude threshold.

[0010] Within the sliding sampling window, a temperature drift compensation coefficient based on ambient temperature is introduced for the multiple sets of original register code values ​​currently cached to perform a first-level correction, resulting in a corrected code value sequence; simultaneously, a dynamic distortion suppression coefficient based on magnetic field strength reference value is introduced for a second-level correction, resulting in a compensated code value sequence; and an inertial filtering weight based on system vibration acceleration value is introduced to dynamically suppress outlier code values ​​that exceed the boundaries of the multidimensional confidence interval.

[0011] By using least squares joint estimation, the modified code value sequence, the compensated code value sequence, and the code value sequence after inertial filtering are cross-verified to find the optimal estimated code value that minimizes the sum of squares of the multi-source parameter residuals. This estimated code value is then output as the effective sampled code value. The weight coefficients of each parameter residual are adjusted according to the system vibration acceleration value at the current moment.

[0012] The operation calibration module is configured to combine the valid sampled code value with the pre-stored gate fully closed reference code value. With fully open reference code value The system calculates and outputs the real-time percentage opening and real-time lifting height of the gate based on the linear conversion relationship.

[0013] The present invention further specifies that the specific expression for constructing the state parameter vector is:

[0014] set up The state parameter vector at time t is ,in This represents the mechanical angle value corresponding to the raw angle register code value read by the magnetic angle sensor at the k-th sampling time. Let K be the ambient temperature value at the k-th sampling time. Let be the reference value of the magnetic field strength at the k-th sampling time. This represents the system vibration acceleration value at the k-th sampling time.

[0015] The constructed state-space equations are as follows:

[0016] ;

[0017] ;

[0018] in, Here is the state transition matrix. For the observation matrix, and These are process noise and observation noise, respectively. and The covariance matrix is ​​based on the current The value is adjusted online in real time. For the system in the first The actual external observation data measured at any given time. For the system in the first The estimated state at any given time.

[0019] The present invention further specifies that the boundary of the multidimensional confidence interval is determined as follows:

[0020] Based on the maximum allowable movement speed of the gate and sampling period Determine the upper confidence limit of the change in angle code value. ,in The mechanical angle equivalent for each code value;

[0021] Temperature drift coefficient obtained from calibration experiments Determine the temperature compensation range ,in For reference calibration temperature;

[0022] Based on magnetic field distortion function Determine the magnetic field fault tolerance range ;

[0023] By taking the intersection of the above three factors within a sliding window, a multidimensional confidence interval boundary is constructed.

[0024] The present invention further specifies that the temperature drift compensation coefficient is calculated as follows:

[0025] ;

[0026] in This is the temperature drift compensation coefficient. This is the gain coefficient for temperature drift response;

[0027] The dynamic distortion suppression coefficient is calculated as follows:

[0028] ;

[0029] in This is the dynamic distortion suppression coefficient. This is the magnetic field base reference value stored during the system's power-on self-test;

[0030] The inertial filter weights are calculated as follows:

[0031] ;

[0032] in For inertial filter weights, The vibration attenuation factor is preset based on the mechanical characteristics of the gate during the on-site commissioning phase.

[0033] The present invention further specifies that the objective function of the least squares joint estimation is:

[0034] ;

[0035] in, Let be the independent variable to be optimized in the objective function. The code value after temperature drift compensation. The code value after magnetic field distortion suppression. The code value after inertial filtering; weighting coefficients. , , The sum equals one.

[0036] The specific values ​​for the weighting coefficients are:

[0037] ;

[0038] ;

[0039] ;

[0040] After normalization, the result is input into the objective function, where, , , This is the preset scaling factor.

[0041] The present invention is further configured such that, in the operation calibration module, the real-time percentage opening degree... The calculation formula is:

[0042] ;

[0043] in , which is the total code difference for the entire journey;

[0044] Real-time lifting height The calculation formula is:

[0045] ;

[0046] in, This refers to the maximum total travel of the gate as recorded on-site.

[0047] The present invention is further configured such that the multi-point calibration redundancy verification module is configured to repeatedly perform the fully closed and fully open position calibration operations multiple times during the gate debugging phase, and to verify the multiple sets of data obtained. and The reference code value is subjected to variance analysis, and abnormal calibration values ​​with deviations exceeding a preset threshold are removed. The arithmetic mean of the remaining valid calibration values ​​is used as the final reference code value and stored in non-volatile memory.

