Safety belt anti-falling protection monitoring method, system and equipment based on sudden change of annular magnetic field and medium

Abstract

The invention discloses a safety belt anti-falling protection monitoring method, system and device based on sudden change of an annular magnetic field and a medium, and belongs to the technical field of safety belt anti-falling protection monitoring. Arranging a functional unit on the hook ring, and applying a first excitation signal to measure and obtain a first magnetic field characteristic value; establishing a second mathematical model containing a hook ring geometric structure and ferromagnetic medium physical attributes, calculating second magnetic field characteristic values under different ferromagnetic medium invasion conditions, and determining a first threshold set; and applying a second excitation signal to periodically measure to obtain a third magnetic field characteristic value, calculating a first deviation characteristic quantity, and comparing the first deviation characteristic quantity with the first threshold set to judge the state of the hook. According to the method, the regression relation between the magnetic field change multiple and the fixed object parameters is established, the differential judgment threshold value of the stable contact working condition and the non-stable contact working condition is determined, transient interference is filtered out through frequency statistics, and accurate judgment and graded alarm of the hook ring contact state are achieved.

Description

Technical Field

[0001] This invention relates to the field of safety belt fall protection monitoring technology, specifically to a safety belt fall protection monitoring method, system, equipment, and medium based on a sudden change in the annular magnetic field. Background Technology

[0002] A reliable connection between the safety belt hook and the fixed object is crucial for fall protection during high-altitude operations. As an open-loop structure, detecting the closed state of the hook presents unique technical challenges.

[0003] Visual inspection relies on manual judgment and cannot accurately identify whether the hook ring is fully closed in high-altitude, long-distance environments. Mechanical triggering devices detect closure through contact switches, but the space at the hook ring opening is small, and mechanical switches are susceptible to wear and poor contact. Furthermore, they cannot detect whether the fixed object has actually passed through the center area of ​​the hook ring.

[0004] When a fixed object passes through a hook ring, there are two states: stable passage and unstable passage. Neither visual inspection nor mechanical triggering methods can utilize this physical characteristic of the hook ring's magnetic circuit, making it impossible to distinguish between stable and unstable passage states. Consequently, the risk of a fall during an unstable passage state cannot be predicted. Summary of the Invention

[0005] In view of the above-mentioned problems, the present invention provides a method, system, device and medium for monitoring safety belt fall protection based on abrupt changes in the annular magnetic field.

[0006] Therefore, the technical problem solved by this invention is: how to establish an electromagnetic coupling model between the hook ring excitation unit and the detection unit to determine the excitation parameters, how to determine the differential judgment threshold between stable contact and unstable contact conditions by numerically simulating the magnetic field response inside the hook ring under different intrusion conditions of ferromagnetic fixed objects, and how to achieve graded judgment and alarm of contact state by comparing the real-time measurement of the hook ring magnetic field with the judgment threshold.

[0007] To solve the above-mentioned technical problems, the present invention provides the following technical solution: a safety belt fall protection monitoring method based on abrupt changes in a circular magnetic field, comprising, A first mathematical model is established to characterize the electromagnetic coupling relationship between the excitation unit and the detection unit, and a first set of parameters is determined through the first mathematical model. A first functional unit and a second functional unit are provided in the hook ring. A first excitation signal is applied to the first functional unit according to the first parameter set. Under the first working condition, the first magnetic field characteristic value is obtained by measuring through the second functional unit. A second mathematical model is established, which includes the geometric structure of the hook ring and the physical properties of the ferromagnetic medium. The second magnetic field feature value at the detection location is calculated using the second mathematical model under different ferromagnetic medium intrusion conditions. The second magnetic field feature value is compared with the first magnetic field feature value to obtain a first correlation rule. A first threshold set is determined based on the first correlation rule. A second excitation signal is applied to the first functional unit, and a third magnetic field characteristic value is obtained by periodic measurement through the second functional unit. A first deviation characteristic quantity between the third magnetic field characteristic value and the first magnetic field characteristic value is calculated, and the first deviation characteristic quantity is compared with the first threshold set to determine the hooking state.

[0008] As a preferred embodiment of the safety belt fall protection monitoring method based on a sudden change in the ring magnetic field described in this invention, wherein: a first mathematical relationship is established between the excitation unit current and the magnetic field strength inside the hook ring; Establish a second mathematical relationship between the magnetic field strength inside the hook ring and the magnetic field induced by the detection probe; Establish a third mathematical relationship between the magnetic field induced by the detection probe and the electrical signal output by the detection unit; The first mathematical model is formed by cascading and coupling the first mathematical relation, the second mathematical relation, and the third mathematical relation.

[0009] As a preferred embodiment of the safety belt fall protection monitoring method based on a sudden change in the annular magnetic field described in this invention, wherein: determining the first parameter set through the first mathematical model includes: taking the excitation unit structural parameters and excitation signal parameters as parameters to be analyzed, and calculating the dual-state response characteristics of the parameters to be analyzed under no-load and ferromagnetic medium intrusion states through the first mathematical model; Sensitivity quantification analysis is performed on the dual-state response characteristics to obtain the sensitivity value of the parameter to be analyzed; The parameters are ranked according to their sensitivity values, and those whose sensitivity values ​​exceed a preset threshold are selected as parameters to be optimized. Establish no-load detectable constraints and intrusion distinguishable constraints for the parameters to be optimized, and perform multi-parameter joint optimization under the constraints to obtain the parameter feasible region; The range of values ​​for the parameter to be optimized is extracted from the feasible domain of the parameter and used as the first parameter set.

[0010] As a preferred embodiment of the safety belt fall protection monitoring method based on abrupt change in the annular magnetic field described in this invention, wherein: determining the first threshold set according to the first correlation law includes: numerically solving multiple sets of values ​​for the relative permeability and spatial position of the ferromagnetic medium to obtain a magnetic field strength simulation dataset; The magnetic field strength simulation dataset is divided into stable contact condition data and unstable contact condition data based on spatial location. Calculate the ratio of the two operating condition data to the first magnetic field characteristic value to obtain the change factor data of the stable contact condition and the change factor data of the unstable contact condition, respectively; A regression relationship between the aforementioned change factor data and relative permeability and spatial location is established. Based on the regression relationship and the change factor data, the threshold values ​​for determining stable contact conditions and unstable contact conditions are determined as the first set of threshold values.

[0011] As a preferred embodiment of the safety belt fall protection monitoring method based on abrupt changes in the annular magnetic field described in this invention, wherein: determining the stable contact condition judgment threshold and the unstable contact condition judgment threshold according to the regression relationship and the change factor data includes: performing statistical analysis on the stable contact condition change factor data and the unstable contact condition change factor data respectively to obtain distribution characteristic parameters; The safety margin coefficients are determined based on the regression relationship and the distribution characteristic parameters, respectively. Extract the extreme values ​​from the change factor data respectively; The decision threshold is obtained by applying a safety margin coefficient to the extreme values.

[0012] As a preferred embodiment of the safety belt fall protection monitoring method based on abrupt changes in the annular magnetic field described in this invention, the periodic measurement to obtain the third magnetic field characteristic value includes: triggering magnetic field measurement according to a preset time interval; Measurement signals are transmitted via impedance matching. Perform digital filtering on the transmitted signal; The amplitude of the filtered signal is extracted as the third magnetic field characteristic value.

