A geomembrane edge-welding parameter optimization control method, device and medium

Abstract

The embodiment of the specification discloses a geomembrane edge welding parameter optimization control method, equipment and medium, relates to the welding technical field, and the method comprises the following steps: collecting multi-dimensional micro-environment parameters of the current welding point through the multi-dimensional environment perception module integrated in the welding execution equipment, identifying the current welding environment based on the multi-dimensional micro-environment parameters, and determining the current welding environment type; dynamically regulating and controlling the working parameters of the treatment equipment according to the current welding environment type, determining the self-adaptive optimized interface treatment parameter set, determining the welding parameter compensation value through the interface treatment parameter set; based on the welding parameter compensation value, the welding parameter adjustment instruction is determined, the welding parameter adjustment instruction is sent to the control unit of the welding execution equipment, and the welding parameter optimization is realized. Responding to the environment and the treatment result, the precise matching of the welding energy input and the instantaneous demand of the material is realized, and the scientificity and adaptability of the parameter setting are significantly improved.

Description

Technical Field

[0001] This specification relates to the field of welding technology, and in particular to a method, equipment and medium for optimizing and controlling welding parameters for geomembrane edge wrapping. Background Technology

[0002] As a crucial seepage-proof material in water conservancy, environmental protection, and municipal engineering projects, the quality of geomembrane edge welding directly determines the overall sealing reliability and service life of the project. With the widespread application of automated welding equipment, precise control of welding parameters has become a core aspect of ensuring welding quality. In existing technologies, geomembrane edge welding often employs fixed process parameters or simple threshold judgments based on a single environmental sensor (such as an ambient temperature and humidity meter) for parameter fine-tuning. While these methods can maintain basic welding quality under normal environmental conditions, under extreme conditions such as low temperature and high humidity, due to complex environmental interference and drastic dynamic changes in the material interface state, existing control strategies reveal significant limitations.

[0003] At the environmental perception level, sensors are typically placed on the outer supports or operating platforms of welding equipment, without considering the interference of welding heat radiation, airflow from equipment movement, and terrain microclimate on the measurements. The data collected by the sensors cannot accurately reflect the real-time microenvironmental state of the welding point. In the interface treatment process, a pre-set open-loop treatment mode with fixed duration and temperature is commonly used, such as uniformly setting hot air temperature and treatment time. This ignores the influence of interface temperature / humidity changes, posing a risk of insufficient treatment (residual moisture at the interface or temperature not meeting process requirements) or excessive treatment (thermal damage to the film surface, material performance degradation). This leads to unstable heat conduction conditions in the welding molten zone, resulting in defects such as incomplete welds, bubbles, and edge warping. When adjusting welding parameters, static interface parameters after treatment are usually used for lookup-based compensation, or fixed compensation coefficients corresponding to environmental parameters are directly applied. The treatment process and the welding parameter generation process are disconnected, leading to a mismatch between the welding heat input and the actual thermodynamic state of the material after treatment. This makes it unable to adapt to changes in the thermal response characteristics of the material due to aging, contamination, or microenvironmental differences, further amplifying the dispersion of welding quality.

[0004] Therefore, in the process of optimizing and controlling the welding parameters of geomembrane edging, there is a lack of precise perception of the microenvironment of the welding point, and the interface treatment process and the generation of welding parameters are disconnected from each other. There is a problem of mismatch between the treatment effect and the welding process parameters, resulting in poor welding quality stability and high defect rate under extreme conditions, which makes it difficult to meet the stringent requirements of high-standard seepage prevention projects for the long-term reliability of welded joints. Summary of the Invention

[0005] This specification provides one or more embodiments of a method, equipment, and medium for optimizing and controlling welding parameters for geomembrane edging, which addresses the following technical problem: In the process of optimizing and controlling welding parameters for geomembrane edging, there is a lack of precise perception of the microenvironment at the welding point, and the interface treatment process and the generation of welding parameters are disconnected from each other, resulting in a mismatch between the treatment effect and the welding process parameters. This leads to poor welding quality stability and a high defect rate under extreme conditions, making it difficult to meet the stringent requirements for the long-term reliability of welded joints in high-standard anti-seepage projects.

