Guardrail plate galvanizing thickness closed-loop control method and system based on multi-sensor fusion

Abstract

The invention provides a guardrail plate galvanizing thickness closed-loop control method and system based on multi-sensor fusion, and the method comprises the steps: obtaining dynamic light interference fringe data and continuous conductivity change data of a preset eddy current sensor, and carrying out the coupling correlation; generating fusion data including thickness distribution characteristics and surface reflection characteristics of a zinc coating of the preset guardrail plate; constructing a thickness reflectivity dynamic relation graph based on the fused data; determining a real-time control parameter combination associated with preset galvanization process equipment according to gradient change characteristics of the thickness reflectivity dynamic relation graph; and inputting the real-time control parameter combination into a galvanization process dynamic decision-making unit of the preset galvanization process equipment to generate a closed-loop control instruction. According to the method, the problem of control lag caused by asynchronous detection of the thickness and the surface reflection characteristics in a high-speed continuous galvanizing scene is solved, the technical bottleneck that the surface quality and the internal thickness cannot be correlated in traditional single sensor detection is broken through, and the coating uniformity and the process stability are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of closed-loop control, in particular to a guardrail plate galvanizing thickness closed-loop control method and system based on multi-sensor fusion. BACKGROUND

[0002] With the continuous development of highway infrastructure construction, the corrosion resistance of guardrail plates as the core component of the road safety protection system is directly related to the service life and public safety. In the large-scale continuous hot galvanizing production process, the uniformity and accuracy of the galvanized layer thickness become key quality indicators. Especially in the dynamic production environment, factors such as zinc pot temperature fluctuation, strip speed variation, and air knife parameter drift can easily cause unexpected fluctuations in the coating thickness. To meet the stringent requirements of product quality consistency, process automation, and resource utilization efficiency, it is urgent to achieve high-precision online perception and real-time feedback regulation of the galvanized layer thickness under complex working conditions, ensuring stable control of the coating within the micron-level precision range in high-speed moving production lines, and avoiding material waste caused by over-plating or quality problems caused by under-plating.

[0003] The existing scheme is an online monitoring system based on a single-point eddy current sensor array, which combines a preset process database for feedback regulation. By arranging multiple fixed eddy current sensors before the cooling section, the surface conductivity response signals of the guardrail plate are collected in real time. According to the calibration curve, the local coating thickness value is deduced, and the measurement results are transmitted to the central control system. After comparing with the target thickness, the process parameters such as air knife pressure, gap, and zinc liquid temperature are dynamically adjusted to achieve a certain degree of closed-loop control. The existing scheme has some inherent defects, including relying only on conductivity single physical field information, which makes it difficult to effectively distinguish between coating thickness changes and signal interference caused by base material microstructure, surface oxidation state, or temperature gradient, resulting in thickness inversion results prone to drift and misjudgment. The sensor arrangement is limited by physical space, making it difficult to cover full-width continuous dynamic scanning, resulting in insufficient spatial resolution and inability to capture rapid fluctuation characteristics of the coating in the transverse and longitudinal directions. The system's response to process disturbances relies on a static calibration model, making it difficult to establish a dynamic mapping relationship between thickness and reflection characteristics under non-steady-state and strong interference conditions, leading to feedback regulation lag and decreased accuracy, affecting the timeliness and accuracy of closed-loop control. SUMMARY

[0004] The application provides a guardrail plate galvanizing thickness closed-loop control method and system based on multi-sensor fusion, to solve the problems in the prior art that only relying on a single physical field information of conductivity, it is difficult to effectively distinguish the signal interference caused by the coating thickness variation, substrate microstructure, surface oxidation state or temperature gradient, resulting in the thickness inversion result being prone to drift and misjudgment; the sensor arrangement is limited by the physical space, it is difficult to cover the full-width continuous dynamic scanning, resulting in insufficient spatial resolution and being unable to capture the rapid fluctuation characteristics of the coating in the transverse and longitudinal directions; the system response to process disturbance depends on the static calibration model, it is difficult to establish a dynamic mapping relationship between the thickness and the reflection characteristics under non-steady-state and strong interference conditions, resulting in feedback regulation lag and precision decline, affecting the timeliness and accuracy of closed-loop control, etc.

[0005] In a first aspect, the application provides a guardrail plate galvanizing thickness closed-loop control method based on multi-sensor fusion, comprising:

[0006] Obtaining dynamic light interference fringe data and continuous conductivity change data generated by a preset eddy current sensor during movement of a preset guardrail plate;

[0007] Coupling and correlating the dynamic light interference fringe data and the continuous conductivity change data to generate fusion data containing the thickness distribution characteristics of the galvanizing layer of the preset guardrail plate and the surface reflection characteristics of the galvanizing layer;

[0008] Based on the light intensity distribution pattern of the fusion data, a thickness reflectivity dynamic relationship map is constructed;

[0009] According to the gradient change characteristics of the thickness reflectivity dynamic relationship map, a real-time control parameter combination associated with a preset galvanizing process equipment is determined;

[0010] The real-time control parameter combination is input into a galvanizing process dynamic decision unit of the preset galvanizing process equipment to generate a closed-loop control instruction.

[0011] Optionally, obtaining dynamic light interference fringe data and continuous conductivity change data generated by a preset eddy current sensor during movement of a preset guardrail plate, comprises:

[0012] Scanning the preset guardrail plate during movement using a preset optical interferometer to obtain an original sequence of light interference fringes;

[0013] Conductivity detection of the preset guardrail plate during movement is performed using a preset eddy current sensor to generate a continuous conductivity signal stream;

[0014] Fringe morphology separation processing is performed on the original sequence of light interference fringes to obtain a light intensity distribution sequence;

[0015] The continuous conductivity signal stream is subjected to moving region segmentation processing to generate conductivity change data segments corresponding to the galvanized layer of the preset guardrail plate;

[0016] The light intensity distribution sequence is time-stamped and bound to the conductivity change data segments to generate dynamic light interference fringe data and continuous conductivity change data.

[0017] Optionally, the dynamic light interference fringe data and the continuous conductivity change data are coupled and associated to generate fusion data containing thickness distribution characteristics of the galvanized layer of the preset guardrail plate and surface reflection characteristics of the galvanized layer, including:

[0018] The interference fringe morphology in the dynamic light interference fringe data is decomposed to obtain surface reflection characteristics of the galvanized layer of the preset guardrail plate and light intensity distribution characteristics corresponding to the surface reflection characteristics;

[0019] The signal response data of the continuous conductivity change data in the movement process of the preset guardrail plate is analyzed to obtain thickness distribution characteristics of the galvanized layer and a conductivity change gradient sequence corresponding to the thickness distribution characteristics;

[0020] The light intensity distribution characteristics and the conductivity change gradient sequence are spatially associated and matched to establish a physical field coordination factor between the surface thickness and the internal thickness of the galvanized layer;

[0021] Based on the physical field coordination factor, the light intensity distribution characteristics and the conductivity change gradient sequence are subjected to bidirectional coupling processing to generate fusion data containing the surface reflection characteristics and the thickness distribution characteristics.

[0022] Optionally, based on the light intensity distribution pattern of the fusion data, a thickness reflectivity dynamic relationship map is constructed, including:

[0023] The light intensity distribution pattern in the fusion data is converted into a frequency domain distribution pattern to generate a light intensity frequency domain distribution characteristic;

[0024] The dominant fluctuation component corresponding to the surface reflection characteristics is separated from the energy principal component of the light intensity frequency domain distribution characteristic;

[0025] The dominant fluctuation component is subjected to time-domain coupling operation with the thickness distribution characteristics to generate a thickness reflectivity coordinated change sequence;

[0026] Based on the evolution law of the thickness reflectivity coordinated change sequence at consecutive time nodes, a thickness reflectivity dynamic relationship map is constructed.