[0013] As a preferred embodiment of the safety belt fall protection monitoring method based on abrupt change in the annular magnetic field described in this invention, the determination of the hook state includes: comparing the first deviation feature with the stable contact condition determination threshold and the unstable contact condition determination threshold in three ways; when the first deviation feature is greater than or equal to the stable contact condition determination threshold, it is marked as a first state level; when the first deviation feature is less than the stable contact condition determination threshold but greater than or equal to the unstable contact condition determination threshold, it is marked as a second state level; and when the first deviation feature is less than the unstable contact condition determination threshold, it is marked as a third state level. The frequency of the state level marking results within a consecutive preset number of times is statistically analyzed, and the state level with the highest statistical frequency is selected as the confirmed state level. When the confirmation status level is the first status level, monitoring is maintained without alarm output; when the confirmation status level is the second status level, a warning level alarm signal is output; and when the confirmation status level is the third status level, a danger level alarm signal is output.

[0014] This invention provides a safety belt fall protection monitoring system based on a sudden change in the annular magnetic field.

[0015] To solve the above technical problems, the present invention provides the following technical solution: a safety belt fall protection monitoring system based on a sudden change in the ring magnetic field, comprising: a magnetic field excitation module, disposed on the hook ring, used to apply an excitation signal according to excitation parameters to establish an alternating magnetic field; A magnetic field detection module is installed on the hook ring to detect the magnetic field strength inside the hook ring; The parameter calculation module is used to establish mathematical models and determine excitation parameters and decision thresholds; The signal processing module is used to transmit and filter the output signal of the magnetic field detection module; The status determination module is used to calculate the magnetic field deviation, compare it with the determination threshold, and confirm the hook status. The alarm output module is used to output alarm signals based on the hook status.

[0016] The present invention provides a computer device, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps of the described method for monitoring and protecting a seat belt from falling based on a sudden change in a circular magnetic field.

[0017] The present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the aforementioned safety belt fall protection monitoring method based on a sudden change in a circular magnetic field.

[0018] The beneficial effects of this invention are as follows: This invention establishes a cascaded coupling mathematical relationship between the current of the excitation unit and the output of the detection unit, calculates the response characteristics of the excitation parameters under both no-load and ferromagnetic medium intrusion states, screens the parameters to be optimized through sensitivity analysis and performs multi-parameter joint optimization, and determines the value range of the excitation unit structural parameters and excitation signal parameters.

[0019] Numerical simulations were used to analyze the magnetic field data of ferromagnetic objects penetrating hook rings at different relative permeabilities and spatial positions. Based on spatial position, the simulations categorized the contact conditions into stable and unstable contact. A regression relationship was established between the magnetic field change factor and the object's parameters. Statistical analysis was performed on the data for both types of conditions to determine safety margin coefficients and differentiated judgment thresholds for each condition. When the object deviates from the center of the hook ring or penetrates at an angle, a warning signal is triggered if the measured deviation falls within the unstable contact threshold range.

[0020] The measurement deviation is compared with two types of judgment thresholds to mark the state level. The frequency of consecutive state level markings is statistically analyzed, and the high-frequency state level is used as the confirmed state level. Individual erroneous markings caused by occasional transient interference will not become confirmed state levels due to their low frequency. Changes in the actual state cause new state levels to continuously appear and become confirmed state levels. Attached Figure Description

[0021] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 This is a flowchart illustrating a safety belt fall protection monitoring method based on a sudden change in the annular magnetic field, as provided in one embodiment of the present invention.

[0023] Figure 2 The above is a flowchart of S1, which illustrates a method for monitoring and protecting safety belts against fall based on sudden changes in a circular magnetic field, according to an embodiment of the present invention.

[0024] Figure 3 The above is a flowchart of S3 for a safety belt fall protection monitoring method based on a sudden change in the annular magnetic field, provided as an embodiment of the present invention. Detailed Implementation

[0025] To make the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0026] Example 1, referring to Figures 1-3 This is one embodiment of the present invention, which provides a safety belt fall protection monitoring method based on abrupt changes in the annular magnetic field, comprising: Step S1: Establish a first mathematical model to characterize the electromagnetic coupling relationship between the excitation unit and the detection unit, and determine the first set of parameters through the first mathematical model.

[0027] Step S2: Set a first functional unit and a second functional unit in the hook ring, apply a first excitation signal to the first functional unit according to the first parameter set, and obtain the first magnetic field characteristic value by measuring through the second functional unit under the first working condition.

[0028] Step S3: Establish a second mathematical model that includes the hook ring geometry and the physical properties of the ferromagnetic medium. Calculate the second magnetic field characteristic value at the detection location under different ferromagnetic medium intrusion conditions using the second mathematical model. Compare the second magnetic field characteristic value with the first magnetic field characteristic value to obtain the first correlation rule. Determine the first threshold set based on the first correlation rule.

[0029] Step S4: Apply a second excitation signal to the first functional unit, obtain the third magnetic field characteristic value through periodic measurement by the second functional unit, calculate the first deviation characteristic quantity between the third magnetic field characteristic value and the first magnetic field characteristic value, and compare the first deviation characteristic quantity with the first threshold set to determine the hook state.

[0030] In step S1, a first mathematical model characterizing the electromagnetic coupling relationship between the excitation unit and the detection unit is established, and a first parameter set is determined through the first mathematical model, including the following steps: S101: Establish the first mathematical relationship between the excitation unit current and the magnetic field strength inside the hook ring.

[0031] It is understandable that the excitation unit is an excitation coil wound around the hook ring. When an alternating current is applied to the excitation coil, an alternating magnetic field is generated inside the hook ring.

[0032] In this embodiment, the hook ring is an open-loop structure in the safety belt system that connects the worker to the anchor point, including a pole strap hook ring and a safety rope hook ring. The excitation coil is wound around the outside of the hook ring body.

[0033] It should be noted that the hook ring has an open ring structure, and the opening position causes the magnetic circuit to be incompletely closed. The method for establishing the first mathematical relationship is as follows: measure the average radius, cross-sectional dimensions of the ring, and opening angle of the hook ring, and calculate the actual circumference of the hook ring. The opening correction factor is determined based on the ratio of the opening angle to the ring circumference; the opening correction factor characterizes the degree of magnetic flux leakage at the opening on the magnetic circuit. The equivalent magnetic circuit length of the hook ring is the product of the actual circumference and the opening correction factor.

[0034] It's easy to understand that, based on Ampere's circuital law, the line integral of the magnetic field strength along the equivalent magnetic circuit is equal to the product of the number of turns in the excitation coil and the current intensity. The first mathematical relation takes the number of turns in the excitation coil, the excitation current intensity, and the equivalent magnetic circuit length as inputs, and calculates the average magnetic field strength within the loop as the output. The average magnetic field strength within the loop is equal to the product of the number of turns in the excitation coil and the current intensity divided by the equivalent magnetic circuit length.

[0035] S102: Establish a second mathematical relationship between the magnetic field strength inside the hook ring and the magnetic field induced by the detection probe.

[0036] Understandably, the detection probe is installed in a fixed position inside the hook ring and uses a TMR magnetic field sensor based on the tunnel magnetoresistive effect.

[0037] In this embodiment, the detection probe is mounted on the inner wall of the hook ring, and the probe's sensing direction is arranged radially along the hook ring. The opening of the hook ring causes the internal magnetic field to exhibit non-uniform spatial distribution.

[0038] It should be noted that the method for establishing the second mathematical relationship is as follows: A cylindrical coordinate system is established with the geometric center of the hook ring as the origin, and the coordinate axes are radial, circumferential, and axial. The circumferential distribution of the magnetic field strength within the hook ring is affected by the opening position; the magnetic field strength decreases when the circumferential position is closer to the opening and increases when the circumferential position is farther from the opening. A circumferential position correction function is introduced, which describes the correction relationship from the average magnetic field strength of the hook ring to the magnetic field strength at any circumferential position. The input to the circumferential position correction function is the difference between the circumferential angle of the probe installation position and the circumferential angle of the opening position, and the output is the circumferential position correction coefficient.