[0006] One or more embodiments of this specification employ the following technical solutions: This specification provides one or more embodiments of a method for optimizing and controlling welding parameters for geomembrane edging. The method includes: collecting multi-dimensional micro-environmental parameters of the current welding point through a multi-dimensional environmental sensing module integrated in the welding execution equipment; identifying the current welding environment type based on the multi-dimensional micro-environmental parameters; wherein the current welding environment type includes ordinary welding environment and extreme welding environment, and the extreme welding environment includes low temperature environment, high humidity environment and composite environment; dynamically adjusting the working parameters of the treatment equipment according to the current welding environment type to determine an adaptively optimized interface treatment parameter set; determining welding parameter compensation values ​​through the interface treatment parameter set; wherein the interface treatment parameters include hot air temperature parameters, wind speed parameters and treatment stage switching instructions; and determining welding parameter adjustment instructions based on the welding parameter compensation values, and sending the welding parameter adjustment instructions to the control unit of the welding execution equipment to achieve welding parameter optimization.

[0007] This specification provides one or more embodiments of a geomembrane edge welding parameter optimization and control device, comprising: At least one processor; and, A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, which, when executed by the at least one processor, enables the at least one processor to perform the above-described method.

[0008] This specification provides one or more embodiments of a non-volatile computer storage medium storing computer-executable instructions configured to perform the above-described method.

[0009] The above-described at least one technical solution adopted in the embodiments of this specification can achieve the following beneficial effects: In the environmental perception stage, the welding execution equipment integrates three types of sensors—forward-looking, lateral, and interface—to collaboratively collect multi-dimensional parameters of the microenvironment at the welding point. This effectively avoids interference from heat source radiation and instantaneous environmental disturbances, ensuring that the data accurately reflects the real-time state of the welding interface. It not only distinguishes between ordinary and extreme welding environments but also precisely identifies specific types such as low-temperature, high-humidity, and composite environments, overcoming the coarse-grained limitations of traditional binary normal / abnormal judgments and providing precise decision-making basis for subsequent differentiated strategies. In the interface treatment stage, the open-loop treatment mode with fixed parameters and fixed durations is abandoned. A dual closed-loop dynamic control system is adopted, consisting of inner-loop control commands and outer-loop treatment stage switching commands. This allows the treatment process to both instantaneously suppress overshoot / undershoot fluctuations and adaptively switch treatment strategies based on the phased laws of material heat and moisture transfer, significantly improving the consistency of treatment effects and energy utilization efficiency, and avoiding over-processing or under-treatment of the interface. In the welding parameter generation stage, the material thermal history information contained in the monitoring data of the treatment process is deeply mined, enabling welding parameters to accurately match the actual thermal response characteristics of the geomembrane caused by aging, pollution, or microenvironmental differences. This avoids the problem of parameters being out of sync with the actual state of the material in conventional static compensation. By organically integrating environmental perception, interface management, and welding parameter adjustment into a synergistic optimization, the incidence of defects such as incomplete welds, bubbles, and edge warping under extreme working conditions is significantly reduced. The final welding parameter commands not only respond to the environment and management results but also deeply couple the thermodynamic behavior of the material under the current specific state, achieving precise matching between welding energy input and instantaneous material demand, and significantly improving the scientific nature and adaptability of parameter settings. At the same time, it greatly reduces the reliance on human experience and enhances the adaptability and process robustness of automated welding systems in complex field environments. Attached Figure Description

[0010] To more clearly illustrate the technical solutions in the embodiments or prior art of this specification, the drawings used in the description of the embodiments or prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this specification. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. In the drawings: Figure 1 A flowchart illustrating a method for optimizing and controlling welding parameters for geomembrane edge wrapping, provided in an embodiment of this specification. Figure 2 This is a structural schematic diagram of a geomembrane edge welding parameter optimization and control device provided in the embodiments of this specification. Detailed Implementation

[0011] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this specification, and not all embodiments. Based on the embodiments of this specification, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of this specification.