[0048] The present invention is further configured such that the self-diagnosis module for running status is configured to monitor the residual sequence of each parameter of the multi-parameter cross-verification adaptive filtering algorithm in the real-time monitoring data preprocessing module. When the residual of a certain parameter channel continuously exceeds the preset alarm threshold, it is determined that the physical parameter acquisition channel has failed. The system automatically cuts off the weight contribution of the channel and outputs the corresponding fault alarm code, so as to enable the detection system to continue to operate under the condition of partial sensor failure and degradation.

[0049] In summary, the present invention has the following main advantages:

[0050] This invention effectively solves long-standing technical problems in traditional gate opening detection, such as measurement inaccuracies caused by temperature drift, magnetic field distortion, and vibration interference, loss of counts in multi-turn counting, inability to directly read the absolute gate position upon power-up, and poor versatility due to the lack of a unified deceleration mechanism. It achieves continuous opening detection with strong anti-interference, no reset required, and absolute positioning. At the same time, combined with multi-point calibration redundancy verification and self-diagnosis of operating status, it ensures long-term stable operation of the system under complex field conditions, without relying on manual periodic calibration or parameter input, which greatly reduces on-site debugging and maintenance costs and significantly improves the reliability, versatility, and unattended adaptability of water conservancy gate automation control. Attached Figure Description

[0051] Figure 1 This is the control flowchart of the present invention. Detailed Implementation

[0052] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention.

[0053] The embodiments of the present invention will now be described.

[0054] A magnetic angle intelligent gate opening detection system, such as Figure 1As shown, the system includes a data acquisition module, a data preprocessing module, and a computational calibration module. The data acquisition module uses a magnetic angle sensor to acquire the raw angle register code value sequence reflecting the real-time opening of the gate at a predetermined sampling frequency, and simultaneously acquires ambient temperature, magnetic field strength reference values, and system vibration acceleration values. In this embodiment, the magnetic angle sensor uses a 12-bit angle register, evenly dividing the 360° circumference into 4096 digital divisions, each digital division corresponding to a raw code value, with a code value range of 0~4095. The sampling frequency is set to 10Hz, meaning data acquisition is completed once every 100ms. The ambient temperature value is acquired using a thermistor integrated on the sensor PCB, with a measurement range of -40℃ to +85℃ and a measurement accuracy of ±0.5℃. The magnetic field strength reference value is obtained through the magnetic field strength detection channel of a Hall chip, with the unit being Gauss (Gs). The system vibration acceleration value is acquired using a triaxial MEMS accelerometer, with the unit being gravitational acceleration g.

[0055] After the data acquisition module outputs the raw measured values ​​of each physical quantity, they are sent to the data preprocessing module to execute a multi-parameter cross-verification adaptive filtering algorithm based on physical constraints to generate valid sampled code values. The calculation and calibration module then uses these valid sampled code values, combined with the pre-stored gate full-closure reference code value, to perform the calculation. With fully open reference code value The system calculates and outputs the real-time percentage opening and real-time lifting height of the gate based on the linear conversion relationship.

[0056] Multi-parameter cross-validation adaptive filtering algorithm.

[0057] The state parameter vector is constructed based on the gate kinematic model. The original angle register code value, ambient temperature value, magnetic field strength reference value and system vibration acceleration value at the current moment are mapped to a unified evaluation dimension to establish the gate opening state space equation.

[0058] set up The state parameter vector at time t is ,in This represents the mechanical angle value corresponding to the raw angle register code value read by the magnetic angle sensor at the k-th sampling time, in degrees (°). Its conversion method is as follows: ,in This is the raw digital code value directly output by the sensor's 12-bit register, with a value range of 0 to 4095.

[0059] The ambient temperature value at the k-th sampling time is expressed in degrees Celsius (°C) and is read by a thermistor after analog-to-digital conversion.

[0060] The reference value of the magnetic field strength at the k-th sampling time is expressed in Gaussians (Gs) and is output by the magnetic field strength detection channel of the Hall chip.

[0061] This represents the system vibration acceleration value at the k-th sampling time, expressed in gravitational acceleration g, obtained by synthesizing triaxial components from a triaxial MEMS accelerometer. .

