Bare conductor self-adaptive spraying system integrated with visual guidance

Abstract

The invention relates to a bare conductor self-adaptive spraying system integrated with visual guidance, and belongs to the technical field of wire and cable manufacturing. The system comprises a data acquisition module, a spraying parameter processing module, a linkage execution module and a spraying quality closed-loop correction module. Obtaining the attribute data of the bare conductor, the real-time working state information of the spraying related assembly and the post-polishing process associated data, and generating an original data set; processing the original data set, distributing parameter processing weights, and generating a target information set; constructing a spraying operation linkage parameter regulation and control model, and outputting a control instruction to regulate the working parameters and the cooperative time sequence of the assembly; coating thickness, uniformity and condensation state data are obtained and compared with a preset standard to generate a quantitative deviation result, a target information set is corrected, a spraying operation linkage parameter regulation and control model is updated, and a control instruction and working parameters of an execution assembly are adjusted. The bare conductor spraying control is realized, the spraying quality and the production efficiency are improved, and the product percent of pass is guaranteed.

Description

Technical Field

[0001] The present invention belongs to the technical field of wire and cable manufacturing, and particularly relates to a bare wire adaptive spraying system integrated with visual guidance. Background Art

[0002] As the core carrier in the power transmission and distribution system, the surface protective spraying quality of bare wires directly determines the corrosion resistance, insulation performance and outdoor service life of the wires, and is a key process link in the field of power engineering equipment manufacturing. Currently, most bare wire spraying operations adopt the traditional fixed parameter spraying mode, and during the operation, only the preset single parameter can be used to complete the spraying operation. It is difficult to adapt to the differences in the inherent properties of bare wires caused by raw materials and processing technologies during the production process, and it is also unable to respond to the working condition fluctuations of spraying-related components during long-term operation, resulting in insufficient adaptability and flexibility of the spraying operation.

[0003] In actual production scenarios, there are problems such as uneven distribution of surface impurities and small fluctuations in cross-sectional dimensions of bare wires, and the process effect of the grinding process will directly affect the state of the spraying substrate. However, the traditional spraying system lacks the ability to obtain and integrate the inherent property data of bare wires and the process-related data after grinding in real time. At the same time, there are problems of time sequence misalignment and dimensional fragmentation in the acquisition of the working state information of spraying components, and a complete set of operation data support cannot be formed, resulting in the mismatch between the setting of spraying parameters and the actual operation requirements, and quality problems such as uneven coating thickness, local missing spraying, and poor condensation effect are likely to occur.

[0004] In the parameter processing link of the existing spraying system, the fixed weight distribution method is mostly adopted, and the parameter processing logic is not dynamically adjusted in combination with the real-time changes in the surface impurity distribution density, size fluctuation amplitude of bare wires and the working condition deviation of components. The parameter priority division lacks the coupling relationship with the core influencing factors of spraying quality, resulting in the generated control parameters being unable to accurately point to the operation pain points, further reducing the accuracy of the spraying operation. At the same time, the execution link of the traditional spraying system lacks a multi-component linkage parameter control mechanism, and the working parameters and coordination time sequence of each execution component are adjusted independently, making it difficult to achieve precise matching of the links such as bare wire conveying, spraying, and feeding. Spraying process defects are likely to be caused by the misalignment of component action time sequence and the out-of-sync parameter adjustment.

[0005] Furthermore, current bare conductor coating operations are mostly conducted in an open-loop control mode, lacking the ability to monitor and correct coating quality in real time. This makes it impossible to detect deviations from preset standards in coating thickness, uniformity, and condensation state in a timely manner. Even if quality problems are found, adjustments can only be made manually afterward. This is not only inefficient and has a strong lag, but also makes it difficult to accurately correct coating parameters and the working status of execution components. As a result, coating quality is inconsistent and the defect rate is high. At the same time, repeated manual adjustments increase production time and costs, reduce overall production efficiency, and fail to meet the requirements of modern power equipment manufacturing for intelligent, efficient, and high-quality bare conductor coating operations.

[0006] Therefore, developing a bare wire coating system that can integrate multi-dimensional operational data in real time, dynamically adapt to changes in bare wire properties and component operating conditions, achieve precise execution through multi-component linkage, and possess closed-loop correction capabilities for coating quality has become a key requirement for solving the current pain points in bare wire coating processes and improving coating quality and production efficiency. Summary of the Invention

[0007] To address the aforementioned problems in the existing technology, this invention provides an integrated vision-guided adaptive spraying system for bare wires. The objective of this invention can be achieved through the following technical solution: An integrated vision-guided adaptive spraying system for bare wires, comprising: a data acquisition module, a spraying parameter processing module, a linkage execution module, and a spraying quality closed-loop correction module; The data acquisition module acquires the inherent attribute data of the bare wire and the real-time working status information of the spraying component, acquires the process-related data after the bare wire is polished, and generates the original data set by adopting a data integration and time-series alignment mechanism. The spraying parameter processing module processes the original data set through data association and priority sorting mechanism, and dynamically allocates parameter processing weights by combining the surface impurity distribution density of bare wires, size fluctuation range and component operating condition deviation, and generates target information set. The linkage execution module constructs a spraying operation linkage parameter control model, outputs control commands, and drives the bare wire execution component in linkage, adjusting the component's working parameters and coordination timing. The spraying quality closed-loop correction module acquires the coating thickness, uniformity, and condensation state data after the spraying operation in real time, compares them with preset standards to generate quantitative deviation results, dynamically corrects the target information set, updates the spraying operation linkage parameter control model, and dynamically adjusts the working parameters of the control commands and execution components.

[0008] Specifically, the data integration and time-series alignment mechanism includes: constructing a unified time axis with the bare conductor's transport speed as the core time benchmark; mapping the self-attribute data, the real-time working status information, and the process-related data to the unified time axis according to the correspondence between time nodes and spatial displacements during the bare conductor's transport process; simultaneously, normalizing the format of the mapped multi-dimensional data, interpolating and completing missing data based on process correlation, identifying, marking, and temporarily isolating abnormal data, and generating the original data set.