[0039] It's easy to understand that the second mathematical relationship takes the average magnetic field strength inside the hook ring and the circumferential position correction coefficient as input, and calculates the induced magnetic field strength at the probe's installation position as output. The induced magnetic field strength of the probe is equal to the product of the average magnetic field strength inside the hook ring and the circumferential position correction coefficient. The circumferential position correction function is obtained through finite element electromagnetic field simulation: a three-dimensional geometric model of the hook ring is established, and the magnetic field strength at different circumferential positions is solved in simulation software, fitting the functional relationship between the circumferential angle difference and the magnetic field strength correction coefficient.

[0040] S103: Establish the third mathematical relationship between the magnetic field induced by the detection probe and the electrical signal output by the detection unit.

[0041] Understandably, the resistance change of the TMR sensor is converted into the output voltage of the detection unit through a signal conversion circuit. The signal conversion circuit includes a Wheatstone bridge and an instrumentation amplifier.

[0042] In this embodiment, the TMR sensor serves as one resistive arm of the bridge circuit, while the other three arms are fixed resistors. Changes in the resistance of the TMR sensor cause a differential voltage to be output by the bridge circuit. This differential voltage is amplified by an instrumentation amplifier to form the output voltage of the detection unit.

[0043] It should be noted that the relationship between the resistance change of the TMR sensor and the induced magnetic field is non-linear. In the weak magnetic field region, the slope of the relationship between the resistance change rate and the magnetic field strength is relatively large; in the strong magnetic field region, the resistance change rate tends to saturate. Since the internal magnetic field strength of the hook ring varies considerably under both unloaded and ferromagnetic medium intrusion conditions, it is necessary to establish a mathematical relationship that accurately describes the response characteristics of the TMR sensor across the entire magnetic field strength range to ensure reliable operation of the detection system in both states. The initial magnetic field strength is set to zero, and the termination value is set to 1.5 times the maximum magnetic field strength predicted by the first mathematical model under ferromagnetic medium intrusion conditions. Calibration points are uniformly selected within this range.

[0044] It's easy to understand that establishing the third mathematical relationship requires first obtaining the magnetoresistive effect characteristics of the TMR sensor. The specific method is as follows: Apply a magnetic field of known strength to the TMR sensor in a standard magnetic field generator. The magnetic field strength range covers the range of magnetic field strengths under both the hook ring's unloaded state and the state of ferromagnetic medium intrusion. Measure the resistance value of the TMR sensor at each magnetic field strength and calculate the change in resistance. Perform curve fitting on the measurement data to establish a functional relationship between the induced magnetic field strength and the change in resistance, denoted as the TMR magnetoresistive effect characteristic function. The physical meaning of this function is: when the TMR sensor senses the magnetic field strength... The change in sensor resistance relative to the reference resistance.

[0045] Furthermore, to establish a complete conversion relationship from the induced magnetic field strength to the output voltage of the detection unit, it is necessary to introduce mathematical expressions describing the conversion characteristics of the Wheatstone bridge and the gain characteristics of the instrumentation amplifier. Based on the resistive voltage division principle and balance condition of the Wheatstone bridge, when the resistance of the TMR sensor changes, the differential voltage output by the bridge is proportional to the change in resistance. Considering the four-arm configuration and balance condition of the bridge, the formula for establishing the third mathematical relationship is as follows: It should be noted that the meanings and data sources of each parameter in the formula are as follows. The output variable of the third mathematical relationship represents the amplitude of the voltage signal finally output by the detection unit. The voltage amplification factor of the instrumentation amplifier is selected based on the target amplitude range of the output voltage of the detection unit. The target amplitude range must meet the input requirements of the subsequent signal processing circuit. The DC excitation voltage applied to the Wheatstone bridge affects the amplitude of the bridge's output differential voltage and is selected based on the rated operating voltage of the TMR sensor and the bridge's power consumption requirements. The TMR magnetoresistance effect characteristic function obtained through the aforementioned experimental calibration is calculated, and the input of this function is the induced magnetic field strength of the detection probe. The output is the corresponding change in resistance. The resistance value of the TMR sensor under zero magnetic field conditions is obtained from the device datasheet provided by the sensor manufacturer.

[0046] It's easy to understand that the coefficient 4 in the formula originates from the derivation of the balance condition of the Wheatstone bridge, reflecting the proportional relationship between the differential voltage output of the bridge and the change in the value of a single resistor arm in a four-arm configuration. Using the above formula, the induced magnetic field strength of the detection probe can be measured. As input, it passes through the TMR magnetoresistive effect characteristic function. The voltage is converted into a resistance change, then converted using a bridge circuit and an amplifier gain converter, ultimately yielding the output voltage of the detection unit. This formula describes the complete conversion path from physical quantity to electrical signal, providing a mathematical tool for predicting the output voltage of the detection unit under different excitation parameters using the first mathematical model.

[0047] S104: Cascade and couple the first mathematical relation, the second mathematical relation, and the third mathematical relation to form the first mathematical model.

[0048] It is understandable that the cascaded coupling implementation is as follows: the first mathematical relation outputs the average magnetic field strength inside the hook loop, and the average magnetic field strength and the circumferential position of the detection probe are used as the input of the second mathematical relation. The second mathematical relation outputs the magnetic field strength induced by the detection probe, and the induced magnetic field strength is used as the input of the TMR magnetoresistive effect characteristic function in the third mathematical relation. The output voltage of the detection unit is calculated through the formula of the third mathematical relation.

[0049] In this embodiment, the first mathematical model, using the input excitation coil turns, excitation current intensity, excitation current frequency, excitation current waveform, and hook loop geometric parameters, sequentially calculates through the first, second, and third mathematical relationships to obtain the predicted value of the detection unit's output voltage. The formula for the third mathematical relationship combines the TMR magnetoresistive effect characteristic function with the bridge conversion relationship and amplifier gain relationship, realizing the quantitative calculation from the magnetic field induced by the detection probe to the output voltage of the detection unit. This allows the first mathematical model to fully predict the impact of changes in excitation parameters on the detection unit's output.

[0050] S105: Using the structural parameters of the excitation unit and the excitation signal parameters as the parameters to be analyzed, the dual-state response characteristics of the parameters to be analyzed under no-load and ferromagnetic medium intrusion states are calculated through the first mathematical model.

[0051] Understandably, the parameters to be analyzed include the number of turns of the excitation coil, the frequency of the excitation current, the waveform of the excitation current, and the amplitude of the excitation current. The no-load state refers to the condition where the hook ring is not suspended from any fixed object, while the ferromagnetic medium intrusion state refers to the condition where a ferromagnetic fixed object passes through the hook ring.

[0052] In this embodiment, the dual-state response characteristics include the output voltage amplitude of the detection unit, the uniformity of the magnetic field distribution within the hook ring, and the temporal stability of the magnetic field. The uniformity of the magnetic field distribution is characterized by the standard deviation of the magnetic field strength at multiple spatial sampling points uniformly distributed circumferentially within the hook ring. The temporal stability of the magnetic field is characterized by the coefficient of variation of the magnetic field strength at continuous-time sampling points. The output voltage of the detection unit under both the no-load state and the ferromagnetic medium intrusion state is calculated using a first mathematical model, and the ratio of the two is the magnetic field response multiple.

[0053] S106: Perform sensitivity quantification analysis on the dual-state response characteristics to obtain the sensitivity value of the parameter to be analyzed.

