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

Abstract

The application provides a guardrail plate galvanizing thickness closed-loop control method and system based on multi-sensor fusion, acquires dynamic light interference fringe data and continuous conductivity change data of a preset eddy current sensor, and performs coupling correlation to generate fusion data containing thickness distribution characteristics and surface reflection characteristics of a galvanizing layer of a preset guardrail plate; a thickness reflectivity dynamic relationship graph is constructed based on the fusion data; a real-time control parameter combination associated with a preset galvanizing process equipment is determined according to gradient change characteristics of the thickness reflectivity dynamic relationship graph; 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. The application solves the control lag problem caused by asynchronous detection of thickness and surface reflection characteristics in a high-speed continuous galvanizing scene, breaks through the technical bottleneck that traditional single-sensor detection cannot associate surface quality and internal thickness, and improves the uniformity of the plated layer and the process stability.

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 fluctuations, strip speed changes, and air knife parameter drifts 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-coating or quality problems caused by under-coating.

[0003] The existing solution 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 solution 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 substrate microstructure, surface oxidation state, or temperature gradient, leading to 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, cross-modal correlation is performed between the vortex conductivity change data and the optical interference fringe data, generating a fusion data carrier of thickness and surface reflection characteristics; then, a thickness reflectivity dynamic relationship map is constructed based on the light intensity distribution pattern, converting 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 adaptive adjustment of the decision unit realizes the closed-loop control of the uniformity of the plated layer. 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 state of the energy of the optical component in the fusion data in the frequency domain and the spatial domain, including the main peak frequency, harmonic components and energy attenuation slope; the thickness reflectivity dynamic relationship atlas refers to a two-dimensional space-time model constructed based on the light intensity pattern, the horizontal axis is the time node, the vertical axis is the thickness reflectivity coupling strength, and the contour line represents the cooperative change trend.

[0070] In the embodiment of the present application, first, the light intensity distribution pattern of the fusion data is analyzed; second, the light intensity pattern is decomposed into frequency domain energy components; then, the energy main component of the dominant galvanized layer surface reflection wave is extracted; finally, the thickness reflectivity dynamic relationship atlas reflecting the cooperative evolution law of thickness and reflectivity is constructed.

[0071] Step 104: determining a real-time control parameter combination associated with the preset galvanizing process equipment according to the gradient change characteristics of the thickness reflectivity dynamic relationship atlas.

[0072] In this step, the gradient change characteristics refer to the change rate extreme point set of adjacent regions in the thickness reflectivity dynamic relationship atlas, including the gradient direction angle, the jump amplitude and the mutation area; the real-time control parameter combination refers to the adjustment instruction set of the current density, the plating solution flow and the conveying belt speed linked with the galvanizing process equipment, and the parameter values are generated based on the matching of the gradient characteristics and the physical limit of the equipment.

[0073] In the embodiment of the present application, first, the gradient change characteristics in the thickness reflectivity dynamic relationship atlas are identified; second, the abnormal interval of the gradient jump region is located; then, the physical field offset corresponding to the gradient direction is quantified; finally, the real-time control parameter combination linked with the current, temperature and flow parameters of the preset galvanizing process equipment is generated.

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

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

[0076] In the embodiment of the present application, first, the real-time control parameter combination is serialized into device interpretable instructions; second, the device running constraint rules are matched through the galvanizing process dynamic decision unit; finally, the closed-loop control instructions for adjusting the galvanizing tank voltage and the conveying speed are output.

[0077] For example, first, the preset guardrail surface moving at a speed of three meters per minute is scanned by a high-speed optical interferometer to capture dynamic optical interference fringe data; at the same time, continuous conductivity change data five millimeters below the guardrail is detected by a preset eddy current sensor. Second, the two kinds of data are input into a coupling processor to match the light intensity and the conductivity peak points according to the spatial coordinates, and fusion data that fuse the thickness distribution characteristics and the surface reflection characteristics are generated. Then, the light intensity distribution main frequency component of the fusion data is extracted to construct a dynamic relationship graph that reflects the collaborative evolution of the thickness and the reflectivity. Subsequently, the jump region with a gradient greater than 10 degrees per millisecond in the graph is identified to generate a real-time control parameter combination containing a current regulation amount of ±50 amperes and a zinc liquid flow increase / decrease value of 10 liters per minute. Finally, the plating solution temperature upper limit of 300 degrees Celsius is matched by a plating equipment decision unit to output a closed-loop control instruction containing an adjustment voltage of ±5 volts and a conveying speed increase / decrease value of 0.5 meters per minute to a plating bath actuator.