[0027] Optionally, the dominant wave component is coupled with the thickness distribution feature in time domain to generate a thickness reflectivity collaborative change sequence, including:

[0028] The phase fluctuation signal of the dominant wave component is demodulated to obtain a physical fluctuation characteristic of the surface reflection characteristic;

[0029] The response delay of the thickness distribution feature during the movement of the pre-set guardrail is compensated to generate a time delay corrected thickness feature;

[0030] The physical fluctuation characteristic and the dynamic trajectory of the time delay corrected thickness feature are matched to construct a thickness reflectivity physical field synchronization relationship;

[0031] Based on the thickness reflectivity physical field synchronization relationship, the physical fluctuation characteristic and the time delay corrected thickness feature are fused and encoded to generate a thickness reflectivity collaborative change sequence.

[0032] Optionally, according to the gradient change characteristic of the thickness reflectivity dynamic relationship map, a real-time control parameter combination associated with the pre-set galvanizing process equipment is determined, including:

[0033] According to the gradient jump region in the thickness reflectivity dynamic relationship map, an abnormal interval of collaborative change between the thickness of the galvanized layer and the reflectivity of the galvanized layer is located;

[0034] The gradient direction distribution characteristic in the abnormal interval is quantitatively processed to generate a physical field deviation trend;

[0035] Based on the parameter conversion benchmark of the pre-set galvanizing process equipment, the physical field deviation trend is regularized and converted to generate a basic control parameter set;

[0036] The basic control parameter set and the running state parameter of the pre-set galvanizing process equipment are integrated to generate a real-time control parameter combination associated with the pre-set galvanizing process equipment.

[0037] Optionally, the real-time control parameter combination is input into a galvanizing process dynamic decision unit of the pre-set galvanizing process equipment to generate a closed-loop control instruction, including:

[0038] The multi-dimensional control parameters in the real-time control parameter combination are serialized to generate a control parameter sequence of the galvanized layer;

[0039] Instruction elements executable by the pre-set galvanizing process equipment are extracted from the control parameter sequence to generate a physical control instruction set;

[0040] The real-time running constraint parameters of the preset galvanizing process equipment and the physical control instruction set are subjected to physical constraint adaptation processing to generate equipment compatible instructions.

[0041] The equipment compatible instructions and decision rules of a galvanizing process dynamic decision unit of the preset galvanizing process equipment are integrated to generate closed-loop control instructions.

[0042] In a second aspect, the present application provides a guardrail plate galvanizing thickness closed-loop control system based on multi-sensor fusion, comprising:

[0043] An acquisition module is configured to acquire dynamic light interference fringe data and continuous conductivity change data generated by a preset eddy current sensor during movement at a preset guardrail plate;

[0044] A coupling module is configured to couple and correlate the dynamic light interference fringe data and the continuous conductivity change data to generate fusion data containing thickness distribution characteristics of a galvanizing layer of the preset guardrail plate and surface reflection characteristics of the galvanizing layer;

[0045] A construction module is configured to construct a thickness reflectivity dynamic relationship graph based on a light intensity distribution mode of the fusion data;

[0046] A determination module is configured to determine a real-time control parameter combination associated with a preset galvanizing process equipment according to gradient change characteristics of the thickness reflectivity dynamic relationship graph;

[0047] A generation module is configured to input the real-time control parameter combination into a galvanizing process dynamic decision unit of the preset galvanizing process equipment to generate closed-loop control instructions.

[0048] In a third aspect, the present application provides a computing device comprising a processor and a memory, wherein the memory stores a computer program, and the processor is configured to run the computer program to execute the multi-sensor fusion based guardrail plate galvanizing thickness closed-loop control method of any one of the first aspect.

[0049] In a fourth aspect, the present application provides a computer storage medium storing computer program instructions, wherein the computer program instructions are executed by a processor to implement the multi-sensor fusion based guardrail plate galvanizing thickness closed-loop control method of any one of the first aspect.

[0050] The application solves the control lag problem caused by asynchronous detection of thickness and surface reflection characteristics in the high-speed continuous galvanizing scene by multi-sensor dynamic data coupling and physical field collaborative modeling. The device correlation control parameters are generated by using the gradient characteristics of the thickness reflectivity dynamic relationship graph, realizing real-time adaptive adjustment of the galvanizing process to the guardrail plate movement process, breaking through the technical bottleneck that traditional single-sensor detection cannot correlate surface quality and internal thickness, and significantly improving the uniformity of the plated layer and the process stability.

[0051] Further, by light intensity frequency domain principal component separation and time domain physical coupling of thickness characteristics, the collaborative law distortion caused by optical interference and eddy current delay in the dynamic galvanizing environment is eliminated. Based on the evolution law of the thickness reflectivity collaborative change sequence, a dynamic graph is constructed, high-frequency fluctuation noise is converted into a quantifiable control physical field evolution trajectory, the surface reflection characteristics and thickness change misalignment problem of high-speed moving guardrails is solved, and high-fidelity plated layer quality evolution basis is provided for closed-loop control.

[0052] These aspects or other aspects of the application will be more apparent in the following description of the embodiments. BRIEF DESCRIPTION OF DRAWINGS

[0053] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiment or prior art description. Obviously, the drawings in the following description are some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor.

[0054] Figure 1 A flow chart of a guardrail plate galvanizing thickness closed-loop control method based on multi-sensor fusion provided by the embodiment of the present application is provided.

[0055] Figure 2 A structural schematic diagram of a guardrail plate galvanizing thickness closed-loop control system based on multi-sensor fusion provided by the embodiment of the present application is provided.

[0056] Figure 3 A structural schematic diagram of a computing device provided by the embodiment of the present application is provided. DETAILED DESCRIPTION

[0057] In order to make the person skilled in the art better understand the present application, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application.

[0058] In some of the flowcharts described in the specification and claims of the present application and in the above-mentioned drawings, a plurality of operations are included which occur in a particular order, but it should be clearly understood that these operations can be performed in the order in which they appear herein or in parallel, and the serial numbers of the operations, such as 101, 102, etc., are merely used to distinguish different operations, and the serial numbers themselves do not represent any execution order. In addition, these flowcharts can include more or fewer operations, and the operations can be performed in sequence or in parallel. It should be noted that the descriptions herein, such as "first", "second", etc., are used to distinguish different messages, devices, modules, etc., and do not represent the order of precedence, nor do "first" and "second" represent different types.

[0059] The technical solutions in the embodiments of the present application will be described clearly and completely below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0060] Figure 1 A flowchart of a guardrail plate galvanizing thickness closed-loop control method based on multi-sensor fusion is provided for the embodiments of the present application, as shown in Figure 1 , which comprises:

[0061] In the real-time galvanizing dynamic environment scenario, the prior art has three fundamental defects: first, single-sensor detection cannot synchronously capture the internal thickness and surface reflection characteristics of the galvanizing layer, resulting in a disconnection between thickness control and surface quality; second, the vortex response delay caused by the high-speed moving guardrail plate and the optical interference cause asynchronous data, making the control decision lag behind the actual process changes; third, the experience threshold control mode is difficult to adapt to the dynamic fluctuations of the galvanizing bath parameters, and frequent local over-plating or under-plating defects occur. To address these issues, the research and development idea of the present application is as follows: first, through a multi-source physical quantity dynamic coupling mechanism, the vortex conductivity change data and the optical interference fringe data are cross-modally correlated to generate a fusion data carrier of thickness and surface reflection characteristics; then, based on the light intensity distribution pattern, a thickness reflectivity dynamic relationship map is constructed to convert the internal coordination law of the plated layer quality into quantifiable gradient features; finally, a control parameter combination bound to the real-time state of the galvanizing equipment is generated according to the gradient change features, and the closed-loop control of the uniformity of the plated layer is realized through the adaptive adjustment of the decision unit. This scheme breaks through the technical barriers of traditional detection and control in the dynamic galvanizing environment, ensuring the coordinated optimization of thickness control and surface quality in the high-speed continuous production scenario. Based on this, the present application provides a guardrail plate galvanizing thickness closed-loop control method based on multi-sensor fusion, as shown in Figure 1 , comprising:

[0062] Step 101: acquiring dynamic light interference fringe data and continuous conductivity change data generated by a preset eddy current sensor during movement of the preset guardrail at a preset guardrail plate.