[0054] Understandably, the implementation method for sensitivity quantification analysis is as follows: For each parameter to be analyzed, three sampling points—initial value, intermediate value, and termination value—are selected within its engineering achievable range. The dual-state response characteristic values ​​corresponding to the three sampling points are calculated using a first mathematical model. The percentage change in the response characteristic corresponding to the intermediate value relative to the initial value is calculated, and divided by the percentage change in the parameter from the initial value to the intermediate value, to obtain the sensitivity value of that parameter.

[0055] In this embodiment, each parameter to be analyzed corresponds to six sensitivity values, which are the sensitivities to output voltage amplitude, magnetic field distribution uniformity, and magnetic field time-domain stability under no-load and ferromagnetic medium immersion conditions, respectively. The six sensitivity values ​​are then averaged with equal weights to obtain the comprehensive sensitivity value.

[0056] S107: Sort the parameters by sensitivity values ​​and select those whose sensitivity values ​​exceed the preset threshold as parameters to be optimized.

[0057] Understandably, the preset threshold is determined by calculating the median of the overall sensitivity values ​​of all parameters to be analyzed, and using this median as the preset threshold. Parameters with overall sensitivity values ​​exceeding the median are included in the set of parameters to be optimized.

[0058] S108: Establish no-load detectable constraints and intrusion distinguishable constraints for the parameters to be optimized, and perform multi-parameter joint optimization under the constraints to obtain the parameter feasible region.

[0059] Understandably, the no-load detectable constraints include: the output voltage amplitude of the detection unit exceeds the minimum detectable voltage of the detection unit under no-load conditions, the standard deviation of the magnetic field distribution uniformity is lower than the average magnetic field strength, and the coefficient of variation of the magnetic field time-domain stability is lower than the preset upper limit. The intrusion distinguishable constraint is: the magnetic field response multiple exceeds the preset lower limit.

[0060] It should be noted that the minimum detectable voltage is set to three times the sum of the voltage value corresponding to the analog-to-digital converter resolution and the standard deviation of the signal noise. The preset upper limit for magnetic field time-domain stability is set to 0.05, indicating that the fluctuation range of the magnetic field strength does not exceed 5% of the average value. The preset lower limit for the magnetic field response multiple is set to 1.2, indicating that the magnetic field strength under ferromagnetic medium intrusion conditions is at least 1.2 times that under no-load conditions.

[0061] In this embodiment, the multi-parameter joint optimization is implemented as follows: the value space of the parameters to be optimized is divided into a grid, with each grid node corresponding to a set of parameter values. All grid nodes are traversed, and the bi-state response characteristics corresponding to each node are calculated using a first mathematical model to determine whether they satisfy the no-load detectability constraint and the intrusion distinguishability constraint. Nodes that satisfy the constraints constitute the parameter feasible region.

[0062] It should be noted that the no-load detectability constraint ensures that the output signal of the detection unit is not submerged in noise and the measurement result is stable when measuring the reference magnetic field strength value in step S2. The intrusion distinguishability constraint ensures that the judgment threshold determined in step S3 can effectively distinguish between the no-load state and the ferromagnetic medium intrusion state.

[0063] S109: Extract the range of values ​​of the parameters to be optimized from the feasible region of the parameters as the first parameter set.

[0064] Understandably, the parameter values ​​of all nodes in the feasible region are statistically analyzed, and the minimum and maximum values ​​of each parameter to be optimized are extracted to form a value range. The first parameter set includes the value range of the number of turns of the excitation coil, the value range of the excitation current frequency, and the type of excitation current waveform.

[0065] It should be noted that step S1 establishes a first mathematical model to quantitatively correlate the excitation parameters with the detection performance under the hook-ring open magnetic circuit structure. The formula for the third mathematical relationship, by introducing the TMR magnetoresistive effect characteristic function, accurately describes the nonlinear response characteristics of the TMR sensor under different magnetic field strengths, enabling the first mathematical model to predict the output voltage of the detection unit when the no-load state and the ferromagnetic medium intrusion state span a large range of magnetic field strengths. Through dual-state response characteristic calculation and constraint setting, the determined first parameter set ensures that the amplitude of the detection unit output signal exceeds the minimum detectable voltage and the magnetic field distribution and time-domain fluctuations meet the stability requirements under the no-load state, and ensures that the magnetic field response multiple is large enough to reliably identify magnetic field abrupt changes under the ferromagnetic medium intrusion state.

[0066] It is understandable that in step S2, the first functional unit is the excitation coil and the second functional unit is the detection probe.

[0067] In this embodiment, the specific implementation of step S2 includes steps S201-S205, wherein: S201: Set the first functional unit in the hook ring.

[0068] It is understandable that, based on the range of values ​​for the number of turns of the excitation coil in the first parameter set, the middle value within that range is selected as the actual number of turns of the excitation coil.

[0069] In this embodiment, enameled copper wire is wound circumferentially along the outer side of the hook ring body, with the number of turns equal to a selected value. The winding position avoids the opening area of ​​the hook ring to ensure that the magnetic field generated by the excitation coil propagates along the hook ring body.

[0070] It should be noted that selecting the middle value of the range ensures that the excitation parameter is in the center of the feasible region of the parameter while satisfying the constraint conditions in step S1, thus avoiding performance fluctuations at the parameter boundary.

[0071] S202: A second functional unit is set in the hook ring.

[0072] It is understandable that the installation position of the detection probe is determined according to the circumferential position correction function in step S1.

[0073] In this embodiment, the position opposite to the hook ring opening angle is selected as the installation position of the detection probe. The TMR sensor is installed at the selected position using a fixing bracket, with the sensor sensing direction arranged radially along the hook ring. The sensor signal lead is connected to a signal conversion circuit, which includes a Wheatstone bridge and an instrumentation amplifier. The values ​​of the bridge excitation voltage and the amplifier gain are consistent with the parameters used when establishing the third mathematical relationship in step S1.

[0074] It should be noted that the signal conversion circuit parameters are consistent with those in step S1 to ensure that the measured output voltage of the detection unit can be used to calculate the magnetic field strength induced by the detection probe through the conversion relationship established in step S1.

[0075] S203: Apply a first excitation signal to the first functional unit according to the first parameter set.

[0076] It is understandable that the frequency and waveform of the first excitation signal are selected based on the frequency range and waveform type of the excitation current in the first parameter set.

[0077] In this embodiment, the median value from the excitation current frequency range is selected as the frequency of the first excitation signal, and a sine wave is selected as the waveform from the excitation current waveform categories. The current amplitude of the first excitation signal is consistent with the excitation current amplitude used in step S1 when the first mathematical model calculates the dual-state response characteristics. The output parameters of the signal generator are set to the selected values, connected to the excitation coil lead, and the first excitation signal is applied.

[0078] It should be noted that all parameters of the first excitation signal are consistent with the parameters calculated theoretically in step S1, ensuring that the actual measurement conditions are the same as the theoretical calculation conditions.

[0079] S204: Ensure the hook ring is in the first working condition.

[0080] It is understandable that the first working condition corresponds to the unloaded state defined in step S1, that is, the working condition in which the hook ring is not suspended from a fixed object and the hook ring opening is in the open state.

[0081] In this embodiment, before applying the first excitation signal, it is checked and confirmed that there are no ferromagnetic objects inside the hook ring. The hook ring is kept open and not in contact with any fixed object. The hook ring is then suspended on a non-ferromagnetic support.

[0082] S205: Obtain the first magnetic field characteristic value through measurement by the second functional unit.

[0083] Understandably, when the first excitation signal is applied and the hook ring is in the first working condition, the output voltage of the detection unit is measured by the detection probe and converted into the magnetic field strength inside the hook ring.