[0078] The embodiment of the present application establishes a thickness and surface reflection physical field collaborative model through multi-sensor dynamic coupling, converts optical interference fringes and eddy current conductivity data into a thickness reflectivity dynamic relationship graph, generates control parameters linked with a zinc plating equipment based on the gradient characteristics of the graph, makes the plating layer thickness control synchronously respond to surface quality changes, and solves the composite defects of thickness detection delay and surface quality loss of control in a high-speed moving scenario through real-time constraint adaptation of a decision unit to output a closed-loop instruction, thereby achieving zinc plating uniformity improvement and process fluctuation suppression.

[0079] In order to solve the fusion distortion problem caused by asynchronous multi-source data acquisition in a high-speed moving scenario, the spatiotemporal synchronization mechanism of optical scanning and eddy current detection is used in this step to ensure the real-time matching of dynamic optical interference fringes and conductivity data. The present application provides a specific embodiment, step 101, obtaining dynamic optical interference fringe data and continuous conductivity change data generated by a preset eddy current sensor during the movement of a preset guardrail, which specifically includes the following steps:

[0080] Step 111: scanning the preset guardrail during movement by using a preset optical interferometer to obtain an original sequence of optical interference fringes.

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

[0082] In the embodiment of the present application, first, the preset optical interferometer is started to continuously scan the surface of the preset guardrail during movement; second, the light and dark alternating fringes formed by the reflected light waves are captured by an interference lens; and finally, the original sequence of optical interference fringes arranged in time sequence is generated.

[0083] Step 112: Conductivity detection of the preset guardrail plate in the moving process is performed by using the preset eddy current sensor to generate a continuous conductivity signal stream.

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

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

[0086] Step 113: The original sequence of light interference fringes is subjected to fringe morphology separation processing to obtain a light intensity distribution sequence.

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

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

[0089] Step 114: The continuous conductivity signal stream is subjected to moving area segmentation processing to generate a conductivity change data segment corresponding to the galvanized layer of the preset guardrail plate.

[0090] In this step, the moving area refers to the effective detection range of the preset guardrail plate moving on the galvanizing tank conveyor belt, including the position interval in the length direction and the scanning coverage domain in the width direction; the moving area segmentation processing refers to the process of segmenting the conductivity signal according to the spatial motion trajectory of the guardrail plate, including position coordinate mapping, motion speed compensation, and region boundary calibration; the conductivity change data segment refers to the conductivity data set bound to the spatial position of the galvanized area of the guardrail plate, including the start position or end position marker and the internal gradient distribution of the region.

[0091] In the embodiment of the present application, first, the detection area is divided according to the moving trajectory of the preset guardrail plate; second, the signal segment corresponding to the position of the galvanized layer is intercepted; and finally, the conductivity change data segment bound to the spatial position is generated.

[0092] Step 115: time-stamp alignment binding the light intensity distribution sequence and the conductivity change data segment, generating dynamic light interference fringe data and continuous conductivity change data.

[0093] In this step, the time-stamp alignment binding operation refers to the technology of matching different sensor data according to a unified clock reference, including time-stamp interpolation correction, sampling rate synchronization and data point mapping.

[0094] In the embodiment of the present application, first, the collection time stamps of the light intensity distribution sequence and the conductivity change data segment are marked; second, the two data streams are aligned according to the same time reference; and finally, the synchronized dynamic light interference fringe data and continuous conductivity change data are generated by binding.

[0095] The embodiment of the present application solves the data collection asynchronous problem in the high-speed moving scene through the space-time synchronization mechanism of optical scanning and eddy current detection; the fringe morphology is separated to extract the pure reflection characteristics, and the moving area is segmented to realize the spatial positioning of the thickness data; and the time-stamp binding ensures the physical field alignment of the multi-source data, thereby providing high-precision input for the collaborative control of the thickness and surface reflection.