[0063] In this step, the dynamic light interference fringe data refers to a sequence of light and dark alternating fringes formed by scanning the reflection light of the galvanized layer surface by an optical interferometer, including fringe spacing, intensity and phase information, for characterizing the flatness and reflectivity variation of the galvanized layer surface; the continuous conductivity change data refers to the electromagnetic induction signal stream detected by the preset eddy current sensor during movement of the guardrail plate, including the amplitude and timing fluctuation characteristics of the conductivity, for inverting the thickness distribution of the galvanized layer.

[0064] In the embodiment of the present application, first, the preset guardrail surface in movement is continuously scanned by an optical interferometer to capture the dynamic light interference fringe data reflecting the reflection characteristics of the galvanized layer surface; at the same time, the preset eddy current sensor is triggered to detect along the movement trajectory of the guardrail plate to generate continuous conductivity change data characterizing the thickness distribution of the galvanized layer inside.

[0065] Step 102: coupling and correlating the dynamic light interference fringe data and the continuous conductivity change data to generate fusion data containing the thickness distribution characteristics of the galvanized layer of the preset guardrail plate and the surface reflection characteristics of the galvanized layer.

[0066] In this step, the coupling and correlation operation refers to the process of physically field binding the optical fringe data and the conductivity data according to the spatial position and time reference, including the construction of the light intensity-conductivity mapping model and the cross-modal feature alignment; the thickness distribution characteristics refer to the spatial distribution law of the galvanized layer thickness value on the guardrail surface parsed from the conductivity data, including the thickness gradient, the extreme point position and the area uniformity index; the surface reflection characteristics refer to the reflection ability parameters of the galvanized layer surface to light waves extracted from the light interference fringes, including the reflection intensity main frequency, the scattering rate and the interference phase angle; the fusion data refer to the multi-source data set formed by integrating the thickness distribution characteristics and the surface reflection characteristics, including the thickness-reflectivity correlation matrix bound by the spatial coordinates and the time evolution sequence.

[0067] In the embodiment of the present application, first, the dynamic light interference fringe data and the continuous conductivity change data are cross-modal physical field binding operations; second, the surface reflection characteristics in the light interference data and the thickness distribution characteristics in the eddy current data are extracted through the coupling and correlation operation; finally, the fusion data containing the surface and internal quality state of the galvanized layer are generated by fusing the two physical characteristics.

[0068] Step 103: constructing a thickness-reflectivity dynamic relationship graph based on the light intensity distribution pattern of the fusion data.

[0069] In this step, the light intensity distribution pattern refers to the distribution of the energy of the optical components in the frequency and spatial domains of the fused data, including the main peak frequency, harmonic components, and energy attenuation slope; the thickness reflectivity dynamic relationship spectrum refers to a two-dimensional spatiotemporal model constructed based on the light intensity pattern, with the horizontal axis representing time nodes, the vertical axis representing the thickness reflectivity coupling strength, and the contour lines representing the cooperative change trend.

[0070] In this embodiment of the invention, the light intensity distribution pattern of the fused data is first analyzed; then the light intensity pattern is decomposed into frequency domain energy components; next, the principal energy component that dominates the surface reflection fluctuation of the galvanized layer is extracted; finally, a dynamic relationship spectrum of thickness and reflectivity that reflects the synergistic evolution of thickness and reflectivity is constructed.

[0071] Step 104: Based on the gradient change characteristics of the thickness reflectivity dynamic relationship spectrum, determine the combination of real-time control parameters associated with the preset galvanizing process equipment.

[0072] In this step, the gradient change feature refers to the set of extreme points of the rate of change of adjacent regions in the dynamic relationship graph of thickness reflectivity, including the gradient direction angle, jump amplitude and abrupt change area; the real-time control parameter combination refers to the set of adjustment instructions for current density, plating solution flow rate and conveyor belt speed linked with the galvanizing process equipment, and the parameter values ​​are generated based on the gradient feature and the physical limits of the equipment.

[0073] In this embodiment of the invention, firstly, the gradient change characteristics in the dynamic relationship spectrum of thickness reflectivity are identified; secondly, the abnormal intervals of the gradient jump region are located; then, the physical field offset corresponding to the gradient direction is quantified; finally, a combination of real-time control parameters linked with the current, temperature, and flow parameters of the preset galvanizing process equipment is generated.

[0074] Step 105: Input the real-time control parameter combination into the galvanizing process dynamic decision unit of the preset galvanizing process equipment to generate closed-loop control commands.

[0075] In this step, the dynamic decision-making unit for the galvanizing process refers to the adaptive controller pre-installed in the galvanizing equipment, including a process rule library, a real-time constraint detection module, and an instruction optimization engine; the closed-loop control instructions refer to the set of galvanizing tank voltage correction, zinc liquid pump flow regulation value, and guardrail conveying speed control signals output by the decision-making unit.

[0076] In this embodiment of the invention, the real-time control parameters are first serialized into equipment-parseable instructions; then, the equipment operation constraint rules are matched through the dynamic decision-making unit of the galvanizing process; finally, closed-loop control instructions for adjusting the voltage of the galvanizing tank and the conveying speed are output.

[0077] For example, firstly, a high-speed optical interferometer scans the surface of a pre-set guardrail moving at a speed of three meters per minute, capturing dynamic optical interference fringe data; simultaneously, a pre-set eddy current sensor detects continuous conductivity changes five millimeters below the guardrail. Next, the two data sets are input into a coupling processor, and the peak points of light intensity and conductivity are matched according to spatial coordinates to generate fused data that integrates thickness distribution characteristics and surface reflectivity. Then, the dominant frequency component of the light intensity distribution in the fused data is extracted to construct a dynamic relationship map reflecting the co-evolution of thickness and reflectivity. Subsequently, abrupt transition regions with gradients greater than 10 degrees per millisecond are identified in the map, generating a real-time control parameter combination including a current adjustment of ±50 amperes and a zinc bath flow rate increase / decrease of 10 liters per minute. Finally, the galvanizing equipment decision unit matches the constraint rule of a galvanizing bath temperature limit of 300 degrees Celsius, outputting a closed-loop control command with an adjustment voltage of ±5 volts and a conveying speed increase / decrease of 0.5 meters per minute to the galvanizing tank actuator.

[0078] This invention establishes a physical field collaborative model of thickness and surface reflection through multi-sensor dynamic coupling, transforming optical interference fringes and eddy current conductivity data into a dynamic relationship spectrum of thickness and reflectivity. Based on the spectrum gradient features, control parameters linked with the galvanizing equipment are generated, enabling the coating thickness control to synchronously respond to changes in surface quality. The decision unit outputs closed-loop commands through real-time constraint adaptation, solving the combined defects of thickness detection delay and surface quality loss control in high-speed moving scenarios, thereby improving galvanizing uniformity and suppressing process fluctuations.

[0079] To address the fusion distortion issue caused by asynchronous multi-source data acquisition in high-speed moving scenarios, this step employs a spatiotemporal synchronization mechanism between optical scanning and eddy current detection to ensure real-time matching between dynamic optical interference fringes and conductivity data. This invention provides a specific embodiment: Step 101, acquiring dynamic optical interference fringe data and continuous conductivity change data generated by a pre-set eddy current sensor during its movement at a pre-set guardrail, specifically includes the following steps:

[0080] Step 111: Use a preset optical interferometer to scan the preset guardrail during the movement to obtain the original sequence of optical interference fringes.

[0081] In this step, the original sequence of optical interference fringes refers to the set of original interference images generated by the optical interferometer scanning the surface of the zinc plating layer, including the fringe spacing, brightness, and phase angle data arranged in chronological order, which is used to characterize the interference state of the reflected light waves.