[0062] The constructed state-space equations are as follows:

[0063] ;

[0064] ;

[0065] in, For the system in the first The actual external observation data measured at any given time. Each component represents the raw measured value directly acquired by the sensor, and the superscript (meas) indicates the measured value.

[0066] For the system in the first The predicted state at time (i.e., the next sampling time), with the subscript indicating the time series.

[0067] Let be the state transition matrix, a 4×4 matrix, describing the change of the system state over time. Based on the kinematic characteristics of the gate, we define:

[0068] ;

[0069] That is, the use of an identity matrix indicates that the changes in each physical parameter are small within adjacent sampling intervals, which is consistent with the actual working conditions of the gate hoist operating at low speed and smoothly.

[0070] The observation matrix is ​​a 4×4 identity matrix, representing the direct correspondence between the components of the observation vector and the state vector.

[0071] Let k be the process noise vector at time k. Each component is independent and follows a normal distribution with a mean of zero. Specifically, ,in Let V be the variance of the angle process noise, and take a value of [value]. ; , Let V be the variance of the temperature process noise, and take a value of [value]. , , Let V be the variance of the magnetic field process noise, and take a value of [value]. , ,in Let be the variance of the vibration process noise, with a value of (0.01g)².

[0072] Let k be the observation noise vector at time k. Each component is independent and follows a normal distribution with a mean of zero. Specifically, ,in The variance of the angular observation noise is taken as a value. , ,in The variance of temperature observation noise is given by a value of [value]. , ,in The variance of magnetic field observation noise is denoted as , and its value is . , , The variance of vibration observation noise is taken as a value. .

[0073] and The covariance matrix is ​​based on the current The value is adjusted online in real time; specifically, the process noise covariance matrix... The angle-related components are adjusted to ,in The static process noise variance is taken as (0.05°)². This is the vibration amplification factor, with a value of 0.5. The unit is g; the observation noise covariance matrix The angle-related components are adjusted to ,in The static observation noise variance is taken as (0.1°)². The vibration deterioration coefficient is 0.3.

[0074] Set physical constraints for data validity.

[0075] Based on the maximum physical movable angle change of the magnetic angle sensor within a unit time window, the maximum magnetic field drift rate caused by temperature change, and the vibration noise amplitude threshold, a multidimensional confidence interval boundary is constructed.

[0076] In a specific embodiment of the present invention, the sampling period The maximum permissible movement speed of the gate is set to 0.1s (i.e., 100ms). Due to the mechanical characteristics of the gate on site, this embodiment takes... The gate screw pitch (in this embodiment, a small channel gate is used, with a screw pitch of 2mm / turn, i.e., 0.002m / turn) and a 256:1 reduction ratio are converted to the magnetic angle sensor. Based on the design of the 256:1 standard reduction gearbox of this invention, a full stroke of 1.0m for this small gate corresponds to a 135° rotation of the magnet, meaning that each meter corresponds to an equivalent angle of... Considering the actual minute movements, the cumulative change over multiple periods can be used for determination in applications.

[0077] Temperature drift coefficient obtained from calibration experiments The unit is Through calibration experiments using high and low temperature chambers, the values ​​used in this embodiment were determined. Reference calibration temperature The factory reference temperature is set to 25℃. The temperature compensation range is: .

[0078] Based on magnetic field distortion function The magnetic field tolerance range is determined. The magnetic field distortion function is obtained through experimental calibration; a linear model is used in this embodiment. ,in This is the magnetic field distortion coefficient, measured in degrees per gauss (°C / Gauss). Through experimental determination, the sample taken in this embodiment... , This is the reference value of the magnetic field base stored during the system's power-on self-test, in Gaussian (Gs). In this embodiment... .

[0079] The intersection of the angle change confidence interval, temperature compensation interval, and magnetic field fault tolerance interval within a sliding window is used to construct the boundary of the multidimensional confidence interval.

[0080] Multi-level correction and dynamic suppression.

[0081] Within the sliding sampling window (in this embodiment, the sliding window width is 5 sampling points), the multiple sets of original register code values ​​currently cached are corrected by introducing a temperature drift compensation coefficient based on the ambient temperature value to obtain the corrected code value sequence.