[0096] In order to improve the physical field collaboration accuracy of the surface reflection characteristics and the internal thickness distribution, this step realizes the fusion of the double thickness data through interference fringe decomposition and conductivity gradient analysis, and eliminates the feature fragmentation defects in the traditional detection. The present application provides a specific embodiment, 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, specifically including the following steps:

[0097] Step 201: decomposing the interference fringe morphology in the dynamic light interference fringe data to obtain the surface reflection characteristics of the galvanized layer of the preset guardrail plate 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 state of the light interference fringe, including the bright and dark fringe spacing, phase angle offset and intensity attenuation slope, reflecting the micro relief and reflectivity difference 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 deduction and effective signal reconstruction, for extracting pure surface reflection information; and the light intensity distribution characteristics refer to the reflection light intensity spatial distribution data set formed after decomposition, including the intensity value matrix corresponding to the two-dimensional coordinate points and the time sequence change characteristics, directly representing the galvanized layer surface quality state.

[0099] In the embodiment of the present application, firstly, the interference fringe pattern characteristics in the dynamic light interference fringe data are identified, including fringe spacing distribution and phase shift; secondly, the background scattering noise and effective reflection signal are separated through decomposition operation; finally, the light intensity distribution characteristics reflecting the surface reflection characteristics of the galvanized layer are extracted, which include the intensity value matrix corresponding to the spatial coordinates.

[0100] Step 202: The signal response data of the continuous conductivity change data in the movement process of the preset guardrail plate is analyzed 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 electromagnetic induction original signal output by the eddy current sensor when detecting the galvanized layer, including current amplitude, phase delay and frequency response spectrum, reflecting the dynamic response of metal ion distribution to alternating magnetic field; the analysis operation refers to the technical process of extracting thickness-related parameters from signal response data, including gradient calculation, extreme point positioning and regional uniformity evaluation, which is used to inverse the thickness distribution law of the coating; the conductivity change gradient sequence refers to the conductivity change rate data set arranged in order of spatial position, including amplitude change amount and directional derivative per unit movement distance, quantifying the local mutation characteristics of thickness distribution.

[0102] In the embodiment of the present application, firstly, the signal response data of the continuous conductivity change data in the movement process of the guardrail plate is obtained, including the inductive current amplitude fluctuation and time sequence change curve; secondly, the conductivity change gradient is calculated through analysis operation, including the amplitude change rate per unit distance; finally, the conductivity change gradient sequence representing the thickness distribution characteristics is generated, which strictly corresponds to the spatial position of the galvanized layer.

[0103] Step 203: The light intensity distribution characteristics and the conductivity change gradient sequence are spatially and positionally matched to establish the physical field coordination factor between the surface thickness and the internal thickness of the galvanized layer.

[0104] In this step, the spatial position correlation matching operation refers to the technology of binding different physical quantity data according to the same spatial coordinates, including coordinate mapping conversion, motion trajectory compensation and position point alignment, to ensure the spatial consistency of surface and internal data; the surface thickness refers to the apparent thickness parameter of the galvanized layer calculated based on the light interference characteristics, including the equivalent thickness value converted from the reflection path difference and the surface relief correction amount; the internal thickness refers to the actual coating thickness parameter inverted from the eddy current conductivity data, including the zinc layer deposition amount corresponding to the electromagnetic induction depth and the compactness compensation value; the physical field coordination factor refers to the quantitative parameter representing the coordinated change law of surface thickness and internal thickness, including the coupling strength coefficient of spatial position points, the change direction angle and the time sequence correlation index.

[0105] In the embodiment of the present application, first, the light intensity distribution characteristics are aligned with the preset guardrail surface coordinate of the conductivity change gradient sequence; second, the surface thickness reflection value and the internal thickness induction value of the same position point are matched; and finally, a physical field coordination factor reflecting the coordinated change intensity of the surface thickness and the internal thickness is established, and the factor includes a coupling intensity coefficient and a time sequence evolution marker.

[0106] Step 204: Bidirectional coupling processing is performed on the light intensity distribution characteristics and the conductivity change gradient sequence based on the physical field coordination factor, to generate fusion data containing the surface reflection characteristics and the thickness distribution characteristics.

[0107] In this step, bidirectional coupling processing refers to a fusion operation acting on the light intensity characteristics and the conductivity sequence at the same time, including reflection rate weight distribution, thickness sensitivity compensation and data reconstruction under the constraint of a physical field.