[0082] In this embodiment of the invention, a preset optical interferometer is first activated to continuously scan the surface of the moving preset guardrail; then, the alternating bright and dark fringes formed by reflected light waves are captured by the interference lens; finally, the original sequence of optical interference fringes arranged in chronological order is generated.

[0083] Step 112: Use a preset eddy current sensor to detect the conductivity of the preset guardrail during the movement process and generate a continuous conductivity signal stream.

[0084] In this step, conductivity refers to the response intensity parameter of zinc ions in the zinc plating layer to the electromagnetic field of the eddy current sensor, including the amplitude of the induced current and the phase delay, reflecting the thickness and density of the metal plating layer; continuous conductivity signal stream refers to the uninterrupted conductivity time-series signal output by the eddy current sensor, including the amplitude fluctuation curve and frequency response characteristics sampled at the millisecond level.

[0085] In this embodiment of the invention, a preset eddy current sensor is first triggered to perform electromagnetic induction detection along the moving direction of the guardrail; secondly, the conductivity change signal generated by metal ions inside the galvanized layer is collected; and finally, a continuous and uninterrupted conductivity signal stream is output.

[0086] Step 113: Perform fringe morphology separation processing on the original sequence of optical interference fringes to obtain the light intensity distribution sequence.

[0087] In this step, fringe morphology separation processing refers to the technical operation of separating effective fringes from noise from the original interference sequence, including fringe edge recognition, background light intensity subtraction, and effective signal enhancement; light intensity distribution sequence refers to the reflected light intensity dataset formed after processing, including the intensity value matrix corresponding to spatial coordinates and the time series variation curve.

[0088] In this embodiment of the invention, the fringe spacing and phase distribution in the original sequence of optical interference fringes are first identified; then the background noise and the effective interference signal are separated; and finally, the light intensity distribution sequence reflecting the surface reflection characteristics is extracted.

[0089] Step 114: Perform moving region segmentation processing on the continuous conductivity signal stream to generate conductivity change data segments corresponding to the galvanized layer of the preset guardrail.

[0090] In this step, the moving area refers to the effective detection range of the pre-set guardrail plate moving on the galvanizing tank conveyor belt, including the position interval in the length direction and the scanning coverage area in the width direction; the moving area segmentation process refers to the process of segmenting the conductivity signal according to the spatial movement trajectory of the guardrail plate, including position coordinate mapping, movement speed compensation and area boundary calibration; the conductivity change data segment refers to the conductivity dataset bound to the spatial position of the galvanized area of ​​the guardrail plate, including the starting or ending position mark and the gradient distribution inside the area.

[0091] In this embodiment of the invention, the detection area is first divided according to the preset movement trajectory of the guardrail; then, the signal segment corresponding to the position of the galvanized layer is extracted; finally, the conductivity change data segment bound to the spatial position is generated.

[0092] Step 115: Timestamp-align and bind the light intensity distribution sequence with the conductivity change data segment to generate dynamic light interference fringe data and continuous conductivity change data.

[0093] In this step, the timestamp alignment binding operation refers to the technique of matching data from different sensors according to a unified clock reference, including timestamp interpolation correction, sampling rate synchronization, and data point mapping.

[0094] In this embodiment of the invention, the acquisition timestamps of the light intensity distribution sequence and the conductivity change data segment are first marked; then, the two data streams are aligned according to the same time base; finally, synchronized dynamic light interference fringe data and continuous conductivity change data are generated by binding.

[0095] This invention addresses the asynchronous data acquisition problem in high-speed moving scenarios through a spatiotemporal synchronization mechanism of optical scanning and eddy current detection; it extracts pure reflection characteristics through stripe morphology separation and achieves spatial positioning of thickness data through moving region segmentation; and it ensures physical field alignment of multi-source data through timestamp binding, providing high-precision input for the coordinated control of thickness and surface reflection.

[0096] To improve the accuracy of the physical field coordination between surface reflection characteristics and internal thickness distribution, this step achieves dual-thickness data fusion through interference fringe decomposition and conductivity gradient analysis, eliminating the feature fragmentation defect in traditional detection. This invention provides a specific embodiment: Step 102, coupling and correlating the dynamic optical interference fringe data and the continuous conductivity variation data to generate fused data containing the thickness distribution characteristics of the galvanized layer of the pre-set guardrail and the surface reflection characteristics of the galvanized layer, specifically including the following steps:

[0097] Step 201: Decompose the interference fringe morphology in the dynamic optical interference fringe data to obtain the surface reflection characteristics of the galvanized layer of the pre-set guardrail and the light intensity distribution characteristics corresponding to the surface reflection characteristics.

[0098] In this step, the interference fringe morphology refers to the physical distribution of the optical interference fringes, including the spacing between bright and dark fringes, phase angle offset, and intensity attenuation slope, reflecting the microscopic undulations and reflectivity differences of the galvanized layer surface; the decomposition operation refers to the technical process of separating the original interference signal into effective components and noise, including fringe edge detection, background light intensity subtraction, and effective signal reconstruction, used to extract pure surface reflection information; the light intensity distribution characteristics refer to the spatial distribution dataset of reflected light intensity formed after decomposition, including the intensity value matrix corresponding to the two-dimensional coordinate points and the time series variation characteristics, directly characterizing the surface quality state of the coating.

[0099] In this embodiment of the invention, firstly, the morphological features of interference fringes in dynamic optical interference fringe data are identified, including the fringe spacing distribution and phase offset; secondly, background scattering noise and effective reflection signals are separated through decomposition operations; finally, the light intensity distribution features reflecting the reflective properties of the galvanized layer surface are extracted, which include an intensity value matrix corresponding to spatial coordinates.

[0100] Step 202: Analyze the signal response data of the continuous conductivity change data during the movement of the pre-installed guardrail to obtain the thickness distribution characteristics of the galvanized layer and the conductivity change gradient sequence corresponding to the thickness distribution characteristics.

[0101] In this step, the signal response data refers to the raw electromagnetic induction signal output by the eddy current sensor when detecting the zinc coating, including current amplitude, phase delay, and frequency response spectrum, reflecting the dynamic response of metal ion distribution to the alternating magnetic field; the analytical operation refers to the technical process of extracting thickness-related parameters from the signal response data, including gradient calculation, extreme point location, and regional uniformity assessment, used to invert the thickness distribution law inside the coating; the conductivity change gradient sequence refers to the conductivity change rate dataset arranged in spatial order, including the amplitude change and directional derivative within a unit movement distance, quantifying the local abrupt changes in thickness distribution.

[0102] In this embodiment of the invention, firstly, signal response data of continuous conductivity change data during the movement of the guardrail is acquired, including the amplitude fluctuation and time sequence change curve of the induced current; secondly, the conductivity change gradient is calculated through analytical operations, including the amplitude change rate within a unit distance; finally, a conductivity change gradient sequence characterizing the thickness distribution is generated, which strictly corresponds to the spatial position of the galvanized layer.

[0103] Step 203: Spatial location correlation matching is performed between the light intensity distribution characteristics and the conductivity change gradient sequence to establish the physical field synergy factor between the surface thickness and internal thickness of the galvanized layer.

[0104] In this step, spatial location association matching refers to the technique of binding different physical quantity data according to the same spatial coordinates, including coordinate mapping transformation, motion trajectory compensation, and position point alignment, to ensure the spatial consistency of surface and internal data; surface thickness refers to the apparent thickness parameter of the zinc coating calculated based on optical interference characteristics, including the equivalent thickness value converted from the reflected optical path difference and the surface undulation correction amount; internal thickness refers to the actual coating thickness parameter inverted from eddy current conductivity data, including the zinc layer deposition amount corresponding to the electromagnetic induction depth and the compactness compensation value; physical field synergy factor refers to the quantitative parameter characterizing the synergistic change law of surface thickness and internal thickness, including the coupling strength coefficient of spatial position point, the angle of change direction, and the time series correlation index.