[0009] Specifically, the data association and priority ranking mechanism includes: establishing a multi-dimensional data association mapping relationship based on the core influencing factors of bare wire coating quality, and coupling and associating the self-attribute data, the real-time working status information and the process association data in the original data set; and classifying the priority levels according to the influence weight of the data on the coating thickness, uniformity and condensation state.

[0010] Specifically, the process of dynamically allocating parameter processing weights is as follows: based on the results of the original data set after processing by the data association and priority sorting mechanism, real-time quantitative data of impurity distribution density, size fluctuation amplitude, and component operating condition deviation on the bare wire surface are extracted. Combined with the influence of factors on the spraying operation effect and the accuracy of the target information set generation, the basic weight ratio of the factors is preset. Then, based on the real-time data deviation, the basic weight ratio is dynamically iteratively adjusted.

[0011] Specifically, the process of generating the target information set is as follows: based on the original data set processed by the data association and priority sorting mechanism, combined with dynamically allocated parameter processing weights, the self-attribute data, the real-time working status information of the spraying-related components, and the process association data after bare wire grinding are subjected to hierarchical analysis and calculation, key data are filtered out, and transformed into spraying operation control parameters, component working parameter thresholds and collaborative timing benchmarks, invalid and redundant data are eliminated and the data format is standardized and regularized to generate the target information set.

[0012] Specifically, the process of constructing the spraying operation linkage parameter control model is as follows: Based on the target information set, the spraying operation control parameters, component working parameter thresholds, and collaborative timing benchmarks are extracted. Combining the working logic and mutual linkage matching relationship of the execution components in the bare conductor spraying operation, a coupling association mapping relationship is established between the bare conductor's own attribute data and the execution component's working parameters and collaborative timing. The bare conductor's own attribute data is set as input variables, and the execution component's working parameter adjustment range and collaborative timing node are set as output variables. At the same time, the process adaptation rules of bare conductor spraying are incorporated to perform initial parameter calibration of the model, thereby generating the spraying operation linkage parameter control model.

[0013] Specifically, the process of outputting the control command is as follows: based on the spraying operation linkage parameter control model, the self-attribute data is input, combined with the target information set, and through the built-in coupling association mapping relationship and process adaptation rules, the working parameter adjustment requirements and collaborative timing matching requirements of the bare wire execution component are deduced and calculated, and the deduction and calculation results are converted into the control command.

[0014] Specifically, the process of adjusting the working parameters and coordination timing of the adjustment component is as follows: Based on the control command, the working parameter adjustment values, adjustment rates, and coordination timing nodes between components corresponding to the bare wire execution component are extracted. Then, according to the inherent working logic of the execution component in the bare wire spraying operation, based on the coordination timing nodes, the sequence of execution component startup, parameter adjustment, continuous operation, and shutdown is sorted out. According to the sequence, based on the corresponding working parameter adjustment values ​​and the adjustment rate, the working parameters of the bare wire execution component are adjusted, and the action rhythm of the component is matched in real time during the adjustment process.

[0015] Specifically, the process for generating the quantitative deviation result is as follows: the coating thickness, uniformity, and condensation state data acquired in real time after the spraying operation are standardized and quantified to generate quantitative detection data. The quantitative detection data are compared with the corresponding preset standards, and the actual difference and relative deviation rate between the quantitative detection data and the preset standards are calculated. The actual difference and the relative deviation rate are weighted and integrated based on the influence weights of coating thickness, uniformity, and condensation state on the overall quality of the spraying to generate the quantitative deviation result.

[0016] Specifically, the process of dynamically correcting the target information set is as follows: based on the quantified deviation result, the currently generated target information set is retrieved, and the parameters related to coating quality deviation in the target information set are adjusted in combination with the self-attribute data of the bare wire and the process adaptation logic of the spraying operation. The value range of the corresponding parameters is corrected according to the magnitude and direction of the deviation, while retaining the valid parameter content in the target information set that is not related to coating quality and eliminating redundant and invalid parameter information.

[0017] Specifically, the process of updating the spraying operation linkage parameter control model is as follows: taking the dynamically corrected target information set as the core update basis, and combining the quantification deviation result, the current spraying operation linkage parameter control model is retrieved, and the coupling and correlation mapping relationship between the built-in bare wire's own attribute data and the working parameters and collaborative timing of the execution component is optimized. Based on the magnitude and direction of the quantification deviation result, the corresponding parameter matching coefficient is adjusted, and the parameter value range in the corrected target information set is integrated into the parameter calibration system, and the output logic is synchronously iterated.

[0018] Specifically, the process of dynamically adjusting the working parameters of the control command and the execution component is as follows: Based on the updated spraying operation linkage parameter control model, the corrected target information set is input, and the working parameter adjustment values, adjustment rates, and collaborative timing nodes between components in the original control command are adjusted through the updated coupling association mapping relationship and parameter matching coefficient. The adjusted parameter content is integrated into the new control command, and the new control command is output to the bare wire execution component. At the same time, the new working parameter adjustment values, new adjustment rates, and new collaborative timing nodes are parsed and extracted. Combined with its current working parameter status, the working parameters of the execution component are dynamically corrected.

[0019] The beneficial effects of this invention are: It solves the problems of fragmented multi-dimensional operation data acquisition and misaligned timing in traditional systems. Through data integration and timing alignment mechanisms, it achieves integrated and standardized processing of data related to bare wire properties, component operating conditions, and grinding processes, ensuring the integrity and accuracy of the original data and laying a reliable data foundation for subsequent parameter control.

[0020] This solves the problem of fixed parameter processing weights in traditional systems, which are out of touch with actual working conditions. By dynamically allocating parameter processing weights and adjusting parameter logic in combination with the real-time status of bare wires and component operating condition deviations, the generated target information set accurately matches the spraying operation requirements, improving the targeting and accuracy of spraying parameter control.

[0021] This invention solves the problem of independent adjustment and timing misalignment of each execution component in traditional systems. By constructing a linkage parameter control model, it realizes the linkage drive and coordinated timing control of multiple components such as bare wire conveying and spraying, effectively avoiding defects such as uneven coating and local missed spraying, and improving the stability and coordination of spraying operations.

[0022] This system solves the problems of open-loop control and lagging deviation correction in traditional spraying quality control. By detecting coating quality in real time and generating quantitative deviation results, it dynamically corrects target information, updates control models, and adjusts component parameters, forming a closed-loop correction system throughout the entire process. This promptly eliminates quality deviations and improves the consistency of coating quality and product qualification rate.