[0108] In the embodiment of the present application, first, the light intensity distribution characteristics are aligned with the preset guardrail surface coordinate of the conductivity change gradient sequence; second, the surface thickness reflection value and the internal thickness induction value of the same position point are matched; and finally, a physical field coordination factor reflecting the coordinated change intensity of the surface thickness and the internal thickness is established, and the factor includes a coupling intensity coefficient and a time sequence evolution marker.

[0109] In the embodiment of the present application, the physical quantity separation of the surface and the internal thickness is realized through interference fringe decomposition and conductivity gradient analysis; the spatial position matching establishes a double-thickness coordination factor; and bidirectional coupling processing fuses the reflection characteristics and the thickness distribution, thereby solving the problem of split detection data of surface quality and internal thickness in the high-speed galvanizing scene, and providing precise plated layer state holographic data for closed-loop control.

[0110] In order to solve the problem of inaccurate thickness and reflection rate coordination law under high-frequency noise interference, this step constructs a plated layer quality dynamic evolution graph through frequency domain principal component separation and time domain physical coupling. The present application provides a specific embodiment, step 103, constructing a thickness reflection rate dynamic relationship graph based on the light intensity distribution mode of the fusion data, specifically including the following steps:

[0111] Step 301: Convert the light intensity distribution mode in the fusion data into a frequency domain distribution mode, to generate light intensity frequency domain distribution characteristics.

[0112] In this step, the conversion operation refers to the technical process of converting the light intensity spatial distribution data into a frequency domain expression, including signal decomposition into fundamental frequency components, energy spectrum calculation, and harmonic extraction, for revealing the hidden frequency characteristics of optical reflection; the frequency domain distribution mode refers to the distribution state of light intensity energy in the frequency dimension, including the main peak frequency position, the secondary peak energy proportion, and the frequency band width parameters, reflecting the periodic fluctuation law of the galvanized layer reflection characteristics; the light intensity frequency domain distribution feature refers to the quantitative data set formed after frequency domain conversion, including the fundamental frequency amplitude, the third harmonic energy, and the frequency band attenuation coefficient, for characterizing the frequency spectrum characteristics of the reflected light wave.

[0113] In the embodiment of the present application, first, the light intensity distribution mode in the fusion data is subjected to a conversion operation to decompose the spatial domain light intensity signal into frequency components; second, the energy distribution state of each frequency component is calculated; and finally, the light intensity frequency domain distribution feature containing the main frequency energy and harmonic characteristics is generated.

[0114] Step 302: Separating the dominant fluctuation component corresponding to the surface reflection characteristics from the energy main component of the light intensity frequency domain distribution feature.

[0115] In this step, the energy main component refers to the core frequency band set with an energy accumulation proportion exceeding a preset proportion in the frequency domain feature, including the main frequency band center frequency, the bandwidth, and the energy density value, and the dominant coating reflection characteristic evolution; the dominant fluctuation component refers to the specific frequency band signal separated from the energy main component, containing the core frequency, phase angle, and time sequence amplitude of the surface reflection fluctuation, directly driving the coating quality change.

[0116] In the embodiment of the present application, first, the frequency band with an energy proportion exceeding a set threshold in the light intensity frequency domain distribution feature is identified as the energy main component; second, the specific frequency band related to the galvanized layer surface reflection fluctuation in the energy main component is separated; and finally, the dominant fluctuation component characterizing the dynamic change of the surface reflection characteristics is extracted.

[0117] Step 303: Time domain coupling operation of the dominant fluctuation component and the thickness distribution feature, generating a thickness reflectivity cooperative change sequence.

[0118] In this step, the time domain coupling operation refers to the technology of binding different physical quantities according to the time reference, including time stamp 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 data set arranged in chronological order, each data point containing four-dimensional parameters of time label, thickness change amount, reflectivity fluctuation amount, and cooperative coefficient.

[0119] In the embodiment of the present application, firstly, the time sequence of the dominant fluctuation component is aligned with the time node of the thickness distribution feature; secondly, a fluctuation trajectory correlation model of the two physical quantities is established; and finally, a thickness reflectivity collaborative change sequence synchronously reflecting the collaborative change of thickness and reflectivity is generated through time domain coupling operation.

[0120] Step 304: Based on the evolution law of the thickness reflectivity collaborative change sequence on the continuous time node, a thickness reflectivity dynamic relationship graph is constructed.