[0084] In this embodiment, the output voltage of the detection unit undergoes sampling and averaging for multiple consecutive cycles to obtain a stable output voltage amplitude. Using the third mathematical relationship from step S1, the induced magnetic field strength of the detection probe is calculated in reverse based on the measured output voltage amplitude. The reverse calculation method is as follows: calculate the resistance change from the output voltage using the formula of the third mathematical relationship, and then use the inverse function of the magnetoresistive effect characteristic function obtained from the experimental calibration in step S1 to find the corresponding induced magnetic field strength based on the resistance change. The calculated induced magnetic field strength is used as the first magnetic field characteristic value.

[0085] It should be noted that the first magnetic field characteristic value is the induced magnetic field strength at the probe position of the hook ring under the first working condition. This value is obtained by measuring the output voltage of the detection unit and calculating it in reverse using the third mathematical relationship established in step S1, and characterizes the magnetic field reference of the hook ring under no-load conditions.

[0086] Furthermore, the inverse function of the magnetoresistive effect characteristic function is obtained by interchanging the coordinate axes of the curve relating the resistance change and the magnetic field strength obtained from the experimental calibration in step S1. During the reverse calculation, the corresponding induced magnetic field strength is looked up in the reverse lookup table based on the calculated resistance change. If it lies between two adjacent data points in the lookup table, a linear interpolation method is used for calculation.

[0087] It should be noted that step S2 establishes a correspondence between theory and actual measurement by keeping the excitation signal parameters and signal conversion circuit parameters consistent with those calculated theoretically in step S1. Through the reverse calculation of the third mathematical relationship, the measured output voltage of the detection unit is converted into the magnetic field strength induced by the detection probe, and the magnetic field reference value of the hook ring under no-load condition is obtained, providing a measured reference for step S3 to calculate the magnetic field change multiple under different invasive conditions of ferromagnetic media.

[0088] In step S3, the first threshold set is determined according to the first correlation rule, including the following steps: S301: Numerical solution is performed on multiple sets of values ​​for the relative permeability and spatial position of the ferromagnetic medium to obtain a simulation dataset of magnetic field strength.

[0089] Understandably, the second mathematical model is a numerical calculation model describing the magnetic field distribution after a ferromagnetic medium enters the hook ring.

[0090] In this embodiment, the method for establishing the second mathematical model is as follows: A three-dimensional geometric model of the hook ring is established in finite element simulation software. The dimensional parameters of the geometric model are consistent with the actual hook ring size used in step S2. An excitation coil is set in the geometric model, and the number of turns, winding position, current frequency, current waveform, and current amplitude of the excitation coil are consistent with the parameters of the first excitation signal applied in step S2. The installation position of the detection probe is marked in the hook ring geometric model, and the marked position is consistent with the coordinates of the actual installation position in step S2.

[0091] It should be noted that a ferromagnetic medium geometry is added inside the hook ring geometry model. This ferromagnetic medium geometry is a cylindrical rod-shaped object representing a ferromagnetic fixing object passing through the hook ring. The spatial position of the ferromagnetic medium is described by two parameters: the radial offset distance is the distance between the axis of the ferromagnetic medium and the geometric center axis of the hook ring projected onto the hook ring plane, and the axial angle is the angle between the axis of the ferromagnetic medium and the normal to the hook ring plane.

[0092] It is not difficult to understand that the second mathematical model is based on the magnetic field distribution equation under quasi-static conditions. Since the frequency of the excitation signal applied in step S2 is in the low-frequency range, the displacement current term can be ignored, and the magnetic field distribution satisfies: in, For position Magnetic vector potential at that location For position Permeability at that point Let be the current density vector of the excitation coil. In the hook ring region, the permeability is taken as the permeability of the hook ring material; in the ferromagnetic medium region, the permeability is taken as the product of the vacuum permeability and the relative permeability; and in the air region, the permeability is taken as the vacuum permeability.

[0093] It should be noted that the relationship between the magnetic vector potential and the magnetic field strength is as follows: the magnetic induction intensity equals the curl of the magnetic vector potential, and the magnetic field strength equals the magnetic induction intensity divided by the permeability. By solving the above equations using the finite element method, the magnetic field strength at the probe installation location is calculated based on the magnetic vector potential. The calculated magnetic field strength value is the second characteristic value of the magnetic field.

[0094] Furthermore, multiple sets of values ​​were taken for the relative permeability and spatial position parameters of the ferromagnetic medium. The relative permeability ranged from 50 to 1000, covering the relative permeability ranges of carbon steel, stainless steel, and cast iron. The radial offset distance ranged from 0 to the inner diameter of the hook ring. The axial angle ranged from 0° to 90°.

[0095] It is easy to understand that finite element numerical solutions are used to solve for different combinations of relative permeability, radial offset distance, and axial angle. Each set of parameters corresponds to a simulation condition, and for each simulation condition, the second magnetic field characteristic value at the probe position is obtained by solving the second mathematical model. The second magnetic field characteristic values ​​of all simulation conditions and their corresponding relative permeability, radial offset distance, and axial angle parameters are summarized to form a magnetic field strength simulation dataset.

[0096] S302: Based on spatial location, the magnetic field strength simulation dataset is divided into stable contact condition data and unstable contact condition data.

[0097] It is understandable that the distinction between stable and unstable contact conditions is based on the spatial position parameters of the ferromagnetic medium.

[0098] In this embodiment, a stable contact condition is defined as one where both the radial offset distance and the axial angle are within a preset range. An unstable contact condition is defined as one where either the radial offset distance or the axial angle exceeds a preset range.

[0099] It should be noted that the preset range is determined based on the hook ring's geometry and the actual usage scenario. The upper limit of the radial offset distance corresponding to stable contact conditions is taken as half the difference between the hook ring's inner diameter and the diameter of the cylindrical ferromagnetic medium, ensuring that the ferromagnetic medium can freely pass through within the hook ring's inner diameter range. The upper limit of the axial angle is determined through a hook ring load-bearing capacity test: the ferromagnetic medium is passed through the hook ring at different axial angles and a rated load is applied. The minimum axial angle when the hook ring undergoes plastic deformation is recorded, and 0.8 times this angle is taken as the upper limit of the axial angle.

[0100] It's easy to understand that the process iterates through all the data in the magnetic field strength simulation dataset, determining the working condition category based on the radial offset distance and axial angle corresponding to each data point. Data where both radial offset distance and axial angle are within a preset range are classified as stable contact working condition data, while the remaining data are classified as unstable contact working condition data.

[0101] S303: Calculate the ratio of the two operating condition data to the characteristic value of the first magnetic field, and obtain the change factor data of the stable contact condition and the change factor data of the unstable contact condition respectively.

[0102] Understandably, for each second magnetic field characteristic value in the stable contact condition data, the ratio of the second magnetic field characteristic value to the first magnetic field characteristic value obtained in step S2 is calculated to obtain the magnetic field change factor under the stable contact condition. For each second magnetic field characteristic value in the unstable contact condition data, the ratio of the second magnetic field characteristic value to the first magnetic field characteristic value is calculated to obtain the magnetic field change factor under the unstable contact condition.

[0103] In this embodiment, all magnetic field variation factors under stable contact conditions, along with their corresponding relative permeability, radial offset distance, and axial angle parameters, are summarized as stable contact condition variation factor data. All magnetic field variation factors under unstable contact conditions, along with their corresponding parameters, are summarized as unstable contact condition variation factor data.

[0104] S304: Establish a regression relationship between relative permeability and spatial location based on the change factor data.

[0105] Understandably, the regression relationship describes the quantitative functional relationship between the magnitude of the magnetic field change and the parameters of the ferromagnetic medium. The regression relationship is established based on the simulation data calculated by the second mathematical model in step S301.