[0023] The system achieves fully automated and intelligent control of the bare wire coating process, reducing labor costs and operational errors, and improving production efficiency. At the same time, the system can adapt to the operating conditions of different specifications of bare wires and components, has strong versatility and environmental adaptability, and its modular architecture design facilitates the technical transformation of existing production lines, making it of great industrial promotion value. Attached Figure Description

[0024] To facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings.

[0025] Figure 1 This is a schematic diagram of the process of an integrated vision-guided adaptive spraying system for bare wires according to the present invention. Figure 2 This is a structural block diagram of an integrated vision-guided bare wire adaptive spraying system according to the present invention; Detailed Implementation

[0026] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description of the specific implementation methods, structures, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided.

[0027] Please see Figure 1-2 An integrated vision-guided adaptive spraying system for bare wires includes: a data acquisition module, a spraying parameter processing module, a linkage execution module, and a spraying quality closed-loop correction module. The data acquisition module acquires the inherent attribute data of the bare wire and the real-time working status information of the spraying component, acquires the process-related data after the bare wire is polished, and generates the original data set by adopting a data integration and time-series alignment mechanism. The spraying parameter processing module processes the original data set through data association and priority sorting mechanism, and dynamically allocates parameter processing weights by combining the surface impurity distribution density of bare wires, size fluctuation range and component operating condition deviation, and generates target information set. The linkage execution module constructs a spraying operation linkage parameter control model, outputs control commands, and drives the bare wire execution component in linkage, adjusting the component's working parameters and coordination timing. The spraying quality closed-loop correction module acquires the coating thickness, uniformity, and condensation state data after the spraying operation in real time, compares them with preset standards to generate quantitative deviation results, dynamically corrects the target information set, updates the spraying operation linkage parameter control model, and dynamically adjusts the working parameters of the control commands and execution components.

[0028] Specifically, the data integration and time-series alignment mechanism includes: constructing a unified time axis with the bare conductor's transport speed as the core time benchmark; mapping the self-attribute data, the real-time working status information, and the process-related data to the unified time axis according to the correspondence between time nodes and spatial displacements during the bare conductor's transport process; simultaneously, normalizing the format of the mapped multi-dimensional data, interpolating and completing missing data based on process correlation, identifying, marking, and temporarily isolating abnormal data, and generating the original data set.

[0029] This embodiment is applied to an automated coating production line for UkV overhead bare conductors. Using the aforementioned automated coating device for overhead bare conductors, it achieves real-time acquisition and integration of multi-dimensional data with time-series alignment for the coating process of steel-cored aluminum stranded wire of model L-XX / YY. This provides data support for subsequent closed-loop control of coating quality. The specific process is as follows: Data acquisition module configuration: The data acquisition module is equipped with multiple types of sensors and data acquisition units to collect three core types of data in real time: Bare conductor intrinsic attribute data: acquired through conductor identification sensors and laser diameter gauges, including conductor type (preset input), actual diameter (accuracy ± α mm), material (steel core aluminum stranded wire, preset input), initial surface oxidation degree (detected by eddy current sensor, quantified as 0-Z level), and acquisition frequency of X Hz.

[0030] Real-time operating status information of spraying-related components: collected by the device's built-in pressure sensor, speed sensor, and displacement sensor, including air pump operating pressure (Pmin-Pmax MPa), first turbine pump inlet flow rate (Qmin-Qmax L / min), rotating tube speed (Nmin-Nmax r / min), lifting rod reciprocating frequency (Fmin-Fmax times / second), grinding roller holding pressure (Ymin-Ymax MPa), second turbine pump extraction flow rate (Smin-Smax L / min), with a sampling frequency of Hz.

[0031] Process-related data after bare wire polishing: acquired by surface roughness meter, weight sensor, and dust concentration sensor, including the surface roughness of the wire after polishing (Ra, unit μm), real-time collection amount of polishing debris (Mmin-Mmax g / min), and surface cleanliness of the wire (quantified by dust residue, Cmin-Cmax mg / m), with a sampling frequency of X Hz.

[0032] Implementation of data integration and time-series alignment mechanisms: Unified timeline construction: Using the bare wire conveying speed as the core time reference, the standard conveying speed of the production line is set as Ym / min (i.e., Vm / s), and a unified time axis is constructed: The smallest division on the time axis is T seconds, which corresponds to a spatial displacement of Lm for the bare conductor. Each time point (t=T,2T,...,n×T) is bound to the spatial location of the conductor (s=L,2L,...,n×L), forming a two-dimensional "time-space" index.

[0033] Multi-dimensional data mapping: The three types of data are mapped to a unified time axis according to the correspondence between "time node - spatial displacement": To address the issue of inconsistent acquisition frequencies, the XHz self-attribute data and process-related data are upscaled to ZHz using interpolation to align with the acquisition frequency of the working status information, ensuring data integrity at each time point.

[0034] Format normalization processing: Standardize the format of the mapped multi-dimensional data: Numerical data should be uniformly retained to two decimal places, and units should be labeled according to international standards (such as pressure MPa, rotation speed r / min, roughness μm). Categorized data (such as conductor type and material) are coded (model L-XX / YY is coded as AB, and steel-cored aluminum stranded wire is coded as CD). The graded quantitative data (such as oxidation degree and cleanliness) retains the original grade and supplements the corresponding descriptions of the values ​​(such as oxidation degree K grade corresponding to oxide layer thickness D1-D2mm).

[0035] Handling missing and outlier data: Missing data interpolation completion: When data is missing at a certain time point (such as a temporary failure of the surface roughness sensor), linear interpolation is used to complete the data based on process correlation; if more than K consecutive nodes are missing, a weighted average interpolation of the M adjacent nodes is used.

[0036] Abnormal data identification and isolation: Set process thresholds for each data item (based on device operating parameters and coating quality requirements), such as the normal range of air pump pressure P1-P2MPa and the normal range of rotary tube speed N1-N2r / min. When data exceeding the threshold is detected, immediately mark it as abnormal and temporarily isolate it. At the same time, retain the time-space index of the data for easy subsequent fault tracing.