[0121] In this step, the evolution law refers to the physical quantity evolution mode presented in the collaborative change sequence, including fluctuation period stability, amplitude attenuation slope and mutation point distribution density, which reveals the dynamic characteristics of the plating layer quality.

[0122] In the embodiment of the present application, firstly, the fluctuation period and amplitude change of the thickness reflectivity collaborative change sequence on the continuous time node are analyzed; secondly, the slope extreme point and inflection point position of the sequence change are extracted; and finally, a thickness reflectivity dynamic relationship graph with time as the horizontal axis and collaborative strength as the vertical axis is constructed.

[0123] The embodiment of the present application can strip the high-frequency noise of optical reflection through frequency domain conversion, energy main component separation and extraction of core fluctuation characteristics; the time domain coupling operation establishes the collaborative sequence of thickness and reflectivity in strict synchronization; and the evolution law graphing converts the dynamic galvanizing process into a quantifiable control physical field model, solving the control misalignment problem caused by the asynchronous surface reflection fluctuation and thickness detection in the high-speed scene.

[0124] In order to eliminate the control deviation caused by the thickness detection delay and optical signal out of step in high-speed movement, this step realizes strict time and space synchronization through phase demodulation and delay compensation. The present application provides a specific embodiment, step 303, time domain coupling operation of the dominant fluctuation component and the thickness distribution feature to generate a thickness reflectivity collaborative change sequence, specifically including the following steps:

[0125] Step 331: Demodulating 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 oscillation signal of the phase angle in the light interference fringe changing with time, including phase shift amplitude, period and non-linear distortion quantity, reflecting the dynamic fluctuation of the surface reflection optical path difference of the galvanizing layer; 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, which is used to restore the physical fluctuation nature of the surface reflection; the physical fluctuation characteristics refer to the surface reflection core parameter set formed after demodulation, including the main oscillation frequency, amplitude attenuation slope and phase stability index, which characterize the inherent fluctuation law of the plating layer surface quality.

[0127] In the embodiment of the present application, firstly, the phase fluctuation signal of the dominant wave component is collected, containing the time sequence change of the phase angle of the interference fringes; secondly, the carrier frequency and the modulation signal are separated through demodulation operation; finally, the physical fluctuation characteristics reflecting the nature of the surface reflection of the galvanized layer are extracted, including the amplitude attenuation rate and the phase shift amount.

[0128] Step 332: compensate for the response delay of the thickness distribution feature in the movement of the pre-set guardrail, and generate a time delay corrected thickness feature.

[0129] In this step, the response delay refers to the time lag of the eddy current sensor output signal relative to the actual position of the guardrail, including the electromagnetic induction lag time and signal transmission delay, resulting in the spatio-temporal misalignment of the thickness detection data; the compensation operation refers to the technical action to eliminate the response delay, including the product calculation of the moving speed and the delay amount, the time stamp forward correction and the signal interpolation reconstruction; the 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 sequence change curve.

[0130] In the embodiment of the present application, firstly, the delay amount of the thickness distribution feature in the movement of the guardrail due to the response lag of the sensor is analyzed; secondly, the compensation operation is performed based on the pre-set moving speed parameter; finally, the time delay corrected thickness feature that eliminates the time delay error is generated, which is real-time synchronized with the surface position.

[0131] Step 333: match the dynamic trajectory of the physical fluctuation characteristics and the time delay corrected thickness feature to construct the thickness reflectivity physical field synchronization relationship.

[0132] In this step, the dynamic trajectory refers to the motion path of the physical quantity changing with time, the physical fluctuation characteristics are the amplitude time oscillation curve, and the thickness feature is the gradient space distribution curve; the matching operation refers to the technology of calculating the spatio-temporal coincidence degree of the two trajectories, including the inflection point alignment, the change rate correlation analysis and the tolerance interval calibration; the thickness reflectivity physical field synchronization relationship refers to the parameter set quantifying the spatio-temporal consistency of the two physical quantities, including the cooperation intensity coefficient, the phase difference threshold and the out-of-step alarm mark.

[0133] In the embodiment of the present application, firstly, the oscillation trajectory of the physical fluctuation characteristics and the gradient change trajectory of the time delay corrected thickness feature are obtained; secondly, the spatial coincidence degree of the two trajectories at the same time node is calculated; finally, the thickness reflectivity physical field synchronization relationship representing the strict synchronization of thickness and reflectivity is constructed, which includes the cooperation intensity matrix and the phase difference tolerance.