[0105] In this embodiment of the invention, firstly, the light intensity distribution characteristics and the conductivity change gradient sequence are aligned with the preset guardrail surface coordinates; secondly, the surface thickness reflection value and the internal thickness sensing value at the same location are matched; finally, a physical field coordination factor reflecting the intensity of the coordinated change of surface thickness and internal thickness is established, which includes a coupling strength coefficient and a time sequence evolution marker.

[0106] Step 204: Based on the physical field synergy factor, perform bidirectional coupling processing on the light intensity distribution characteristics and the conductivity change gradient sequence to generate fused data containing the surface reflection characteristics and the thickness distribution characteristics.

[0107] In this step, bidirectional coupling processing refers to the fusion operation that simultaneously applies to the light intensity characteristics and conductivity sequence, including reflectivity weighting, thickness sensitivity compensation, and data reconstruction under physical field constraints.

[0108] In this embodiment of the invention, firstly, reflectivity weighting is applied to the light intensity distribution characteristics based on the physical field synergy factor; secondly, thickness sensitivity compensation is performed on the conductivity change gradient sequence; finally, the spatiotemporal evolution laws of the two physical quantities are fused through bidirectional coupling processing to generate a fused data entity that simultaneously contains surface reflection characteristics and thickness distribution characteristics.

[0109] This invention achieves the separation of physical quantities of surface and internal thickness through interference fringe decomposition and conductivity gradient analysis; establishes a dual thickness synergy factor through spatial position matching; and integrates reflection characteristics and thickness distribution through bidirectional coupling processing, solving the problem of fragmented surface quality and internal thickness detection data in high-speed galvanizing scenarios, and providing accurate holographic data of coating status for closed-loop control.

[0110] To address the issue of inaccurate correlation between thickness and reflectivity under high-frequency noise interference, this step constructs a dynamic evolution map of coating quality through frequency domain principal component separation and temporal physical coupling. A specific embodiment of this invention is provided: Step 103, based on the light intensity distribution pattern of the fused data, constructs a dynamic relationship map of thickness and reflectivity, specifically including the following steps:

[0111] Step 301: Convert the light intensity distribution pattern in the fused data into a frequency domain distribution pattern to generate light intensity frequency domain distribution features.

[0112] In this step, the conversion operation refers to the technical process of transforming the spatial distribution data of light intensity into a frequency domain representation, including signal decomposition into fundamental frequency components, energy spectrum calculation, and harmonic extraction, which are used to reveal the hidden frequency characteristics of optical reflection; the frequency domain distribution pattern refers to the distribution state of light intensity energy in the frequency dimension, including the position of the main peak frequency, the proportion of the secondary peak energy, and the bandwidth parameter, reflecting the periodic fluctuation law of the reflective characteristics of the galvanized layer; the light intensity frequency domain distribution characteristics refer to the quantized dataset formed by the frequency domain conversion, which includes the fundamental frequency amplitude, the third harmonic energy, and the bandwidth attenuation coefficient, and is used to characterize the spectral characteristics of the reflected light wave.

[0113] In this embodiment of the invention, firstly, a conversion operation is performed on the light intensity distribution pattern in the fused data to decompose the spatial domain light intensity signal into frequency components; secondly, the energy distribution state of each frequency component is calculated; and finally, a light intensity frequency domain distribution feature containing the dominant frequency energy and harmonic characteristics is generated.

[0114] Step 302: Separate the dominant wave component corresponding to the surface reflection characteristics from the principal energy component of the frequency domain distribution characteristics of the light intensity.

[0115] In this step, the principal energy component refers to the set of core frequency bands in the frequency domain characteristics whose energy accumulation exceeds a preset proportion, including the center frequency, bandwidth and energy density value of the principal frequency band, which dominates the evolution of the coating reflection characteristics; the dominant fluctuation component refers to the specific frequency band signal separated from the principal energy component, which includes the core frequency, phase angle and timing amplitude of the surface reflection fluctuation, and directly drives the change in coating quality.

[0116] In this embodiment of the invention, firstly, frequency bands in the frequency domain distribution characteristics of light intensity with an energy proportion exceeding a set threshold are identified as the principal energy components; secondly, specific frequency bands related to the surface reflection fluctuations of the galvanized layer are separated from the principal energy components; finally, the dominant fluctuation component characterizing the dynamic changes in surface reflection properties is extracted.

[0117] Step 303: Perform a time-domain coupling operation between the dominant fluctuation component and the thickness distribution characteristics to generate a thickness reflectivity co-variation sequence.

[0118] In this step, temporal coupling operation refers to the technique of binding different physical quantities according to a time reference, including timestamp alignment, fluctuation trajectory matching, and cooperative intensity calculation, to generate a thickness reflectivity joint evolution sequence; the thickness reflectivity cooperative change sequence refers to a cooperative intensity dataset arranged in chronological order, with each data point containing a four-dimensional parameter including a timestamp, thickness change, reflectivity fluctuation, and cooperative coefficient.

[0119] In this embodiment of the invention, the time series of the dominant fluctuation component is first aligned with the time nodes of the thickness distribution characteristics; second, a correlation model of the fluctuation trajectories of the two physical quantities is established; and finally, a thickness-reflectivity co-change sequence that synchronously reflects the co-change of thickness and reflectivity is generated through time-domain coupling operation.

[0120] Step 304: Based on the evolution of the thickness reflectivity coordinated change sequence at continuous time nodes, construct a dynamic relationship map of thickness reflectivity.

[0121] In this step, the evolution law refers to the evolution pattern of physical quantities presented in the coordinated change sequence, including the stability of the fluctuation period, the slope of amplitude decay, and the distribution density of abrupt change points, revealing the dynamic characteristics of coating quality.

[0122] In this embodiment of the invention, the fluctuation period and amplitude changes of the thickness reflectivity synergistic change sequence at continuous time nodes are first analyzed; then the extreme points of the slope and the inflection points of the sequence change are extracted; finally, a dynamic relationship map of thickness reflectivity with time as the horizontal axis and synergistic intensity as the vertical axis is constructed.

[0123] This invention employs frequency domain transformation to remove high-frequency noise from optical reflection and energy principal component separation to extract core fluctuation features. Temporal domain coupling operations establish a strictly synchronized cooperative sequence between thickness and reflectivity. Evolution law mapping transforms the dynamic galvanizing process into a quantifiable and controllable physical field model, solving the control inaccuracy problem caused by the asynchronous detection of surface reflection fluctuations and thickness in high-speed scenarios.

[0124] To eliminate control deviations caused by thickness detection delay and optical signal asynchrony during high-speed movement, this step achieves strict spatiotemporal synchronization through phase demodulation and delay compensation. This invention provides a specific embodiment where step 303 involves temporally coupling the dominant fluctuation component with the thickness distribution characteristics to generate a thickness reflectivity co-variance sequence, specifically including the following steps:

[0125] Step 331: Demodulate the phase fluctuation signal of the dominant fluctuation component to obtain the physical fluctuation characteristics of the surface reflection characteristics.

[0126] In this step, the phase fluctuation signal refers to the oscillating signal in the optical interference fringes where the phase angle changes over time, including the phase shift amplitude, period, and nonlinear distortion, reflecting the dynamic fluctuation of the optical path difference reflected from the galvanized layer surface; the demodulation operation refers to the technical process of separating the effective modulation component from the phase signal, including carrier frequency suppression, envelope extraction, and noise filtering, used to restore the physical fluctuation nature of surface reflection; the physical fluctuation characteristics refer to the core parameter set of surface reflection formed after demodulation, including the main oscillation frequency, amplitude attenuation slope, and phase stability index, characterizing the inherent fluctuation law of the coating surface quality.

[0127] In this embodiment of the invention, the phase fluctuation signal of the dominant fluctuation component is first acquired, including the phase angle timing change of the interference fringes; the carrier frequency and the modulation signal are separated by demodulation operation; and finally, the physical fluctuation characteristics reflecting the reflective nature of the galvanized layer surface are extracted, including the amplitude attenuation rate and the phase offset.

[0128] Step 332: Compensate for the response delay of the thickness distribution feature during the movement of the preset guardrail plate to generate a time delay corrected thickness feature.