[0037] Original dataset generation: After constructing a unified timeline, mapping multi-dimensional data, normalizing the format, and handling missing and outlier data, a structured raw data set is generated. Each data record in this set contains complete information including "timestamp-spatial displacement-multi-dimensional data items-data status," maintaining temporal consistency and spatial correlation between the data and the bare wire coating process. This data can be directly used for subsequent applications such as coating process parameter optimization and quality defect tracing. Specifically, the data association and priority ranking mechanism includes: establishing a multi-dimensional data association mapping relationship based on the core influencing factors of bare wire coating quality, and coupling and associating the self-attribute data, the real-time working status information and the process association data in the original data set; and classifying the priority levels according to the influence weight of the data on the coating thickness, uniformity and condensation state.

[0038] Specifically, the process of dynamically allocating parameter processing weights is as follows: based on the results of the original data set after processing by the data association and priority sorting mechanism, real-time quantitative data of impurity distribution density, size fluctuation amplitude, and component operating condition deviation on the bare wire surface are extracted. Combined with the influence of factors on the spraying operation effect and the accuracy of the target information set generation, the basic weight ratio of the factors is preset. Then, based on the real-time data deviation, the basic weight ratio is dynamically iteratively adjusted.

[0039] Specifically, the process of generating the target information set is as follows: based on the original data set processed by the data association and priority sorting mechanism, combined with dynamically allocated parameter processing weights, the self-attribute data, the real-time working status information of the spraying-related components, and the process association data after bare wire grinding are subjected to hierarchical analysis and calculation, key data are filtered out, and transformed into spraying operation control parameters, component working parameter thresholds and collaborative timing benchmarks, invalid and redundant data are eliminated and the data format is standardized and regularized to generate the target information set.

[0040] In this embodiment, continuing the application scenario of the automatic coating production line for UkV overhead bare conductors, and targeting steel-cored aluminum stranded wire of model L-XX / YY, based on the aforementioned structured raw data set, the coating parameter processing module completes data association, priority sorting, and dynamic weight allocation, ultimately generating a target information set that can be directly used for coating operation control, supporting real-time optimization of the coating process and quality closed-loop control. The specific process is as follows: Spraying parameter processing module configuration: Receive "self-attribute data - real-time working status information - process-related data" from the original data set, and combine it with the surface impurity distribution density (ρ, unit: individuals / mm) of the bare wire. 2 The system uses three core factors—size fluctuation range (ΔD, in mm), component operating condition deviation (ΔW, dimensionless)—to complete data processing and output target information.

[0041] Implementation of data association and priority sorting mechanisms: Based on the core influencing factors of bare wire coating quality, a multi-dimensional data coupling correlation model is established: Couple self-attribute data with process-related data: correlate the size fluctuation range (ΔD) of bare wires with the surface impurity distribution density (ρ) after polishing to clarify the mapping logic that "the greater the size fluctuation, the higher the risk of impurity residue". Couple real-time working status information with process-related data: associate component operating condition deviations (ΔW, such as air pump pressure deviation, rotating tube speed deviation) with surface roughness (Ra) after grinding, and establish an association rule of "operating condition deviation exceeds threshold → roughness compliance rate decreases"; Three types of data are coupled across dimensions: with coating uniformity as the core objective, the data is linked to "bare wire size fluctuation + air pump pressure deviation + impurity distribution density", forming a mapping relationship of multi-factor synergistic influence.

[0042] Priority hierarchy: Based on the weight of the data's impact on key indicators of coating quality, priorities are divided into three levels: First priority (weight ratio P1): Component condition deviation data in real-time working status information (such as air pump pressure deviation, rotary tube speed deviation) directly affect coating uniformity and thickness accuracy, and serve as the core control basis. Secondary priority (weight ratio P2): Data related to the dimensional fluctuation range in its own attribute data, which affects the coating thickness adaptability, and serves as a secondary control basis; Level 3 Priority (Weight Ratio P3): Surface impurity distribution density data in process-related data, which affects the coating's condensation state and adhesion, and serves as an auxiliary control basis; the priority satisfies P1 > P2 > P3, and P1 + P2 + P3 = 100%.

[0043] Implementation of dynamic parameter allocation for weight processing: Based on the degree of influence of factors on the spraying effect and the accuracy of target information, the following basic weighting is preset: Component operating condition deviation (ΔW) base weight P1_base=K% Size fluctuation range (ΔD) base weight P2_base=L% Surface impurity distribution density (ρ) base weight P3_base=M%; (K+L+M=100); Based on the real-time quantization data deviation in the original dataset, the weights are dynamically adjusted: When the component operating condition deviation ΔW > ΔW0 (preset deviation threshold), P1 is increased to K1% (K1 > K), and P2 is simultaneously decreased to L1% and P3 to M1% (K1 + L1 + M1 = 100), thus strengthening the priority of operating condition control. When the bare conductor size fluctuation range ΔD > ΔD0 (preset fluctuation threshold), P2 is increased to L2% (L2 > L), and P1 is appropriately decreased to K2% and P3 to M2%, focusing on thickness adaptability control; When the surface impurity distribution density ρ > ρ0 (preset density threshold), P3 is increased to M3% (M3 > M), and P1 is slightly decreased to K3% and P2 to L3% to ensure the coating condensation effect.

[0044] Implementation of target information set generation: Based on the original data after association sorting and weight allocation, a target information set is generated through hierarchical parsing and computation: Layered analysis: First-level priority data focuses on analyzing the component operating condition deviation threshold and control range; second-level priority data analyzes the coating thickness adaptation range corresponding to size fluctuations; and third-level priority data analyzes the condensation auxiliary parameters corresponding to impurity density. Key data screening: Remove redundant data that is not related to coating quality (such as sensor model and acquisition unit number), and retain core control data; Parameter conversion: The key data after screening is converted into three types of core information: spraying operation control parameters (such as air pump pressure control range P_reg, rotary tube speed control value N_reg), component working parameter thresholds (such as air pump pressure upper limit P_max, grinding roller holding pressure lower limit Y_min), and coordinated timing benchmarks (such as the coordinated ratio of lifting rod reciprocating frequency and wire conveying speed F-V_ratio). Formatting: The converted information is standardized, numerical parameters are retained to two decimal places, threshold parameters are labeled with the "upper limit - lower limit" range, and time series references are labeled with collaborative logic, ultimately generating a set of structured target information.