[0134] Step 334: based on the thickness reflectivity physical field synchronization relationship, fuse and encode the physical fluctuation characteristics and the time delay corrected thickness feature to generate a thickness reflectivity cooperative change sequence.

[0135] In this step, the fusion coding process is based on the data reconstruction operation of the synchronization relationship, including reflectivity weight distribution, thickness correlation coefficient embedding and space-time label binding.

[0136] In the embodiment of the application, first, the physical wave characteristics are weighted according to the thickness reflectivity physical field synchronization relationship; second, the thickness characteristics are marked with a correlation coefficient after time delay correction; and finally, a thickness reflectivity correlation change sequence containing a space-time binding label is generated through fusion coding processing.

[0137] The embodiment of the application strips the optical carrier interference through phase demodulation to extract pure physical wave characteristics; delay compensation eliminates the space-time error in eddy current detection; dynamic trajectory matching constructs an inseparable physical field synchronization relationship; and fusion coding generates a correlation sequence with a synchronization label to solve the problem of control disorder caused by the out-of-sync of thickness and reflectivity data in the high-speed galvanizing scene.

[0138] In order to solve the problem of disconnection between the physical field deviation trend and the equipment execution parameters, this step generates adaptive control parameters that can be responded by the equipment through parameter conversion benchmark and running state integration. The application provides a specific embodiment, step 104, determining a real-time control parameter combination associated with a pre-set galvanizing process equipment according to the gradient change characteristics of the thickness reflectivity dynamic relationship graph, specifically including the following steps:

[0139] Step 401: locating an abnormal interval of the thickness of the galvanizing layer and the reflectivity of the galvanizing layer in the gradient jump region of the thickness reflectivity dynamic relationship graph.

[0140] In this step, the gradient jump region refers to a continuous region in the thickness reflectivity dynamic relationship graph whose change rate exceeds a preset threshold, including a gradient value mutation point coordinate set and a jump amplitude, reflecting abnormal distortion of the thickness and reflectivity correlation; reflectivity refers to the reflection ability parameter of the galvanizing layer surface to incident light waves, including reflection intensity main value, scattering rate and polarization characteristics, used to characterize the surface smoothness and uniformity of the plated layer; the abnormal interval refers to the physical coverage range of the gradient jump region on the guardrail plate surface, containing a starting position or ending position marker, interval length and abnormal level classification, used to locate the galvanizing defect region.

[0141] In the embodiment of the application, first, the region whose gradient change rate exceeds the set threshold in the thickness reflectivity dynamic relationship graph is scanned; second, the spatial coordinate range of the gradient jump region is identified; and finally, the abnormal interval of the thickness and reflectivity correlation change of the galvanizing layer that exceeds the normal fluctuation range is located, which contains a starting position marker and an abnormal coverage area parameter.

[0142] Step 402: quantitatively processing the gradient direction distribution characteristics in the abnormal interval to generate a physical field deviation trend.

[0143] In this step, the gradient direction distribution feature refers to the angle distribution state of the gradient vector in the abnormal interval, including the main direction angle, the direction dispersion degree and the vector density peak, revealing the spatial orientation of the imbalance of the coating quality; the quantization processing refers to the technology of converting the gradient direction feature into a calculable parameter, including the main direction angle mean calculation, the secondary direction deviation evaluation and the vector density integral operation; the physical field offset trend refers to the direction parameter set of the coating quality imbalance formed after quantization, including the offset dominant angle, the diffusion range angle and the trend intensity coefficient, predicting the direction of the coordinated deterioration of thickness and reflectivity.

[0144] In the embodiment of the present application, first, the gradient direction distribution feature in the abnormal interval is extracted, including the gradient vector angle distribution and the change rate density; second, the main direction distribution density and the secondary direction deviation are calculated; finally, the physical field offset trend representing the imbalance direction of the galvanized layer quality is generated, which includes the offset angle mean and the diffusion range.

[0145] Step 403: The physical field offset trend is regularized and converted based on the parameter conversion reference of the preset galvanizing process equipment, to generate a basic control parameter set.