[0012] This specification provides an embodiment of a method for optimizing and controlling the welding parameters of geomembrane edging. It should be noted that the execution subject in this specification embodiment can be a server or any device with data processing capabilities. Figure 1 This is a flowchart illustrating a method for optimizing and controlling welding parameters for geomembrane edge wrapping, as provided in an embodiment of this specification. Figure 1 As shown, the main steps include the following: Step S101: The multi-dimensional environment sensing module integrated in the welding execution equipment collects multi-dimensional micro-environment parameters of the current welding point, and identifies the current welding environment based on the multi-dimensional micro-environment parameters to determine the current welding environment type.

[0013] The current welding environment type includes ordinary welding environment and extreme welding environment. The extreme welding environment includes low temperature environment, high humidity environment and composite environment. It should be noted that the geomembrane in the embodiments of this specification refers to thermoplastic geomembrane using hot melt welding process (such as hot wedge welding, hot air welding), specifically including: high density polyethylene (HDPE) geomembrane, low density polyethylene (LDPE) geomembrane, linear low density polyethylene (LLDPE) geomembrane, polyvinyl chloride (PVC) geomembrane, chlorinated polyethylene (CPE) geomembrane, etc.

[0014] Before collecting multi-dimensional micro-environmental parameters of the current welding point through a multi-dimensional environmental sensing module integrated into the welding execution equipment, this method also includes: acquiring the three-dimensional structural model data and welding heat source operating parameters corresponding to the welding execution equipment; the three-dimensional structural model data refers to the digital three-dimensional model of the welding execution equipment, including the geometric dimensions, spatial positions, and material properties of each component of the equipment. This data can be acquired by exporting the equipment design model using computer-aided design (CAD) software, or by scanning and reconstructing the actual equipment using a 3D laser scanner to generate accurate three-dimensional point cloud or mesh model data containing components such as the equipment shell, welding heat source, pressure rollers, and supports. The welding heat source operating parameters refer to the thermodynamic parameters of the welding heat source under normal operating conditions, including heat source type (such as hot wedge, hot air), rated power, operating temperature range, and thermal radiation characteristic curve. These parameters can be extracted from the equipment technical manual, read in real time through the heat source controller interface, or obtained by calibrating and fitting the surface temperature distribution of the heat source under standard operating conditions using an infrared thermal imager.

[0015] The three-dimensional structural model data and the operating parameters of the welding heat source are input into a pre-built thermal radiation interference simulation model to calculate the thermal radiation intensity distribution data of the welding heat source on the preset sensing area. The thermal radiation interference simulation model is a pre-built numerical simulation model based on computational fluid dynamics (CFD) or thermal radiation heat transfer theory, used to simulate the heat distribution radiated by the welding heat source into the surrounding space during operation. This model considers the geometry of the heat source, surface temperature, emissivity, and the reflection and absorption characteristics of the surrounding environment. Subsequently, the aforementioned three-dimensional structural model data and welding heat source operating parameters are input into a pre-constructed thermal radiation interference simulation model. The thermal radiation interference simulation model is verified and constructed in a professional simulation platform (such as ANSYS Fluent or COMSOL Multiphysics) based on thermal radiation heat transfer theory and computational fluid dynamics principles. After importing the three-dimensional model of the equipment, the heat source operating parameters are set as boundary conditions, and the preset sensing candidate area around the equipment is defined as the computational domain, which typically covers the area in front of, to the side of, and near the pressure roller of the welding heat source. Numerical simulation calculations are run, and the thermal radiation intensity distribution data of each spatial grid point in the preset sensing area when the welding heat source is working is output, i.e., the radiation power density distribution cloud map received per unit area. This data presents a complete thermal radiation field distribution cloud map in the form of a matrix corresponding to three-dimensional coordinates and radiation intensity values.