[0045] Specifically, the process of constructing the spraying operation linkage parameter control model is as follows: Based on the target information set, the spraying operation control parameters, component working parameter thresholds, and collaborative timing benchmarks are extracted. Combining the working logic and mutual linkage matching relationship of the execution components in the bare conductor spraying operation, a coupling association mapping relationship is established between the bare conductor's own attribute data and the execution component's working parameters and collaborative timing. The bare conductor's own attribute data is set as input variables, and the execution component's working parameter adjustment range and collaborative timing node are set as output variables. At the same time, the process adaptation rules of bare conductor spraying are incorporated to perform initial parameter calibration of the model, thereby generating the spraying operation linkage parameter control model.

[0046] Specifically, the process of outputting the control command is as follows: based on the spraying operation linkage parameter control model, the self-attribute data is input, combined with the target information set, and through the built-in coupling association mapping relationship and process adaptation rules, the working parameter adjustment requirements and collaborative timing matching requirements of the bare wire execution component are deduced and calculated, and the deduction and calculation results are converted into the control command.

[0047] Specifically, the process of adjusting the working parameters and coordination timing of the adjustment component is as follows: Based on the control command, the working parameter adjustment values, adjustment rates, and coordination timing nodes between components corresponding to the bare wire execution component are extracted. Then, according to the inherent working logic of the execution component in the bare wire spraying operation, based on the coordination timing nodes, the sequence of execution component startup, parameter adjustment, continuous operation, and shutdown is sorted out. According to the sequence, based on the corresponding working parameter adjustment values ​​and the adjustment rate, the working parameters of the bare wire execution component are adjusted, and the action rhythm of the component is matched in real time during the adjustment process.

[0048] In this embodiment, continuing the application scenario of the UkV overhead bare conductor automatic coating production line, for steel-cored aluminum stranded wire of model L-XX / YY, based on the aforementioned target information set, a spraying operation linkage parameter control model is constructed through the linkage execution module, outputting precise control commands, and driving the core execution components such as overhead wire feeding roller B, air pump F, rotating tube C, and grinding roller G in linkage, so as to achieve dynamic matching of working parameters and collaborative timing, and ensure the automation and precise linkage of coating operations.

[0049] Linked execution module configuration: The components include a bare wire conveying assembly (overhead feed roller B, overhead take-up roller D), a coating assembly (air pump F, rotating tube C, lifting rod E), and a grinding assembly (grinding roller G, turbine pump H). The module communicates with the drive controllers of each component via an industrial bus, enabling real-time transmission of control commands and reception of execution status feedback.

[0050] Construction and Implementation of Spraying Operation Linkage Parameter Control Model: Three types of key information are extracted from the target information set as the basis for model construction: Spraying operation control parameters: air pump pressure reference value P, rotary tube speed reference value N, grinding roller holding pressure reference value Y, and wire feed speed reference value V. Component operating parameter thresholds: air pump pressure upper and lower limits P low, P high; rotating tube speed upper and lower limits N low, N high; grinding roller holding pressure upper and lower limits Y low, Y high; wire feeding speed upper and lower limits V low, V high. Coordination timing reference: the coordination ratio K1 between the reciprocating frequency of the lifting rod and the wire feeding speed, and the coordination ratio K2 between the wire feeding speed and the rotation speed of the rotating tube.

[0051] Establishment of coupling association mapping relationship: Using the bare conductor's own attribute data as input variables, and the adjustment range of the component's working parameters and the collaborative timing nodes as output variables, a simple coupling relationship is established: Actual diameter D of the wire: The larger the diameter, the higher the minimum adjustment value P of the air pump pressure is lowered, and the adjustment value N of the rotation speed of the rotating tube is raised accordingly to ensure that the paint is fully covered and evenly applied. Initial surface oxidation degree O: The higher the oxidation degree level, the greater the adjustment value Y of the grinding roller holding pressure, and the earlier the start timing node T1 of the grinding action, so as to enhance the grinding effect to remove the oxide layer; Dimensional fluctuation range ΔD: The greater the fluctuation, the slower the adjustment rate V of the feed line speed. The adaptation value N of the rotating tube speed is dynamically corrected according to the fluctuation range to compensate for the coating effect caused by dimensional deviation.

[0052] Process adaptation rules and initial parameter calibration: Coating thickness rule: air pump pressure × rotating tube speed ÷ wire feeding speed = fixed coating thickness coefficient K thickness, which ensures consistent coating thickness under different working conditions; Grinding-spraying coordination rule: After the grinding roller pressure stabilizes, delay for a period of time T before starting the air pump to supply air, to avoid spraying before the wires are completely ground; Safety threshold rules: The operating parameters of any component must not exceed the upper and lower limits set in the target information set; if they do, a speed reduction protection will be triggered immediately. Initial parameter calibration uses preset simple correlation coefficients according to the rules to ensure that the initial output of the model meets the basic process requirements.

[0053] Control command output implementation: Input data and model calculations: The real-time collected data on the bare conductor's intrinsic properties (actual diameter D, oxidation degree O, and dimensional fluctuation range ΔD) are input into the linkage parameter control model, and derivation and calculation are performed in conjunction with the target information set: First, calculate the basic adjustment value of each component based on the coupling relationship (e.g., air pump pressure adjustment value P_adjustment = P_base + K_diameter pressure × (D - standard diameter)). Then adjust the adjustment value according to the process adaptation rules (e.g., the rotation tube speed adjustment value N_adjust = N_base × (V_base ÷ actual feed line speed V_actual)). Finally, check whether the adjustment value is within the threshold range. If it exceeds the range, take the threshold boundary value (e.g., P adjustment must not be higher than P high and must not be lower than P low).