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

[0147] In the embodiment of the present application, first, the parameter conversion reference library of the preset galvanizing process equipment is called, including the current thickness response curve and the temperature reflectivity correlation table; second, the offset angle matching conversion rule of the physical field offset trend is matched; finally, the device executable basic control parameter set containing the current adjustment amount and the zinc liquid flow value is generated through the regularized conversion processing.

[0148] Step 404: The basic control parameter set and the running state parameter of the preset galvanizing process equipment are integrated to generate a real-time control parameter combination associated with the preset galvanizing process equipment.

[0149] In this step, the running state parameter refers to the process state data of the galvanizing equipment monitored in real time, including the instantaneous value of the plating liquid temperature, the zinc ion concentration fluctuation and the conveying belt speed change rate; the integration operation refers to the technology of fusing the control parameter and the equipment state, including the parameter state compatibility verification, the physical limit constraint correction and the dynamic weight distribution.

[0150] In the embodiment of the present application, firstly, the running state parameters of the preset galvanizing process equipment are read, including real-time temperature of plating solution, zinc ion concentration and conveying belt speed; secondly, the basic control parameter set and the running state parameters are input into a dynamic constraint engine; finally, a real-time control parameter combination matching the physical limit of the equipment is output through integrated operation.

[0151] The embodiment of the present application accurately positions the plated layer in the gradient jump region and the abnormal interval; generates a physical field offset trend through gradient direction quantization; realizes reliable mapping of the physical trend to the control instruction through a parameter conversion benchmark; and ensures real-time adaptation of the control parameters to the equipment working condition through running state integration, thereby breaking through the technical bottleneck of the split between physical detection and equipment execution in traditional control.

[0152] In order to break through the bottleneck of mismatch between control instructions and physical limits of the equipment, this step ensures the safety and process optimization of the closed-loop instruction through constraint adaptation and decision rule fusion. The present application provides a specific embodiment, step 105, inputting the real-time control parameter combination into a galvanizing process dynamic decision unit of the preset galvanizing process equipment to generate a closed-loop control instruction, specifically including the following steps:

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

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

[0155] In the embodiment of the present application, firstly, the multi-dimensional control parameters in the real-time control parameter combination are identified, including current adjustment amount, zinc liquid flow value and temperature compensation amount; secondly, each parameter unit is sorted according to execution priority; finally, a time-axis-ordered galvanizing layer control parameter sequence is generated.

[0156] Step 502: extracting 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, the executable instruction elements refer to native signal units that can be directly parsed by the equipment executor, including voltage pulse width, servo valve opening percentage and resistance heating power value; the physical control instruction set refers to a device-level operation command set composed of instruction elements, including motor control signals, valve driving signals and heating control signals.

[0158] In the embodiment of the application, firstly, the current, flow and temperature adjustment instructions in the control parameter sequence are parsed; secondly, voltage pulse instructions, valve opening signals and heating power values recognizable by preset galvanizing process equipment actuators are separated; and finally, a physical control instruction set executable by the original equipment is generated.

[0159] Step 503: Perform physical constraint adaptation processing on the real-time running 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 running constraint parameters refer to the physical limit values of the current working condition of the galvanizing equipment, including the maximum tolerable current of the plating tank, the zinc liquid temperature safety threshold and the upper limit of the pump flow; the physical constraint adaptation processing refers to the technology of matching the control instructions with the equipment physical limits, including instruction amplitude limiting, rate gradual adjustment and over-limit alarm suppression; and the equipment compatible instructions refer to the instruction entities generated after the constraint adaptation, which ensure that the current value does not exceed the maximum threshold, the flow value is within the pump capacity range and the temperature value is in the safety interval.

[0161] In the embodiment of the application, firstly, the real-time running constraint parameters of the preset galvanizing process equipment are obtained, including the maximum current threshold, the plating liquid temperature safety range and the zinc pump flow limit; secondly, the instruction parameters of the physical control instruction set and the constraint parameters are input into a dynamic adaptation engine; and finally, the equipment compatible instructions that do not exceed the equipment physical limits are generated through physical constraint adaptation processing.

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

[0163] In this step, the decision-making rules refer to the preset process optimization logic in the dynamic decision-making unit, including the current priority adjustment rule, the temperature flow linkage strategy and the abnormal processing priority; and the integration operation refers to the technology of fusing the equipment instructions and the process rules, including rule weight distribution, instruction rule logic binding and conflict resolution processing.