[0106] In this embodiment, the data on the change factor under stable contact conditions and the change factor under unstable contact conditions are combined, with the magnetic field change factor as the dependent variable and the relative permeability of the ferromagnetic medium, radial offset distance, and axial angle as independent variables. The regression relationship uses a polynomial function including cross terms: in, The multiple of the magnetic field change. The relative permeability of a ferromagnetic medium. Radial offset distance, The included angle is axial. to is the regression coefficient.

[0107] It should be noted that the regression coefficients are determined using the least squares method. By substituting the relative permeability, radial offset distance, axial angle, and corresponding magnetic field change factors for all simulated operating conditions into the above formula, and solving using the least squares method, the regression coefficients that minimize the sum of squared residuals between the predicted and actual values ​​are obtained.

[0108] It is easy to understand that after the regression relationship is established, the relative permeability, radial offset distance, and axial angle of any ferromagnetic medium are input, and the predicted magnetic field change factor is calculated through the regression relationship. The regression relationship extends the finite number of simulation conditions solved by the second mathematical model in step S301 to the prediction of any point in the parameter space.

[0109] S305: Determine the threshold for judging stable contact conditions and the threshold for judging unstable contact conditions based on the regression relationship and the change factor data, and use them as the first threshold set.

[0110] Understandably, the judgment threshold is used to determine the contact status of the hook ring based on the measured magnetic field deviation in real-time monitoring.

[0111] Furthermore, in step S305, the determination of the stable contact condition threshold and the unstable contact condition threshold based on the regression relationship and change factor data includes: S305.1: Perform statistical analysis on the change factor data of stable contact conditions and the change factor data of unstable contact conditions respectively to obtain the distribution characteristic parameters.

[0112] It is understandable that the minimum and average values ​​of the variation factors for stable contact conditions are calculated as the distribution characteristic parameters for stable contact conditions. Similarly, the minimum and average values ​​of the variation factors for unstable contact conditions are calculated as the distribution characteristic parameters for unstable contact conditions.

[0113] S305.2: Determine the safety margin coefficient based on the regression relationship and distribution characteristic parameters respectively.

[0114] Understandably, the safety margin coefficient is used to introduce a safety margin into the decision threshold.

[0115] In this embodiment, the safety margin coefficient is calculated as follows: For stable contact conditions, the relative permeability values ​​of common ferromagnetic fixing materials are selected, and the center value of the spatial position parameter range for stable contact conditions is selected. The predicted magnetic field change factor is calculated through regression. The relative error between the predicted magnetic field change factor and the average value is calculated. The safety margin coefficient for stable contact conditions is equal to 1 minus twice the relative error. The same method is used to calculate the safety margin coefficient for unstable contact conditions.

[0116] S305.3: Extract extreme values ​​from the change factor data.

[0117] It is understandable that the minimum value is extracted from the change factor data of stable contact conditions, and the minimum value is extracted from the change factor data of unstable contact conditions.

[0118] S305.4: Apply a safety margin coefficient to the extreme values ​​to obtain the judgment threshold.

[0119] Understandably, the threshold for determining a stable contact condition is equal to the product of the minimum change factor of the stable contact condition and the safety margin coefficient of the stable contact condition. The threshold for determining an unstable contact condition is equal to the product of the minimum change factor of the unstable contact condition and the safety margin coefficient of the unstable contact condition. The thresholds for determining stable and unstable contact conditions constitute the first threshold set.

[0120] It should be noted that step S3 uses the second mathematical model to numerically solve the magnetic field distribution within the hook ring of the ferromagnetic fixture under different parameter conditions, based on the magnetic field distribution equation under quasi-static conditions. The magnetic field change factor is calculated using the first magnetic field characteristic value measured in step S2 as a benchmark, and a regression relationship is established based on the simulation data from the second mathematical model. The regression relationship extends a finite number of simulation conditions to the prediction of any point in the parameter space using a polynomial function containing cross terms. The judgment threshold is obtained by extracting the extreme values ​​of the change factor and applying a safety margin coefficient determined based on the regression prediction error.

[0121] Understandably, step S4 is the real-time monitoring stage, which determines the contact state of the hook ring by periodically measuring the change in the magnetic field strength inside the hook ring. Specifically, it includes the following steps: S401 applies a second excitation signal to the first functional unit.

[0122] It is understandable that the second excitation signal is consistent with the first excitation signal applied in step S2 in terms of the number of turns of the excitation coil, the current frequency, the current waveform, and the current amplitude, to ensure that the real-time monitoring conditions are the same as the reference measurement conditions.

[0123] In this embodiment, a second excitation signal is continuously applied to the excitation coil via a signal generator, generating a stable alternating magnetic field within the hook loop. The second excitation signal is continuously applied, unlike the first excitation signal which is applied during a single measurement. The second excitation signal remains continuously output throughout the entire real-time monitoring process.

[0124] S402 obtains the third magnetic field characteristic value through periodic measurements by the second functional unit.

[0125] It is understandable that the specific implementation method for obtaining the characteristic value of the third magnetic field through periodic measurement includes steps S402.1-S402.4, wherein: S402.1: Trigger magnetic field measurement according to the preset time interval.

[0126] Understandably, the preset time interval is determined based on the dynamic response requirements of the actual application scenario of the hook ring.

[0127] In this embodiment, a timer triggers magnetic field measurements at preset time intervals. Each time a measurement is triggered, the detection unit acquires the voltage signal output by the detection probe. The preset time interval must be less than the minimum duration of the hook loop's state change to ensure that the state change process can be captured.

[0128] S402.2: Transmit measurement signals via impedance matching.

[0129] Understandably, the detection probe and the signal conversion circuit are connected via a transmission line. Transmission lines have characteristic impedance; when the circuit impedance at both ends of the transmission line does not match the characteristic impedance, the signal is reflected at the transmission line endpoints, causing signal distortion.

[0130] In this embodiment, impedance matching circuits are provided at both ends of the transmission line, and the impedance value of the impedance matching circuits is consistent with the characteristic impedance of the transmission line. The voltage signal output by the detection probe is transmitted to the amplifier input of the signal conversion circuit through the impedance matching circuit, thus avoiding signal reflection.

[0131] S402.3: Perform digital filtering on the transmitted signal.

[0132] It is understandable that the transmitted signal contains a signal component corresponding to the excitation signal frequency and a frequency component corresponding to the ambient noise. Digital filtering uses digital signal processing algorithms to remove the noise frequency component while retaining the excitation signal frequency component.

[0133] In this embodiment, the voltage signal output by the signal conversion circuit is converted into a digital signal by an analog-to-digital converter. Digital filtering is then performed on the digital signal, using a low-pass filter. The cutoff frequency of the low-pass filter is set to be higher than the second excitation signal frequency but lower than the main noise frequency, retaining the excitation signal frequency component and its adjacent frequency range while filtering out high-frequency noise components.

[0134] S402.4: Extract the amplitude of the filtered signal as the third magnetic field characteristic value.

[0135] It is understandable that the filtered digital signal is a time-domain waveform, and the amplitude of the time-domain waveform reflects the amplitude of the output voltage of the detection unit.

[0136] In this embodiment, the filtered digital signal is sampled within one excitation signal cycle to obtain multiple sampling point values. The difference between the maximum and minimum values ​​of the sampling points is calculated, and half of the difference is used as the output voltage amplitude of the detection unit.

[0137] It should be noted that the output voltage amplitude of the detection unit is converted into the induced magnetic field strength of the detection probe through reverse calculation using the third mathematical relationship established in step S2. The reverse calculation method is consistent with the method used in step S2 to obtain the first magnetic field characteristic value from the output voltage amplitude: the resistance change is calculated from the output voltage according to the formula of the third mathematical relationship, and then the corresponding induced magnetic field strength is found by reverse calculation using the inverse function of the magnetoresistive effect characteristic function obtained from the experimental calibration in step S1. The induced magnetic field strength value obtained by reverse calculation is the third magnetic field characteristic value.