[0054] Control command conversion: The model calculation results are converted into standardized control instructions. Each instruction includes a component name, target adjustment value, adjustment rate, and timing flag, as shown in the example below: Grinding roller G: Adjust to the target holding pressure Y, the adjustment rate is V pressure, and the timing mark is the start reference time T0; Overhead wire feeding roller B: Adjust to the target wire feeding speed V_adjustment, the adjustment rate is V_speed, and the timing mark is T0+T1; Air pump F: Adjust to the target pressure P, the adjustment rate is V air, and the timing mark is T0+T delay (grinding stabilization delay time). Rotating tube C: Adjust to the target speed N adjustment, the adjustment rate is V revolutions, and the timing mark is T0+T delay+T2; Lifting rod E: Adjust to the target reciprocating frequency F, the adjustment rate is V frequency, and the timing mark is T0+T delay+T3.

[0055] Implementation of working parameters and collaborative timing adjustment: Timing node analysis and action sequence planning: Based on the timing markers of control commands, the sequence of execution components—"start-adjustment-operation-shutdown"—is traced: At time T0: Start the grinding roller G, raise it to the target holding pressure according to the set adjustment rate and keep it stable; At time T0+T1: Adjust the wire feeding speed of the overhead wire feeding roller B to synchronously match the rotation speed of the overhead wire take-up roller D to ensure that the wire is taut and not loose; T0+T delay time: Start the air pump F and raise it to the target pressure according to the adjustment rate. At the same time, start the turbine pump H to ensure normal suction and supply of paint. At time T0+T+T2: Start the rotating tube C, increase it to the target speed according to the adjustment rate, and apply the coating to the wire by rotation using a brush; At time T0+T+T3: Adjust the reciprocating frequency of the lifting rod E to the target value, and coordinate with the action of the rotating tube C to compensate for the uniformity of coating; Shutdown sequence: When the operation is finished, first shut down the air pump F and the rotating pipe C → after the paint residue has been cleaned, reduce the holding pressure of the grinding roller G → finally reduce the speed of the feed roller B and take-up roller D until they stop.

[0056] Real-time parameter adjustment and motion rhythm matching: During the adjustment process, the action rhythm is matched in real time according to the component linkage logic: Speed ​​coordination: The wire feeding speed and the rotation speed of the rotating tube C maintain a preset coordination ratio K2. If the wire feeding speed changes due to size fluctuations, the rotation speed of the rotating tube C is adjusted synchronously and proportionally to ensure consistent coating density. Pressure-frequency coordination: When the pressure of air pump F changes, the reciprocating frequency of lifting rod E is adjusted synchronously according to the mixing ratio K1 to avoid local coating accumulation; Status feedback correction: Receive the actual operating parameters of each component in real time. If the deviation from the target value exceeds the allowable range Δ, reduce the adjustment rate to avoid parameter overshoot until the parameters stabilize within the target range.

[0057] Through the above-mentioned coordinated adjustment, the entire process of bare wire coating, including grinding, conveying, coating and compensation, is coordinated to ensure that the coating thickness uniformity deviation is controlled within the allowable range and the condensation adequacy rate meets the process requirements.

[0058] Specifically, the process for generating the quantitative deviation result is as follows: the coating thickness, uniformity, and condensation state data acquired in real time after the spraying operation are standardized and quantified to generate quantitative detection data. The quantitative detection data are compared with the corresponding preset standards, and the actual difference and relative deviation rate between the quantitative detection data and the preset standards are calculated. The actual difference and the relative deviation rate are weighted and integrated based on the influence weights of coating thickness, uniformity, and condensation state on the overall quality of the spraying to generate the quantitative deviation result.

[0059] Specifically, the process of dynamically correcting the target information set is as follows: based on the quantified deviation result, the currently generated target information set is retrieved, and the parameters related to coating quality deviation in the target information set are adjusted in combination with the self-attribute data of the bare wire and the process adaptation logic of the spraying operation. The value range of the corresponding parameters is corrected according to the magnitude and direction of the deviation, while retaining the valid parameter content in the target information set that is not related to coating quality and eliminating redundant and invalid parameter information.

[0060] Specifically, the process of updating the spraying operation linkage parameter control model is as follows: taking the dynamically corrected target information set as the core update basis, and combining the quantification deviation result, the current spraying operation linkage parameter control model is retrieved, and the coupling and correlation mapping relationship between the built-in bare wire's own attribute data and the working parameters and collaborative timing of the execution component is optimized. Based on the magnitude and direction of the quantification deviation result, the corresponding parameter matching coefficient is adjusted, and the parameter value range in the corrected target information set is integrated into the parameter calibration system, and the output logic is synchronously iterated.

[0061] Specifically, the process of dynamically adjusting the working parameters of the control command and the execution component is as follows: Based on the updated spraying operation linkage parameter control model, the corrected target information set is input, and the working parameter adjustment values, adjustment rates, and collaborative timing nodes between components in the original control command are adjusted through the updated coupling association mapping relationship and parameter matching coefficient. The adjusted parameter content is integrated into the new control command, and the new control command is output to the bare wire execution component. At the same time, the new working parameter adjustment values, new adjustment rates, and new collaborative timing nodes are parsed and extracted. Combined with its current working parameter status, the working parameters of the execution component are dynamically corrected.

[0062] In this embodiment, continuing the application scenario of the automatic coating production line for UkV overhead bare conductors, and targeting steel-cored aluminum stranded wire of model L-XX / YY, based on the operation results output by the aforementioned linkage execution module, the coating quality data is collected in real time through the coating quality closed-loop correction module to complete quantitative deviation analysis, target information set correction, linkage parameter control model update, and dynamic adjustment of control commands. The specific process is as follows: Spray coating quality closed-loop correction module configuration: The module is equipped with a data acquisition unit, a deviation calculation unit, a correction and update unit, and a parameter adjustment unit. It is compatible with three types of testing equipment: coating thickness detector, uniformity monitor, and condensation state sensor. At the same time, it communicates bidirectionally with the linkage execution module and the spraying parameter processing module through an industrial bus, which can retrieve the target information set and linkage parameter control model in real time and output the corrected control commands synchronously.

[0063] Implementation of Quantitative Bias Result Generation: Three types of core quality data are collected in real time after the spraying operation and then processed in a standardized and quantitative manner: Coating thickness test data: The thickness data of the entire conductor segment is obtained through a coating thickness tester and quantified into a coating thickness value H; Coating uniformity test data: The thickness difference of different sections is analyzed by a uniformity monitor and quantified into a uniformity index U. Coating curing state data: The degree of coating curing is detected by a curing state sensor and quantified as curing state level S.