[0129] In this step, response delay refers to the time lag of the eddy current sensor output signal relative to the actual position of the guardrail, including electromagnetic induction lag time and signal transmission delay, which leads to spatiotemporal inaccuracy of thickness detection data; compensation operation refers to the technical actions to eliminate response delay, including the product calculation of moving speed and delay, timestamp forward correction, and signal interpolation reconstruction; time delay corrected thickness feature refers to the thickness data entity generated after compensation, including the position-synchronized thickness gradient value and the corrected time-series change curve.

[0130] In this embodiment of the invention, the thickness distribution characteristics are first analyzed to account for the delay caused by the sensor response lag during the movement of the guardrail; then, a compensation operation is performed based on the preset moving speed parameters; finally, a time delay correction thickness feature is generated to eliminate the time delay error, and this feature is synchronized with the surface position in real time.

[0131] Step 333: Match the dynamic trajectory of the physical wave characteristics and the time delay correction thickness characteristics to construct a physical field synchronization relationship of thickness reflectivity.

[0132] In this step, the dynamic trajectory refers to the movement path of a physical quantity as it changes over time, the physical wave characteristics are the amplitude-time oscillation curves, and the thickness characteristics are the gradient spatial distribution curves; the matching operation refers to the technique for calculating the spatiotemporal overlap of the two trajectories, including trajectory inflection point alignment, rate of change correlation analysis, and tolerance interval calibration; the thickness-reflectivity physical field synchronization relationship refers to the parameter set that quantifies the spatiotemporal consistency of the two physical quantities, including the coordination intensity coefficient, phase difference threshold, and out-of-step alarm flag.

[0133] In this embodiment of the invention, firstly, the oscillation trajectory of the physical wave characteristics and the gradient change trajectory of the time delay correction thickness characteristics are obtained; secondly, the spatial overlap of the two trajectories at the same time node is calculated; finally, a thickness-reflectivity physical field synchronization relationship is constructed to characterize the strict synchronization of thickness and reflectivity, which includes a cooperative intensity matrix and a phase difference tolerance.

[0134] Step 334: Based on the physical field synchronization relationship of the thickness reflectivity, the physical fluctuation characteristics and the time delay correction thickness features are fused and encoded to generate a thickness reflectivity coordinated change sequence.

[0135] In this step, fusion encoding processing refers to data reconstruction operations based on synchronization relationships, including reflectivity weight allocation, thickness coordination coefficient embedding, and spatiotemporal label binding.

[0136] In this embodiment of the invention, firstly, the physical fluctuation characteristics are weighted and encoded according to the physical field synchronization relationship of thickness reflectivity; secondly, the time delay correction thickness features are labeled with a coordination coefficient; and finally, a thickness reflectivity coordination change sequence containing spatiotemporal binding labels is generated through fusion encoding processing.

[0137] This invention addresses the control disorder caused by the loss of synchronization between thickness and reflectivity data in high-speed galvanizing scenarios by using phase demodulation to remove optical carrier interference, delay compensation to eliminate spatiotemporal errors in eddy current detection, dynamic trajectory matching to construct an indivisible physical field synchronization relationship, and fusion encoding to generate a cooperative sequence with synchronization tags.

[0138] To address the disconnect between the physical field offset trend and the equipment execution parameters, this step integrates parameter conversion benchmarks with operating status to generate adaptive control parameters that the equipment can respond to. This invention provides a specific embodiment: Step 104, based on the gradient change characteristics of the thickness reflectivity dynamic relationship spectrum, determines the real-time control parameter combination associated with the preset galvanizing process equipment, specifically including the following steps:

[0139] Step 401: Locate the abnormal interval where the thickness of the galvanized layer and the reflectivity of the galvanized layer change in synergy based on the gradient jump region in the thickness reflectivity dynamic relationship graph.

[0140] In this step, the gradient jump region refers to the continuous region in the thickness reflectivity dynamic relationship spectrum where the rate of change exceeds a preset critical value, including the coordinate set of gradient value abrupt change points and the jump amplitude, reflecting the abnormal distortion of the synergistic relationship between thickness and reflectivity; reflectivity refers to the reflectivity parameter of the galvanized layer surface to incident light waves, including the main value of reflection intensity, scattering rate and polarization characteristics, used to characterize the surface smoothness and uniformity of the coating; the abnormal interval refers to the physical coverage of the gradient jump region on the guardrail surface, including the starting or ending position markings, interval length and abnormal level classification, used to locate the galvanized defect area.

[0141] In this embodiment of the invention, firstly, the region in the dynamic relationship spectrum of thickness reflectance that exceeds the set threshold is scanned; secondly, the spatial coordinate range of the gradient jump region is identified; finally, the abnormal interval where the galvanized layer thickness and reflectance change together exceed the normal fluctuation range is located, and the interval includes the starting position mark and the abnormal coverage area parameter.

[0142] Step 402: Quantize the gradient direction distribution characteristics within the abnormal interval to generate a physical field offset trend.

[0143] In this step, the gradient direction distribution characteristics refer to the angular distribution of gradient vectors within the abnormal interval, including the main direction angle, directional dispersion, and peak vector density, revealing the spatial orientation of coating quality imbalance; quantization refers to the technique of converting gradient direction characteristics into computable parameters, including the calculation of the mean of the main direction angle, the evaluation of the secondary direction deviation, and the integral operation of the vector density; the physical field offset trend refers to the set of coating quality imbalance direction parameters formed after quantization, including the offset dominant angle, diffusion range angle, and trend intensity coefficient, predicting the direction of coordinated deterioration of thickness and reflectivity.

[0144] In this embodiment of the invention, the gradient direction distribution features within the abnormal interval are first extracted, including the gradient vector angle distribution and the rate of change density; then the distribution density of the main direction and the deviation of the secondary direction are calculated; finally, a physical field offset trend characterizing the direction of the galvanized layer quality imbalance is generated, which includes the mean offset angle and the diffusion range.

[0145] Step 403: Based on the parameter conversion benchmark of the preset galvanizing process equipment, the physical field offset trend is subjected to regularization conversion processing to generate a set of basic control parameters.

[0146] In this step, the parameter conversion benchmark refers to the process control rule library stored in the preset equipment, which includes the current density thickness correction table, the zinc liquid flow reflectivity correlation matrix, and the temperature deposition rate response curve; the rule-based conversion process refers to the technology of converting physical trends into control instructions according to the rules of the benchmark library, including offset angle current correction matching and trend intensity flow adjustment value mapping; the basic control parameter set refers to the primary instruction set generated by rule conversion, which includes independent parameter units such as current adjustment, zinc liquid flow increase / decrease value, and temperature compensation.

[0147] In this embodiment of the invention, firstly, a parameter conversion reference library for a pre-set galvanizing process equipment is called, including current thickness response curves and temperature reflectivity correlation tables; secondly, conversion rules are matched according to the offset angle of the physical field offset trend; finally, a set of basic control parameters that can be executed by the equipment, including current adjustment amount and zinc liquid flow rate value, is generated through regularization conversion processing.

[0148] Step 404: Integrate the basic control parameter set and the operating status parameters of the preset galvanizing process equipment to generate a real-time control parameter combination associated with the preset galvanizing process equipment.

[0149] In this step, the operating status parameters refer to the process status data monitored in real time by the galvanizing equipment, including the instantaneous value of the plating bath temperature, the fluctuation of zinc ion concentration, and the rate of change of conveyor belt speed; the integrated operation refers to the technology of integrating control parameters with equipment status, including parameter status compatibility verification, physical limit constraint correction, and dynamic weight allocation.

[0150] In this embodiment of the invention, the operating status parameters of the preset galvanizing process equipment are first read, including the real-time temperature of the plating solution, the zinc ion concentration, and the conveyor belt speed; then, the set of basic control parameters and the operating status parameters are input into the dynamic constraint engine; finally, the combination of real-time control parameters that are matched with the physical limits of the equipment is integrated by the operation output.