[0082] Temperature drift compensation coefficient The calculation method is as follows:

[0083] ;

[0084] in This is the temperature drift response gain coefficient, in units of... In this embodiment, ; The measured temperature value at the current moment, in °C;

[0085] The code value after first-level correction (temperature drift compensation) is The first item is to convert the original code value into a mechanical angle value, and the second item is the temperature drift compensation amount, in degrees (°).

[0086] A second-level correction is performed using a dynamic distortion suppression coefficient based on the magnetic field strength reference value. Dynamic distortion suppression coefficient The calculation method is as follows:

[0087] ;

[0088] in This is the dynamic distortion suppression coefficient. This is the magnetic field base reference value stored during the system's power-on self-test. The measured magnetic field strength value at the current moment is expressed in Gs.

[0089] The code value after secondary correction (magnetic field compensation) is The unit is degrees.

[0090] An inertial filter weight based on the system vibration acceleration value is introduced to dynamically suppress outlier code values ​​that exceed the boundaries of the multidimensional confidence interval. Inertial filter weight The calculation method is as follows:

[0091] ;

[0092] in The vibration attenuation factor is preset based on the gate's mechanical characteristics during the on-site commissioning phase. In this embodiment, it is taken as... =2 (dimensionless).

[0093] The code value after inertial filtering is The unit is degrees.

[0094] Least squares joint estimation cross-validation.

[0095] By using least squares joint estimation, the modified code value sequence, the compensated code value sequence, and the code value sequence after inertial filtering are cross-verified to find the optimal estimated code value that minimizes the sum of squared residuals of the multi-source parameters.

[0096] The objective function is:

[0097] ;

[0098] in, , where is the independent variable to be optimized in the objective function, and the unit is degrees (°), representing the angle value corresponding to the candidate code value used to participate in the cross-validation calculation under the boundary constraints of the multidimensional confidence interval within the current sliding window; The angle value corresponding to the optimal estimated code value that minimizes the objective function is expressed in degrees (°), which is the final effective sampled value output. This is the angle value (°) corresponding to the code value after temperature drift compensation. This represents the angle value (°) corresponding to the code value after magnetic field distortion suppression. This represents the angle value (°) corresponding to the code value after inertial filtering.

[0099] Weighting coefficient , , The system is pre-set manually to ensure that the sum is one.

[0100] The specific values ​​for the weighting coefficients are:

[0101] ;

[0102] ;

[0103] ;

[0104] The units and meanings of each coefficient are as follows:

[0105] This is the temperature influence scaling factor, with units of 1 / ℃, reflecting the degree of influence of temperature deviation on the weight. In this embodiment, α = 0.02 / ℃ is used.

[0106] This is the current measured temperature value, in °C.

[0107] For reference calibration, 25℃ is used;

[0108] This is the magnetic field influence scaling factor, with units of 1 / Gs, reflecting the degree of influence of magnetic field deviation on the weight. In this embodiment, β = 0.01 / Gs is used.

[0109] This is the current measured magnetic field strength value, in Gs;

[0110] The baseline value for the magnetic field is taken as 100 Gs;

[0111] This is the vibration influence scaling factor, with units of 1 / g², reflecting the degree of influence of vibration acceleration on the weight. In this embodiment, it is taken as... ;

[0112] After calculating the original weight values, normalization processing is required, that is:

[0113] ;

[0114] ;

[0115] ;

[0116] Will , , Substitute into the objective function for replacement , , .

[0117] Multi-point calibration redundancy check.

[0118] The system also includes a multi-point calibration redundancy verification module. During the gate commissioning phase, the fully closed and fully open position calibration operations are repeatedly performed. Specifically, when the gate is fully closed, sensor register code values ​​are continuously collected 10 times to form the first dataset; when the gate is fully open, sensor register code values ​​are also continuously collected 10 times to form the second dataset. Variance analysis is performed on the acquired Data_Close and Data_Open reference code values ​​to calculate the standard deviation of each set of data. When a calibration value deviates from the arithmetic mean of the data set by more than a preset threshold (in this embodiment, the threshold is set to ±3 code values), the calibration value is determined to be an outlier and is discarded. The arithmetic mean of the remaining valid calibration values ​​is stored as the final reference code value in non-volatile memory (Flash) to ensure the statistical reliability and repeatability of the calibration reference.

[0119] Opening conversion and height output.