[0138] It is easy to understand that the third magnetic field characteristic value characterizes the magnetic field strength level at the location of the detection probe inside the hook ring at the real-time monitoring moment. The process for obtaining the third magnetic field characteristic value is consistent with the process for obtaining the first magnetic field characteristic value in step S2, ensuring the comparability of the benchmark measurement and the real-time measurement results.

[0139] S403 calculates the first deviation characteristic quantity between the characteristic value of the third magnetic field and the characteristic value of the first magnetic field.

[0140] Understandably, the first deviation characteristic quantity characterizes the degree of change in the magnetic field strength inside the hook ring relative to the no-load state reference at the real-time monitoring moment.

[0141] In this embodiment, the first deviation characteristic is calculated as follows: the ratio of the third magnetic field characteristic value to the first magnetic field characteristic value measured in step S2 is calculated. A ratio of 1 indicates that the magnetic field strength inside the hook ring is consistent with the unloaded state, and a ratio greater than 1 indicates that the magnetic field strength inside the hook ring is higher than the unloaded state level.

[0142] It should be noted that the physical meaning of the first deviation characteristic quantity is consistent with the magnetic field change factor defined in step S3. Step S3 uses a second mathematical model to simulate and calculate the magnetic field change factor under different ferromagnetic medium parameter conditions and determines the judgment threshold. The first deviation characteristic quantity calculated in step S4 is compared with the judgment threshold in step S3 to determine the contact state.

[0143] S404 compares the first deviation feature with the first threshold set to determine the hook state.

[0144] It is understood that the hook state is determined by comparing the first deviation feature with the first threshold set determined in step S3. The first threshold set includes a stable contact condition determination threshold and an unstable contact condition determination threshold. The specific implementation method for determining the hook state includes the following three steps: Step 1: Use three values ​​to compare and mark the state level.

[0145] It is understandable that the first deviation characteristic quantity is compared with the stable contact condition judgment threshold and the unstable contact condition judgment threshold in three ways.

[0146] In this embodiment, when the first deviation characteristic quantity is greater than or equal to the stable contact condition determination threshold, it is marked as the first state level. The first state level indicates that the hook ring is in a stable contact state, the ferromagnetic fixing object passes perpendicularly through the central area of ​​the hook ring, and the hook ring has normal load-bearing capacity.

[0147] When the first deviation characteristic value is less than the stable contact condition judgment threshold and greater than or equal to the unstable contact condition judgment threshold, it is marked as the second state level. The second state level indicates that the hook ring is in an unstable contact state, the ferromagnetic fixing object deviates from the center of the hook ring or passes through it at an angle, the load-bearing capacity of the hook ring decreases, and there is a risk of the fixing object slipping off.

[0148] When the first deviation characteristic is less than the threshold for determining an unstable contact condition, it is marked as the third state level. The third state level indicates that the hook ring is in an unloaded state or the change in the magnetic field is insufficient to determine a contact state, the hook ring is not properly suspending the fixed object or the fixed object has not passed through the hook ring.

[0149] It should be noted that the three-value comparison covers all possible values ​​of the first deviation characteristic. The value range of the first deviation characteristic is divided into three intervals by the stable contact condition judgment threshold and the unstable contact condition judgment threshold, with each interval corresponding to a state level. The three state levels completely describe the contact state of the hook ring: stable contact, unstable contact, and no-load or no contact.

[0150] Step 2: Frequency statistics determine the confirmation status level.

[0151] It is understandable that a single measurement may be affected by transient disturbances, leading to incorrect state-level labeling. By performing frequency statistics on the state-level labels of multiple consecutive measurements, occasional interferences can be filtered out, and the true state changes can be identified.

[0152] In this embodiment, frequency statistics are performed on the state level marking results within a consecutive preset number of times. The number of occurrences of the first state level, the second state level, and the third state level are counted. The state level with the highest statistical frequency is selected as the confirmation state level.

[0153] It should be noted that the preset number of measurements is determined as follows: the number of measurements per unit time is calculated based on a preset time interval, and the minimum number of measurements required is determined based on the shortest duration of the hook ring state change. The preset number of measurements is the smaller of the minimum number of measurements and the number of measurements per unit time. The preset number of measurements must ensure that sufficient measurement samples can be collected within the duration of the state change, while avoiding excessively long statistical windows that could lead to response delays.

[0154] It's easy to understand that the working mechanism of frequency statistics is as follows: when a real change occurs in the loop's state, the state level labels for multiple consecutive measurements are all the new state level after the change. This new state level dominates the statistical frequency and is selected as the confirmed state level. When a measurement is affected by transient interference, causing an incorrect state level label, since the interference only affects one or a few measurements, the erroneous state level accounts for a low proportion of the statistical frequency and will not be selected as the confirmed state level. Frequency statistics achieve the suppression of transient interference and the identification of continuous state changes.

[0155] Step 3: Output an alarm signal based on the confirmed status level.

[0156] Understandably, the actual contact status of the hook ring is determined based on the confirmed status level, and a corresponding alarm signal is output.

[0157] In this embodiment, when the confirmed state level is the first state level, it is determined that the hook ring is in a stable contact state, the hook ring is working normally, the monitoring state is maintained, and no alarm signal is output.

[0158] When the status level is confirmed to be Level 2, the hook ring is determined to be in an unstable contact state, posing a safety hazard, and a warning alarm signal is output. This warning alarm signal is output via an audible and visual alarm to remind the operator to adjust the connection between the hook ring and the fixed object, ensuring the fixed object passes perpendicularly through the center of the hook ring.

[0159] When the status level is confirmed to be level three, it is determined that the hook ring is not properly suspending the fixed object or the fixed object has not passed through the hook ring, the hook ring loses its protective function, and a hazard level alarm signal is output. The hazard level alarm signal is output through an audible and visual alarm, and at the same time, an alarm message is sent to the monitoring center through a wireless communication module, requiring the workers to immediately stop work and check the safety belt connection status.

[0160] It should be noted that the three-level alarm mechanism corresponds to three confirmation status levels, covering all working states of the hook ring. Stable contact corresponds to normal operation with no alarm; unstable contact corresponds to a warning level alarm; and no-load or no-contact status corresponds to a danger level alarm. The graded alarm mechanism outputs different response measures according to the degree of danger.

[0161] It should be noted that step S4, by periodically measuring the magnetic field strength inside the hook ring and calculating the deviation characteristic quantity relative to the no-load state, compares the deviation characteristic quantity with the judgment threshold determined in step S3, thus achieving real-time judgment of the three states of stable contact, unstable contact, and no-load of the hook ring. Through the frequency statistics mechanism, the error in the state level marking caused by a single transient interference will not affect the judgment of the confirmed state level, and continuous state changes are identified and corresponding alarm signals are output after multiple consecutive measurements. The graded alarm mechanism outputs different levels of alarm signals according to the degree of danger of the hook ring contact state. The warning level alarm prompts the operator to adjust the connection method, and the danger level alarm requires the operator to stop the operation immediately.

[0162] Example 2 is an embodiment of the present invention. This embodiment provides a safety belt fall protection monitoring system based on a sudden change in the ring magnetic field, including: a magnetic field excitation module, which is disposed on the hook ring and is used to apply an excitation signal according to the excitation parameters to establish an alternating magnetic field; A magnetic field detection module is installed on the hook ring to detect the magnetic field strength inside the hook ring; The parameter calculation module is used to establish mathematical models and determine excitation parameters and decision thresholds; The signal processing module is used to transmit and filter the output signal of the magnetic field detection module; The status determination module is used to calculate the magnetic field deviation, compare it with the determination threshold, and confirm the hook status. The alarm output module is used to output alarm signals based on the hook status.