[0151] This invention achieves precise positioning of coating coordination anomaly intervals through gradient jump regions; quantizes gradient directions to generate physical field offset trends; implements reliable mapping from physical trends to control commands through parameter conversion benchmarks; and integrates operating states to ensure that control parameters are adapted to equipment operating conditions in real time, breaking through the technical bottleneck of the separation between physical detection and equipment execution in traditional control.

[0152] To overcome the bottleneck of mismatch between control commands and equipment physical limits, this step ensures the safety and process optimization of closed-loop commands through constraint adaptation and decision rule fusion. This invention provides a specific embodiment: Step 105, the real-time control parameter combination is input into the dynamic decision unit of the galvanizing process of the preset galvanizing process equipment to generate closed-loop control commands, specifically including the following steps:

[0153] Step 501: Serialize the multi-dimensional control parameters in the real-time control parameter combination to generate the control parameter sequence of the galvanized layer.

[0154] In this step, multi-dimensional control parameters refer to a set of independent parameter units including current density adjustment, zinc liquid flow rate increase / decrease, and temperature compensation, with each parameter unit corresponding to a control dimension of the galvanizing process; serialization processing refers to the technical process of sorting multi-dimensional parameters according to execution time sequence, including parameter priority evaluation, timestamp marking, and execution order queue generation; control parameter sequence refers to a queue of parameter instructions arranged in execution time sequence, including current, flow rate, and temperature adjustment instruction units with millisecond-level timestamps.

[0155] In this embodiment of the invention, firstly, multi-dimensional control parameters in the real-time control parameter combination are identified, including current regulation, zinc liquid flow rate and temperature compensation; secondly, each parameter unit is sorted according to execution priority; finally, a time-ordered sequence of zinc plating layer control parameters is generated.

[0156] Step 502: Extract the executable instruction elements of the preset galvanizing process equipment from the control parameter sequence to generate a physical control instruction set.

[0157] In this step, executable instruction elements refer to native signal units that the device actuator can directly parse, including voltage pulse width, servo valve opening percentage, and resistance heating power value; physical control instruction set refers to a set of device-level operation commands composed of instruction elements, including motor control signals, valve drive signals, and heating control signals.

[0158] In this embodiment of the invention, firstly, the current, flow rate, and temperature regulation commands in the control parameter sequence are parsed; secondly, the voltage pulse commands, valve opening signals, and heating power values ​​that can be recognized by the actuator of the preset galvanizing process equipment are separated; finally, a set of physical control commands natively executable by the equipment is generated.

[0159] Step 503: Perform physical constraint adaptation processing on the real-time operating constraint parameters of the preset galvanizing process equipment and the physical control instruction set to generate equipment compatible instructions.

[0160] In this step, the real-time operating constraint parameters refer to the physical limits of the galvanizing equipment under the current operating conditions, including the maximum withstand current of the plating tank, the safe threshold of the zinc bath temperature, and the upper limit of the pump flow rate; physical constraint adaptation processing refers to the technology of matching control commands with the physical limits of the equipment, including command amplitude limiting, rate gradual adjustment, and over-limit alarm suppression; equipment compatibility commands refer to the command entities generated after constraint adaptation, ensuring that the current value does not exceed the maximum threshold, the flow rate is within the pump capacity range, and the temperature value is within the safe range.

[0161] In this embodiment of the invention, firstly, the real-time operating constraint parameters of the preset galvanizing process equipment are obtained, including the maximum current threshold, the safe range of the plating solution temperature, and the zinc pump flow limit; secondly, the instruction parameters and constraint parameters of the physical control instruction set are input into the dynamic adaptation engine; finally, the physical constraint adaptation process generates equipment-compatible instructions that do not exceed the physical limits of the equipment.

[0162] Step 504: Integrate the equipment compatibility command and the decision rules of the galvanizing process dynamic decision unit of the preset galvanizing process equipment to generate a closed-loop control command.

[0163] In this step, decision rules refer to the pre-set process optimization logic in the dynamic decision unit, including current priority adjustment rules, temperature and flow linkage strategies, and abnormal handling priorities; integrated operation refers to the technology of integrating equipment instructions with process rules, including rule weight allocation, instruction rule logic binding, and conflict resolution.

[0164] In this embodiment of the invention, the decision rule library of the dynamic decision unit of the galvanizing process is first invoked, including the current thickness response priority rule and the temperature reflectivity correlation strategy; secondly, the equipment compatibility instructions and decision rules are weighted and logically bound; finally, the closed-loop control instructions for adjusting the voltage of the galvanizing tank and the conveying speed are integrated and output.

[0165] This invention achieves the orderly execution of multi-dimensional control commands through parameter serialization; physical command set extraction ensures equipment-level operability; constraint adaptation processing overcomes the bottleneck of mismatch between control commands and equipment physical limits; and decision rule integration endows the commands with process optimization intelligence, realizing accurate, safe, and intelligent output of control commands in high-speed galvanizing scenarios.

[0166] Figure 2 This invention provides a schematic diagram of a closed-loop control system for the galvanized thickness of guardrail panels based on multi-sensor fusion, as shown in the embodiment of the invention. Figure 2 As shown, the system includes:

[0167] The acquisition module 21 is used to acquire dynamic optical interference fringe data and continuous conductivity change data generated by the preset eddy current sensor during its movement at the preset guardrail.

[0168] The coupling module 22 is used to couple and correlate the dynamic optical interference fringe data and the continuous conductivity change data to generate fused data that includes the thickness distribution characteristics of the galvanized layer of the preset guardrail and the surface reflection characteristics of the galvanized layer.

[0169] Construction module 23 is used to construct a dynamic relationship map of thickness reflectivity based on the light intensity distribution pattern of the fused data;

[0170] The determination module 24 is used to determine the combination of real-time control parameters associated with the preset galvanizing process equipment based on the gradient change characteristics of the thickness reflectivity dynamic relationship spectrum.

[0171] The generation module 25 is used to input the real-time control parameter combination into the galvanizing process dynamic decision unit of the preset galvanizing process equipment to generate closed-loop control commands.

[0172] Figure 2 The aforementioned closed-loop control system for the galvanized thickness of guardrail panels based on multi-sensor fusion can execute... Figure 1 The implementation principle and technical effects of the closed-loop control method for galvanized thickness of guardrails based on multi-sensor fusion, as described in the illustrated embodiment, will not be repeated here. The specific operation methods of each module and unit in the closed-loop control system for galvanized thickness of guardrails based on multi-sensor fusion in the above embodiments have been described in detail in the embodiments related to this method, and will not be elaborated upon here.

[0173] In one possible design, Figure 2 The illustrated embodiment of a closed-loop control system for the galvanized thickness of guardrail panels based on multi-sensor fusion can be implemented as a computing device, such as... Figure 3 As shown, the computing device may include a storage component 31 and a processing component 32;

[0174] The storage component 31 stores one or more computer instructions, wherein the one or more computer instructions are invoked and executed by the processing component 32.

[0175] The processing component 32 is used to: acquire dynamic optical interference fringe data and continuous conductivity change data generated during the movement of a pre-set eddy current sensor at a pre-set guardrail plate; couple and correlate the dynamic optical interference fringe data and the continuous conductivity change data to generate fused data containing the thickness distribution characteristics of the galvanized layer of the pre-set guardrail plate and the surface reflectivity characteristics of the galvanized layer; construct a dynamic relationship map of thickness reflectivity based on the light intensity distribution pattern of the fused data; determine the combination of real-time control parameters associated with the pre-set galvanizing process equipment according to the gradient change characteristics of the dynamic relationship map of thickness reflectivity; and input the combination of real-time control parameters into the dynamic decision unit of the galvanizing process of the pre-set galvanizing process equipment to generate closed-loop control commands.

[0176] The processing component 32 may include one or more processors to execute computer instructions to complete all or part of the steps in the above-described method. Alternatively, the processing component may be implemented as one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the above-described method.