[0164] In the embodiment of the application, firstly, the decision-making rule library of the galvanizing process dynamic decision-making unit is called, including the current thickness response priority rule and the temperature reflectivity correlation strategy; secondly, the equipment compatible instructions and the decision-making rules are subjected to weight distribution and logic binding; and finally, the closed-loop control instructions for adjusting the voltage of the galvanizing tank and the conveying speed are output through the integration operation.

[0165] The embodiment of the application realizes the ordered execution of multi-dimensional control instructions through parameter serialization; ensures the equipment-level operability through physical instruction set extraction; breaks through the bottleneck of mismatching between control instructions and equipment physical limits through constraint adaptation processing; and realizes the precise, safe and intelligent output of control instructions in the high-speed galvanizing scene through decision-making rule integration and giving the instructions process optimization intelligence.

[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 configured to: acquire dynamic light interference fringe data and continuous conductivity change data generated by the preset eddy current sensor during movement of the preset guardrail; couple and correlate the dynamic light interference fringe data and the continuous conductivity change data to generate fusion data containing thickness distribution characteristics of the galvanized layer of the preset guardrail and surface reflection characteristics of the galvanized layer; construct a thickness-reflectivity dynamic relationship graph based on a light intensity distribution mode of the fusion data; determine a real-time control parameter combination associated with the preset galvanizing process equipment according to a gradient change characteristic of the thickness-reflectivity dynamic relationship graph; and input the real-time control parameter combination into a galvanizing process dynamic decision unit of the preset galvanizing process equipment to generate a closed-loop control instruction.

[0176] The processing component 32 can include one or more processors to execute computer instructions to complete all or part of the steps in the above method. Of course, the processing component can also be 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 elements for executing the above method.

[0177] The storage component 31 is configured to store various types of data to support the operation of 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, the computing device can also include other components, such as an input / output interface, a display component, a communication component, etc.

[0179] The input / output interface provides an interface between the processing component and peripheral interface modules, which can be output devices, input devices, etc.

[0180] The communication component is configured to facilitate wired or wireless communication between the computing device and other devices, etc.

[0181] The computing device can be a physical device or an elastic computing host provided by a cloud computing platform, and the computing device can be a cloud server, and the processing component, the storage component, etc. can be basic server resources rented or purchased from the cloud computing platform.

[0182] The embodiment of the present application further provides a computer storage medium, which stores a computer program, and the computer program can realize the above-mentioned method when being executed by a computer. Figure 1 The embodiment of the present application further provides a computer storage medium, which stores a computer program, and the computer program can realize the above-mentioned method when being executed by a computer.

[0183] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-mentioned system, device and unit can refer to the corresponding processes in the foregoing method embodiments, and will not be described here.

[0184] The device embodiments described above are only schematic, wherein the units described as separate components can or can not be physically separate, and the components displayed as units can or can not be physical units, that is, can be located in one place, or can be distributed on multiple network units. Part or all of the modules can be selected according to actual needs to achieve the purpose of the embodiment scheme. Those skilled in the art can understand and implement without creative labor.

[0185] Through the description of the foregoing embodiments, those skilled in the art can clearly understand that each embodiment can be realized by means of software and necessary general hardware platform, and of course can also be realized by hardware. Based on such understanding, the foregoing technical solutions can be embodied in the form of software product, which can be stored in a computer readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes a plurality of instructions to make a computer device (which can be a personal computer, server, or network device, etc.) execute the method described in each embodiment or some parts of the embodiment.

[0186] Finally, it should be noted that: the foregoing embodiments are only used to illustrate the technical solutions of the present application, and not to limit them; although the foregoing embodiments of the present application have been described in detail, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement to some technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

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. The step of 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 includes: 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.

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 plate; 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, 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 coordinated change sequence at continuous time nodes, a dynamic relationship map of thickness reflectivity is constructed.

4. The method according to claim 3, characterized in that, The dominant fluctuation component is coupled with the thickness distribution characteristics in the time 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 physical field synchronization relationship 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.

5. 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.

6. 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.

7. A closed-loop control system for the galvanized thickness of guardrail panels based on multi-sensor fusion, applied to the closed-loop control method for the galvanized thickness of guardrail panels based on multi-sensor fusion as described in any one of claims 1-6, 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.

8. 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 6.

9. 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 6.

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