[0064] Deviation comparison and calculation: The quantified test data is compared with the preset standard to calculate the core deviation index: Actual differences: Calculate the actual thickness difference ΔH = H - preset thickness standard, the actual uniformity difference ΔU = U - preset uniformity standard, and the actual condensation state difference ΔS = S - preset condensation state standard respectively; Relative deviation rate: Calculate the relative deviation rates R_H, R_U, and R_S (ratios of deviation values ​​to preset standards) for the three types of data.

[0065] Weighted integration generates quantitative bias results: Combining the weights of the three quality indicators on the overall coating quality (thickness weight W1, uniformity weight W2, and condensation state weight W3, satisfying W1+W2+W3=1), the actual difference and relative deviation rate are weighted and integrated: Weighted processing logic: Quantification deviation result ΔQ = (ΔH×W1 + ΔU×W2 + ΔS×W3) × (R_H×W1 + R_U×W2 + R_S×W3); Results explanation: A positive ΔQ value indicates that the quality is better than the standard, a negative value indicates that the quality does not meet the standard, and the larger the absolute value, the more significant the deviation.

[0066] Dynamic modification and implementation of target information set: Retrieve the currently active target information set and filter the core parameters directly related to coating quality: Thickness-related parameters: air pump pressure reference value P, rotary tube speed reference value N, wire feeding speed reference value V; Uniformity-related parameters: Coordination ratios K1 and K2, and the adjustment range of the lifting rod reciprocating frequency; Condensation state related parameters: air pump pressure threshold range P low to P high, rotating tube speed threshold range N low to N high.

[0067] Parameter tuning and ensemble optimization: Based on the quantification deviation result ΔQ, and combined with the bare wire's own attribute data (diameter D, oxidation degree O, dimensional fluctuation range ΔD) and process adaptation logic, the associated parameters are corrected: If ΔQ is negative and caused by insufficient thickness: expand the range of air pump pressure (increase P base, relax P height), and simultaneously increase the reference value N base of the rotating tube speed to ensure sufficient coating supply; If ΔQ is negative and this is due to non-uniformity: optimize the coordination ratios K1 and K2, reduce the fluctuation range of the lifting rod reciprocating frequency adjustment, and improve the accuracy of motion coordination; If ΔQ is negative and this is due to insufficient condensation: adjust the lower limit P of the air pump pressure threshold to ensure that the coating atomization effect matches the condensation requirements; Retain valid parameters that are not related to quality (such as safety protection thresholds), eliminate redundant historical debugging parameters, and form a corrected target information set.

[0068] Implementation of updated spraying operation linkage parameter control model: Retrieve the current spraying operation linkage parameter control model, and use the corrected target information set and quantified deviation result ΔQ as the core basis to clarify the optimization direction: The direction of deviation determines the optimization trend (e.g., if the thickness is insufficient, the positive correlation between "diameter D and air pump pressure P" is strengthened). The absolute value of the deviation determines the optimization range (the larger the absolute value of ΔQ, the greater the adjustment range of the parameter matching coefficient).

[0069] Iterate over the built-in coupling and association mapping relationships in the model: Adjust parameter matching coefficients: such as optimizing K-diameter pressure (correlation coefficient between diameter and air pump pressure) and K-oxygen pressure (correlation coefficient between oxidation degree and grinding pressure). If ΔQ is caused by insufficient thickness and D being too large, increase K-diameter pressure so that the air pump pressure adjustment range increases synchronously when the diameter increases. Integrate into the new parameter calibration system: Update the parameter value range in the corrected target information set (such as the adjusted P_low-P_high) to the model calibration system and constrain the output parameter boundaries; Synchronous iterative output logic: ensures that the model, based on the new relationships, outputs working parameters, collaborative timing nodes, and quality requirements that are accurately matched.

[0070] Dynamic adjustment of control commands and execution component operating parameters: Based on the updated linkage parameter control model, the corrected target information set is input, and the original control commands are adjusted through the optimized coupling correlation mapping relationship and parameter matching coefficients: Adjust working parameters: such as the original air pump pressure adjustment value P, adjust it upward or downward according to the deviation of ΔQ to generate a new pressure adjustment value P new; Optimize the adjustment rate: When the absolute value of the deviation is large, appropriately increase the adjustment rate (e.g., Vgasnew > Vgas) to accelerate quality correction; when the deviation is small, reduce the adjustment rate to avoid parameter oscillation. Correct the timing node of the coordination: If there is a deviation in uniformity, adjust the start timing difference T2 between the rotating tube and the lifting rod to generate a new timing node T2 new.

[0071] Command output: The adjusted parameters are integrated into new control commands and output to the actuators such as air pump F, rotary tube C, grinding roller G, and lifting rod E; Parameter parsing and correction: After receiving a new control command, each component parses and extracts the new working parameter adjustment value, new adjustment rate, and new coordination timing node, and dynamically corrects the working parameters in combination with its own current working status (such as the current pressure of the air pump and the current speed of the rotating tube). Closed-loop feedback: After parameter adjustment, the module continuously collects coating quality data and repeats the above process to achieve a cyclical iteration of "detection-correction-optimization" until the quantification deviation result ΔQ is within the allowable range.

[0072] Through this closed-loop correction mechanism, the target information set and linkage parameter control model are continuously optimized, so that the coating quality deviation is gradually reduced, ensuring that the coating thickness, uniformity and condensation state remain stable within the preset standard range during long-term operation.

[0073] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. An integrated vision-guided adaptive spraying system for bare wires, characterized in that, include: Data acquisition module, spraying parameter processing module, linkage execution module, and spraying quality closed-loop correction module; The data acquisition module acquires the inherent attribute data of the bare wire and the real-time working status information of the spraying component, acquires the process-related data after the bare wire is polished, and generates the original data set by adopting a data integration and time-series alignment mechanism. The spraying parameter processing module processes the original data set through data association and priority sorting mechanism, and dynamically allocates parameter processing weights by combining the surface impurity distribution density of bare wires, size fluctuation range and component operating condition deviation, and generates target information set. The linkage execution module constructs a spraying operation linkage parameter control model, outputs control commands, and drives the bare wire execution component in linkage, adjusting the component's working parameters and coordination timing. The spraying quality closed-loop correction module acquires the coating thickness, uniformity, and condensation state data after the spraying operation in real time, compares them with preset standards to generate quantitative deviation results, dynamically corrects the target information set, updates the spraying operation linkage parameter control model, and dynamically adjusts the working parameters of the control commands and execution components.