[0177] Storage component 31 is configured to store various types of data to support operations at the terminal. The storage component can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0178] Of course, computing devices may also include other components, such as input / output interfaces, display components, communication components, etc.

[0179] Input / output interfaces provide interfaces between processing components and peripheral interface modules, which can be output devices, input devices, etc.

[0180] The communication components are configured to facilitate wired or wireless communication between computing devices and other devices.

[0181] The computing device can be a physical device or an elastic computing host provided by a cloud computing platform. In this case, the computing device can refer to a cloud server, and the aforementioned processing components, storage components, etc., can be basic server resources rented or purchased from the cloud computing platform.

[0182] This invention also provides a computer storage medium storing a computer program, which, when executed by a computer, can perform the above-described functions. Figure 1 The embodiment shown is a closed-loop control method for the galvanized thickness of guardrail panels based on multi-sensor fusion.

[0183] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0184] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0185] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence 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 ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0186] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A closed-loop control method for the galvanized thickness of guardrail panels based on multi-sensor fusion, characterized in that, include: Acquire dynamic optical interference fringe data and continuous conductivity change data generated by the preset eddy current sensor during its movement at the preset guardrail plate; The dynamic optical interference fringe data and the continuous conductivity variation data are coupled and correlated to generate fused data that includes the thickness distribution characteristics of the galvanized layer of the pre-set guardrail and the surface reflection characteristics of the galvanized layer. Based on the light intensity distribution pattern of the fused data, a dynamic relationship map of thickness reflectivity is constructed. Based on the gradient change characteristics of the thickness reflectivity dynamic relationship spectrum, the combination of real-time control parameters associated with the pre-set galvanizing process equipment is determined. The real-time control parameters are input into the dynamic decision-making unit of the galvanizing process of the preset galvanizing equipment to generate closed-loop control commands.

2. The method according to claim 1, characterized in that, Acquire dynamic optical interference fringe data and continuous conductivity change data generated during the movement of a pre-positioned eddy current sensor at a pre-positioned guardrail, including: A pre-set optical interferometer is used to scan the pre-set guardrail during the movement to obtain the original sequence of optical interference fringes; The conductivity of the pre-set guardrail plate during movement is detected using a pre-set eddy current sensor, generating a continuous conductivity signal stream; The original sequence of optical interference fringes is subjected to fringe morphology separation processing to obtain the light intensity distribution sequence; The continuous conductivity signal stream is processed by moving region segmentation to generate conductivity change data segments corresponding to the galvanized layer of the preset guardrail. The light intensity distribution sequence is time-stamped and bound to the conductivity change data segment to generate dynamic light interference fringe data and continuous conductivity change data.

3. The method according to claim 1, characterized in that, The dynamic optical interference fringe data and the continuous conductivity variation data are coupled and correlated to generate fused data that includes the thickness distribution characteristics of the galvanized layer of the pre-installed guardrail and the surface reflection characteristics of the galvanized layer, including: The interference fringe morphology in the dynamic light interference fringe data is decomposed to obtain the surface reflection characteristics of the galvanized layer of the pre-set guardrail and the light intensity distribution characteristics corresponding to the surface reflection characteristics. The signal response data of the continuous conductivity change data during the movement of the pre-installed guardrail are analyzed to obtain the thickness distribution characteristics of the galvanized layer and the conductivity change gradient sequence corresponding to the thickness distribution characteristics. Spatial location correlation matching is performed between the light intensity distribution characteristics and the conductivity change gradient sequence to establish a physical field synergy factor between the surface thickness and internal thickness of the galvanized layer; Based on the physical field synergy factor, the light intensity distribution characteristics and the conductivity variation gradient sequence are bidirectionally coupled to generate fused data containing the surface reflection characteristics and the thickness distribution characteristics.

4. The method according to claim 1, characterized in that, Based on the light intensity distribution pattern of the fused data, a dynamic relationship map of thickness reflectivity is constructed, including: The light intensity distribution pattern in the fused data is converted into a frequency domain distribution pattern to generate light intensity frequency domain distribution features; The dominant wave component corresponding to the surface reflection characteristics is separated from the principal energy component of the frequency domain distribution characteristics of the light intensity. The dominant fluctuation component is coupled with the thickness distribution feature in the time domain to generate a thickness reflectivity co-variance sequence. Based on the evolution of the thickness reflectivity co-variance sequence at continuous time nodes, a dynamic relationship map of thickness reflectivity is constructed.

5. The method according to claim 4, characterized in that, The dominant fluctuation component is coupled with the thickness distribution characteristics in the temporal domain to generate a thickness reflectivity co-variance sequence, including: The phase fluctuation signal of the dominant fluctuation component is demodulated to obtain the physical wave characteristics of the surface reflection characteristics. The response delay of the thickness distribution feature during the movement of the preset guardrail is compensated to generate a time-delay corrected thickness feature; The physical wave characteristics and the dynamic trajectory of the time delay correction thickness characteristics are matched to construct a synchronization relationship of the physical field of thickness reflectivity. Based on the physical field synchronization relationship of the thickness reflectivity, the physical fluctuation characteristics and the time delay correction thickness features are fused and encoded to generate a thickness reflectivity coordinated change sequence.

6. The method according to claim 1, characterized in that, Based on the gradient change characteristics of the thickness reflectivity dynamic relationship spectrum, the combination of real-time control parameters associated with the pre-set galvanizing process equipment is determined, including: Based on the gradient jump region in the thickness reflectivity dynamic relationship graph, the abnormal interval of the coordinated change between the thickness of the galvanized layer and the reflectivity of the galvanized layer is located. The gradient direction distribution characteristics within the abnormal interval are quantized to generate a physical field offset trend. Based on the parameter conversion benchmark of the pre-set galvanizing process equipment, the physical field offset trend is subjected to regularization conversion processing to generate a set of basic control parameters. The basic control parameter set and the operating status parameters of the preset galvanizing process equipment are integrated to generate a real-time control parameter combination associated with the preset galvanizing process equipment.

7. The method according to claim 1, characterized in that, The real-time control parameter combination is input into the dynamic decision-making unit of the galvanizing process of the preset galvanizing process equipment to generate closed-loop control commands, including: The multi-dimensional control parameters in the real-time control parameter combination are serialized to generate the control parameter sequence of the zinc plating layer. Extract the executable instruction elements of the preset galvanizing process equipment from the control parameter sequence to generate a physical control instruction set; The real-time operating constraint parameters of the pre-set galvanizing process equipment and the physical control instruction set are subjected to physical constraint adaptation processing to generate equipment compatible instructions. The equipment compatibility instructions and the decision rules of the dynamic decision unit of the galvanizing process of the preset galvanizing process equipment are integrated to generate closed-loop control instructions.

8. A closed-loop control system for the galvanized thickness of guardrail panels based on multi-sensor fusion, characterized in that, include: The acquisition module is used to acquire dynamic optical interference fringe data and continuous conductivity change data generated by the preset eddy current sensor during its movement at the preset guardrail. The coupling module is used to couple and correlate the dynamic optical interference fringe data and the continuous conductivity change data to generate fused data that includes the thickness distribution characteristics of the galvanized layer of the preset guardrail and the surface reflection characteristics of the galvanized layer. The construction module is used to construct a dynamic relationship map of thickness reflectivity based on the light intensity distribution pattern of the fused data; The determination module is used to determine the combination of real-time control parameters associated with the preset galvanizing process equipment based on the gradient change characteristics of the thickness reflectivity dynamic relationship spectrum. The generation module is used to input the real-time control parameter combination into the galvanizing process dynamic decision unit of the preset galvanizing process equipment to generate closed-loop control commands.

9. A computing device, characterized in that, It includes a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are invoked and executed by the processing component to implement a closed-loop control method for galvanized thickness of guardrail based on multi-sensor fusion as described in any one of claims 1 to 7.

10. A computer storage medium, characterized in that, The system contains a computer program that, when executed by a computer, implements a closed-loop control method for the galvanized thickness of guardrail panels based on multi-sensor fusion, as described in any one of claims 1 to 7.

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