[0120] In the operation calibration module, the real-time percentage opening degree The calculation formula is:

[0121] ;

[0122] in , which is the total code difference corresponding to the entire journey, and is dimensionless;

[0123] The reference code value stored when the gate is fully closed is dimensionless and ranges from 0 to 4095.

[0124] The reference code value stored is dimensionless and ranges from 0 to 4095 when the gate is fully open.

[0125] This represents the real-time percentage opening, expressed in percent.

[0126] Real-time lifting height The calculation formula is:

[0127] ;

[0128] in, This is the maximum total stroke of the gate recorded on-site, in millimeters (mm), and is input by the on-site operator based on the actual mechanical limit of the gate.

[0129] The real-time lifting height is shown in millimeters (mm).

[0130] Self-diagnosis of operational status.

[0131] The system also includes a self-diagnostic module for operational status. This module monitors the residual sequence of each parameter in the multi-parameter cross-validation adaptive filtering algorithm in the data preprocessing module in real time. Specifically, it defines the angle residual. Temperature residual Magnetic field residual When the residual of a certain channel exceeds the preset alarm threshold for 10 consecutive sampling periods (angle residual threshold set to 5 codes, temperature residual threshold set to 5℃, magnetic field residual threshold set to 10Gs), the physical parameter acquisition channel is determined to have failed. At this time, the system automatically removes the weight contribution of this channel in the least squares joint estimation (i.e., sets the corresponding weight coefficient of this channel to 0 and renormalizes the weights of other channels), and simultaneously outputs the corresponding fault alarm code through the RS485 communication interface (e.g., fault code 0x01 indicates a temperature channel fault, 0x02 indicates a magnetic field channel fault, and 0x04 indicates a vibration channel fault). This allows the detection system to continue operating even with partial sensor failure and degradation, preventing the entire system from being paralyzed due to a single sensor failure.

[0132] The steps of the methods or algorithms described in this application can be directly embedded in hardware, software units executed by a processor, or a combination of both. Exemplarily, a storage medium can be connected to a processor so that the processor can read information from and write information to the storage medium. Optionally, the storage medium can also be integrated into the processor. Optionally, the processor and the storage medium can also be located in different components within a terminal. These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable apparatus for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0133] Although this application has been described in conjunction with specific features and embodiments, it is obvious that various modifications and combinations can be made thereto without departing from the spirit and scope of this application. Accordingly, this specification and drawings are merely illustrative examples of this application and are considered to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Clearly, those skilled in the art can make various alterations and modifications to this application without departing from its scope. Thus, if such modifications and modifications fall within the scope of this application and its equivalents, this application intends to include such modifications and modifications.

Claims

1. A magnetic angle intelligent gate opening detection system, characterized in that: The system includes: The data acquisition module is configured to acquire the sequence of raw angle register code values ​​reflecting the real-time opening of the gate through a magnetic angle sensor at a predetermined sampling frequency, and simultaneously acquire ambient temperature value, magnetic field strength reference value and system vibration acceleration value. The data preprocessing module is configured to perform a physical constraint-based multi-parameter cross-verification adaptive filtering algorithm on the original angle register code value sequence to generate valid sampled code values. The multi-parameter cross-verification adaptive filtering algorithm specifically includes: A state parameter vector is constructed, and based on the gate kinematic model, the original angle register code value, the ambient temperature value, the magnetic field strength reference value, and the system vibration acceleration value at the current moment are mapped to a unified evaluation dimension to establish the gate opening state space equation. Set physical constraints for data validity, and construct multidimensional confidence interval boundaries based on the maximum physical movable angle change of the magnetic angle sensor within a unit time window, the maximum magnetic field drift rate caused by temperature change, and the vibration noise amplitude threshold. Within the sliding sampling window, for the multiple sets of original register code values ​​currently cached, a temperature drift compensation coefficient based on the ambient temperature value is introduced for first-level correction to obtain a corrected code value sequence; simultaneously, a dynamic distortion suppression coefficient based on the magnetic field strength reference value is introduced for second-level correction to obtain a compensated code value sequence; and an inertial filtering weight based on the system vibration acceleration value is introduced to dynamically suppress outlier code values ​​that exceed the boundaries of the multidimensional confidence interval. By using least squares joint estimation, the corrected code value sequence, the compensated code value sequence, and the code value sequence after inertial filtering are cross-verified to find the optimal estimated code value that minimizes the sum of squares of the multi-source parameter residuals. This estimated code value is then output as the effective sampled code value. The weighting coefficients of each parameter residual are adjusted according to the system vibration acceleration value at the current moment. The operation calibration module is configured to combine the valid sampled code value with the pre-stored gate fully closed reference code value. With fully open reference code value The system calculates and outputs the real-time percentage opening and real-time lifting height of the gate based on the linear conversion relationship.