2. The system according to claim 1, characterized in that, The data integration and time-series alignment mechanism specifically includes: constructing a unified time axis with the bare wire conveying speed as the core time benchmark, and mapping the self-attribute data, the real-time working status information, and the process-related data to the unified time axis according to the correspondence between time nodes and spatial displacements during the bare wire conveying process; Simultaneously, the mapped multi-dimensional data is normalized, missing data is interpolated based on process correlation to complete it, and abnormal data is identified, marked, and temporarily isolated to generate the original data set.

3. The system according to claim 1, characterized in that, The data association and priority ranking mechanism specifically includes: establishing a multi-dimensional data association mapping relationship based on the core influencing factors of bare wire coating quality, coupling and associating the self-attribute data, the real-time working status information and the process association data in the original data set; and classifying the priority levels according to the influence weight of the data on the coating thickness, uniformity and condensation state.

4. The system according to claim 1, characterized in that, The specific process of dynamically allocating parameter processing weights is as follows: Based on the results of the original data set after processing by the data association and priority sorting mechanism, real-time quantitative data of impurity distribution density, size fluctuation range, and component operating condition deviation on the bare wire surface are extracted. Combined with the influence of factors on the spraying operation effect and the accuracy of the target information set generation, the basic weight ratio of the factors is preset. Then, the basic weight ratio is dynamically iteratively adjusted according to the real-time data deviation.

5. The system according to claim 1, characterized in that, The specific process of generating the target information set is as follows: based on the original data set processed by the data association and priority sorting mechanism, combined with the dynamically allocated parameter processing weights, the self-attribute data, the real-time working status information of the spraying-related components and the process association data after the bare wire grinding are analyzed and calculated in layers to filter out key data. The data is then converted into spraying operation control parameters, component working parameter thresholds, and collaborative timing benchmarks. Invalid and redundant data are removed, and the data format is standardized and regularized to generate the target information set.

6. The system according to claim 1, characterized in that, The specific process of constructing the spraying operation linkage parameter control model is as follows: Based on the target information set, extract the spraying operation control parameters, component working parameter thresholds and collaborative timing benchmarks, and combine the operation logic and mutual linkage matching relationship of the execution components in the bare conductor spraying operation to establish the coupling association mapping relationship between the bare conductor's own attribute data and the working parameters and collaborative timing of the execution components; The bare conductor's own attribute data is set as input variables, and the working parameter adjustment range and collaborative timing node of the execution component are set as output variables. At the same time, the process adaptation rules of bare conductor spraying are incorporated to perform initial parameter calibration of the model, thereby generating the spraying operation linkage parameter control model.

7. The system according to claim 1, characterized in that, The specific process of outputting the control command is as follows: based on the spraying operation linkage parameter control model, the self-attribute data is input, combined with the target information set, and through the built-in coupling association mapping relationship and process adaptation rules, the working parameter adjustment requirements and collaborative timing matching requirements of the bare wire execution component are deduced and calculated, and the deduction and calculation results are converted into the control command.

8. The system according to claim 1, characterized in that, The specific process of adjusting the working parameters and coordination timing of the adjustment component is as follows: Based on the control command, extract the adjustment values ​​of the working parameters, the adjustment rate and the coordination timing nodes between the components corresponding to the bare wire execution component; Based on the inherent operational logic of the execution components in the bare conductor spraying operation, and based on the aforementioned collaborative timing nodes, the sequence of execution component startup, parameter adjustment, continuous operation, and shutdown is determined. In accordance with this sequence, the working parameters of the bare conductor execution components are adjusted according to the corresponding working parameter adjustment values ​​and the adjustment rate, and the action rhythm of the components is matched in real time during the adjustment process.

9. The system according to claim 1, characterized in that, The specific process for generating the quantitative deviation results is as follows: the coating thickness, uniformity, and condensation state data acquired in real time after the spraying operation are standardized and quantified to generate quantitative detection data; The quantitative test data is compared with the corresponding preset standards, and the actual difference and relative deviation rate between the quantitative test data and the preset standards are calculated. The actual difference and the relative deviation rate are weighted and integrated by combining the influence weights of coating thickness, uniformity and condensation state on the overall quality of spraying, and the quantitative deviation result is generated.

10. The system according to claim 1, characterized in that, The specific process of dynamically correcting the target information set is as follows: Based on the quantified deviation result, the currently generated target information set is retrieved, and the parameter content related to coating quality deviation in the target information set is adjusted in combination with the self-attribute data of the bare wire and the process adaptation logic of the spraying operation. The value range of the corresponding parameter is corrected according to the magnitude and direction of the deviation, while retaining the valid parameter content in the target information set that is not related to coating quality and eliminating redundant and invalid parameter information.

11. The system according to claim 1, characterized in that, The specific process of updating the spraying operation linkage parameter control model is as follows: taking the dynamically corrected target information set as the core update basis, combined with the quantification deviation result, the current spraying operation linkage parameter control model is retrieved, and the coupling and correlation mapping relationship between the built-in bare wire's own attribute data and the working parameters and collaborative timing of the execution component is optimized. Based on the magnitude and direction of the quantification deviation result, the corresponding parameter matching coefficient is adjusted. At the same time, the parameter value range in the corrected target information set is integrated into the parameter calibration system, and the output logic is synchronously iterated.

12. The system according to claim 1, characterized in that, The specific process of dynamically adjusting the working parameters of the control command and the execution component is as follows: Based on the updated spraying operation linkage parameter control model, the corrected target information set is input, and the working parameter adjustment values, adjustment rates, and collaborative timing nodes between components in the original control command are adjusted through the updated coupling association mapping relationship and parameter matching coefficient. The adjusted parameter content is integrated into the new control command, and the new control command is output to the bare wire execution component. At the same time, the new working parameter adjustment values, new adjustment rates, and new collaborative timing nodes are parsed and extracted. Combined with its current working parameter status, the working parameters of the execution component are dynamically corrected.

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