[0163] This embodiment also provides an electronic device applicable to a seatbelt fall protection monitoring method based on a sudden change in a ring magnetic field, comprising: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the seatbelt fall protection monitoring method based on a sudden change in a ring magnetic field as proposed in the above embodiment.

[0164] This embodiment also provides a storage medium storing a computer program that, when executed by a processor, implements a safety belt fall protection monitoring method based on a sudden change in the annular magnetic field as proposed in the above embodiment.

[0165] The storage medium proposed in this embodiment belongs to the same inventive concept as the method for monitoring and protecting a safety belt from falling based on a sudden change in a ring magnetic field proposed in the above embodiments. Technical details not described in detail in this embodiment can be found in the above embodiments, and this embodiment has the same beneficial effects as the above embodiments.

[0166] Based on the above description of the implementation methods, those skilled in the art can clearly understand that the present invention can be implemented using software and necessary general-purpose hardware, and of course, it can also be implemented using hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk, or optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of the various embodiments of the present invention.

[0167] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A safety belt fall protection monitoring method based on abrupt changes in a circular magnetic field, characterized in that, include: A first mathematical model is established to characterize the electromagnetic coupling relationship between the excitation unit and the detection unit, and a first set of parameters is determined through the first mathematical model. A first functional unit and a second functional unit are provided in the hook ring. A first excitation signal is applied to the first functional unit according to the first parameter set. Under the first working condition, the first magnetic field characteristic value is obtained by measuring through the second functional unit. A second mathematical model is established, which includes the geometric structure of the hook ring and the physical properties of the ferromagnetic medium. The second magnetic field feature value at the detection location is calculated using the second mathematical model under different ferromagnetic medium intrusion conditions. The second magnetic field feature value is compared with the first magnetic field feature value to obtain a first correlation rule. A first threshold set is determined based on the first correlation rule. A second excitation signal is applied to the first functional unit, and a third magnetic field characteristic value is obtained by periodic measurement through the second functional unit. A first deviation characteristic quantity between the third magnetic field characteristic value and the first magnetic field characteristic value is calculated, and the first deviation characteristic quantity is compared with the first threshold set to determine the hooking state.

2. The safety belt fall protection monitoring method based on abrupt changes in the annular magnetic field as described in claim 1, characterized in that, The first mathematical model for establishing the electromagnetic coupling relationship between the excitation unit and the detection unit includes: establishing a first mathematical relationship between the current of the excitation unit and the magnetic field strength inside the hook ring; Establish a second mathematical relationship between the magnetic field strength inside the hook ring and the magnetic field induced by the detection probe; Establish a third mathematical relationship between the magnetic field induced by the detection probe and the electrical signal output by the detection unit; The first mathematical model is formed by cascading and coupling the first mathematical relation, the second mathematical relation, and the third mathematical relation.

3. The safety belt fall protection monitoring method based on abrupt changes in the annular magnetic field as described in claim 2, characterized in that, Determining the first parameter set through the first mathematical model includes: The excitation unit structural parameters and excitation signal parameters are used as parameters to be analyzed. The dual-state response characteristics of the parameters to be analyzed under no-load and ferromagnetic medium intrusion states are calculated using the first mathematical model. Sensitivity quantification analysis is performed on the dual-state response characteristics to obtain the sensitivity value of the parameter to be analyzed; The parameters are ranked according to their sensitivity values, and those whose sensitivity values ​​exceed a preset threshold are selected as parameters to be optimized. Establish no-load detectable constraints and intrusion distinguishable constraints for the parameters to be optimized, and perform multi-parameter joint optimization under the constraints to obtain the parameter feasible region; The range of values ​​for the parameter to be optimized is extracted from the feasible domain of the parameter and used as the first parameter set.

4. The safety belt fall protection monitoring method based on abrupt changes in the annular magnetic field as described in claim 3, characterized in that, Determining the first threshold set based on the first association rule includes: Numerical solutions were performed on multiple sets of values ​​for the relative permeability and spatial location of the ferromagnetic medium to obtain a simulation dataset of magnetic field strength. The magnetic field strength simulation dataset is divided into stable contact condition data and unstable contact condition data based on spatial location. Calculate the ratio of the two operating condition data to the first magnetic field characteristic value to obtain the change factor data of the stable contact condition and the change factor data of the unstable contact condition, respectively; A regression relationship between the aforementioned change factor data and relative permeability and spatial location is established. Based on the regression relationship and the change factor data, the threshold values ​​for determining stable contact conditions and unstable contact conditions are determined as the first set of threshold values.

5. The safety belt fall protection monitoring method based on abrupt changes in the annular magnetic field as described in claim 4, characterized in that, The determination of the stable contact condition threshold and the unstable contact condition threshold based on the regression relationship and the change factor data includes: Statistical analysis was performed on the change factor data of the stable contact condition and the change factor data of the unstable contact condition to obtain the distribution characteristic parameters; The safety margin coefficients are determined based on the regression relationship and the distribution characteristic parameters, respectively. Extract the extreme values ​​from the change factor data respectively; The decision threshold is obtained by applying a safety margin coefficient to the extreme values.

6. The safety belt fall protection monitoring method based on abrupt changes in the annular magnetic field as described in claim 5, characterized in that, The periodic measurements obtained the third magnetic field characteristic value, including: Magnetic field measurements are triggered according to a preset time interval; Measurement signals are transmitted via impedance matching. Perform digital filtering on the transmitted signal; The amplitude of the filtered signal is extracted as the third magnetic field characteristic value.

7. The safety belt fall protection monitoring method based on abrupt changes in the annular magnetic field as described in claim 6, characterized in that, The determination of the hook status includes: The first deviation feature is compared with the stable contact condition determination threshold and the unstable contact condition determination threshold. When the first deviation feature is greater than or equal to the stable contact condition determination threshold, it is marked as the first state level. When the first deviation feature is less than the stable contact condition determination threshold but greater than or equal to the unstable contact condition determination threshold, it is marked as the second state level. When the first deviation feature is less than the unstable contact condition determination threshold, it is marked as the third state level. The frequency of the state level marking results within a consecutive preset number of times is statistically analyzed, and the state level with the highest statistical frequency is selected as the confirmed state level. When the confirmation status level is the first status level, monitoring is maintained without alarm output; when the confirmation status level is the second status level, a warning level alarm signal is output; and when the confirmation status level is the third status level, a danger level alarm signal is output.

8. A safety belt fall protection monitoring system based on a sudden change in a circular magnetic field, employing the safety belt fall protection monitoring method based on a sudden change in a circular magnetic field as described in any one of claims 1 to 7, characterized in that, include: The magnetic field excitation module, located on the hook ring, is used to apply an excitation signal according to the excitation parameters to establish an alternating magnetic field; A magnetic field detection module is installed on the hook ring to detect the magnetic field strength inside the hook ring; The parameter calculation module is used to establish mathematical models and determine excitation parameters and decision thresholds; The signal processing module is used to transmit and filter the output signal of the magnetic field detection module; The status determination module is used to calculate the magnetic field deviation, compare it with the determination threshold, and confirm the hook status. The alarm output module is used to output alarm signals based on the hook status.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the safety belt fall protection monitoring method based on the sudden change of the annular magnetic field as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the safety belt fall protection monitoring method based on the sudden change of the annular magnetic field as described in any one of claims 1 to 7.

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