2. The magnetic angle intelligent gate opening detection system according to claim 1, characterized in that: The specific expression for constructing the state parameter vector is as follows: set up The state parameter vector at time t is ,in This represents the mechanical angle value corresponding to the raw angle register code value read by the magnetic angle sensor at the k-th sampling time. Let K be the ambient temperature value at the k-th sampling time. Let be the reference value of the magnetic field strength at the k-th sampling time. This represents the system vibration acceleration value at the k-th sampling time. The constructed state-space equations are as follows: ； ； in, Here is the state transition matrix. For the observation matrix, and These are process noise and observation noise, respectively. and The covariance matrix is ​​based on the current The value is adjusted online in real time. For the system in the first The actual external observation data measured at any given time. For the system in the first The estimated state at any given time.

3. The magnetic angle intelligent gate opening detection system according to claim 2, characterized in that: The method for determining the boundaries of the multidimensional confidence interval is as follows: Based on the maximum allowable movement speed of the gate and sampling period Determine the upper confidence limit of the change in angle code value. ,in The mechanical angle equivalent for each code value; Temperature drift coefficient obtained from calibration experiments Determine the temperature compensation range ,in For reference calibration temperature; Based on magnetic field distortion function Determine the magnetic field fault tolerance range ; The intersection of the above three factors within a sliding window is used to construct the boundary of the multidimensional confidence interval.

4. The magnetic angle intelligent gate opening detection system according to claim 3, characterized in that: The temperature drift compensation coefficient is calculated as follows: ； in This is the temperature drift compensation coefficient. This is the gain coefficient for temperature drift response; The dynamic distortion suppression coefficient is calculated as follows: ； in This is the dynamic distortion suppression coefficient. This is the magnetic field base reference value stored during the system's power-on self-test; The inertial filter weights are calculated as follows: ； in For inertial filter weights, The vibration attenuation factor is preset based on the mechanical characteristics of the gate during the on-site commissioning phase.

5. The magnetic angle intelligent gate opening detection system according to claim 4, characterized in that: The objective function of the least squares joint estimation is: ； in, Let be the independent variable to be optimized in the objective function. The code value after temperature drift compensation. The code value after magnetic field distortion suppression. The code value after inertial filtering; weighting coefficients. , , The sum equals one. The specific values ​​for the weighting coefficients are: ； ； ； After normalization, the result is input into the objective function, where, , , This is the preset scaling factor.

6. The magnetic angle intelligent gate opening detection system according to claim 5, characterized in that: In the computational calibration module, the real-time percentage opening... The calculation formula is: ； in , which is the total code difference for the entire journey; The real-time lifting height The calculation formula is: ； in, This refers to the maximum total travel of the gate as recorded on-site.

7. The magnetic angle intelligent gate opening detection system according to claim 6, characterized in that: The system also includes: The multi-point calibration redundancy verification module is configured to repeatedly perform the fully closed and fully open position calibration operations multiple times during the gate commissioning phase, and to verify the multiple sets of data obtained. and The reference code value is subjected to variance analysis, and abnormal calibration values ​​with deviations exceeding a preset threshold are removed. The arithmetic mean of the remaining valid calibration values ​​is used as the final reference code value and stored in non-volatile memory.

8. The magnetic angle intelligent gate opening detection system according to claim 7, characterized in that: The system also includes: The self-diagnostic module for operation status is configured to monitor the residual sequence of each parameter of the multi-parameter cross-verification adaptive filtering algorithm in the data preprocessing module in real time. When the residual of a certain parameter channel continuously exceeds the preset alarm threshold, it is determined that the physical parameter acquisition channel has failed. The system automatically cuts off the weight contribution of the channel and outputs the corresponding fault alarm code, so as to enable the detection system to continue to operate under the condition of partial sensor failure and degradation.

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