[0016] A preset thermal interference suppression threshold is obtained. This threshold refers to the maximum allowable thermal radiation intensity value for which the sensor can operate normally. This threshold is preset based on the heat resistance specifications of the selected sensor (such as the upper limit of operating temperature and thermal radiation sensitivity). Using this thermal radiation intensity distribution data and the preset thermal interference suppression threshold, a set of sensor unit installation locations that meet the thermal interference suppression conditions is determined. The simulated thermal radiation intensity distribution data is compared point-by-point with the preset thermal interference suppression threshold, and all spatial locations with thermal radiation intensities below the threshold are selected, forming a candidate location region that meets the thermal interference suppression conditions. Further considering the constraints of the equipment's mechanical structure (such as avoiding moving parts and ensuring installation rigidity), the principle of minimizing airflow interference (selecting stable airflow areas), and the measurement representativeness requirements (covering the area in front of, to the side of, and at the interface of the welding point), three specific installation points that do not interfere with each other and meet the multi-dimensional sensing requirements are selected from the candidate location region. These correspond to the installation locations of the forward-looking sensor unit, the lateral sensor unit, and the interface sensor unit, respectively, forming a set of sensor unit installation locations.

[0017] The forward-looking sensor unit, lateral sensor unit, and interface sensor unit are respectively installed at corresponding positions in the sensor unit installation location set to deploy the multi-dimensional environmental perception module. Specifically, the forward-looking sensor unit is located in front of the welding heat source in its direction of travel; the lateral sensor units are located on both sides of the welding heat source; and the interface sensor unit is located before the contact area of ​​the welding roller. It should be noted that the forward-looking sensor unit, installed at a selected position in front of the welding heat source in its direction of travel, is used to monitor environmental parameters in front of the welding point that are not affected by heat. The lateral sensor units are symmetrically installed at selected positions on both sides of the welding heat source to monitor the lateral environmental gradient of the welding area. The interface sensor unit is installed at a selected position before the contact area of ​​the welding roller (i.e., before the contact area between the roller and the geomembrane) to directly monitor the state of the welding interface.

[0018] In one embodiment of this specification, the look-ahead sensing unit is fixed on the front support of the welding execution equipment, located in front of the welding heat source (such as a hot wedge or hot air nozzle), at a distance of 1.2–1.8 times the width of the heat source along the welding direction. For example, if the heat source is 50mm wide, it is positioned 60–90mm in front. The installation method uses a rigid cantilever bracket and a heat-insulating ceramic gasket. The sensor body is embedded in a shield with airflow guide channels, not in contact with the geomembrane surface, at a height of approximately 20–30mm from the membrane surface. It should be noted that this position is in the un-welded area, without pressure rollers, heat sources, or tension interference; it is only a pre-scanning area. The look-ahead sensing unit is used to predict the macroscopic environmental conditions (temperature, humidity) of the area about to enter the welding zone. Due to the environmental lag effect after the geomembrane is laid, the current welding point is more affected by the undisturbed environment in front. If only the area near the heat source is measured, the data will be distorted due to thermal radiation interference. Look-ahead measurement can predict sudden environmental changes (such as gusts of wind or localized condensation) in advance. Lateral sensing units are used to capture the lateral environmental gradient of the welding area (e.g., direct sunlight on one side, dampness on the other). Geomembranes are often laid on open slopes or ditches, where environmental parameters vary significantly laterally. Single-point measurements cannot reflect the uniformity of the weld width, easily leading to incomplete welds on one side and scorching on the other. Interface sensing units are used to directly obtain the true state of the welding interface (membrane surface temperature, surface humidity). Ambient temperature and humidity do not equal membrane surface state. At low temperatures, the membrane surface is prone to frost formation, and in high humidity environments, the membrane surface adsorbs a layer of water molecules, which directly affects heat conduction and molecular diffusion. Only by directly measuring the interface can an accurate determination be made as to whether remediation is needed.

[0019] The lateral sensing units are symmetrically installed on the side beams of the equipment on both sides of the welding heat source, horizontally at a distance of 0.8–1.2 times the width of the heat source (e.g., 40–60 mm) from the center of the heat source, and at the same height as the working surface of the heat source. The sensors are embedded in a lateral protective cover with a slit-type sampling port, allowing only lateral ambient airflow to enter, avoiding welding spatter and direct heat radiation paths. Located outside the welding edge area (the geomembrane edge is typically 50–100 mm wide, with the sensing unit placed on the outer edge), it does not interfere with the movement trajectory of the pressure roller. The lateral sensing units are used to capture the lateral environmental gradient of the welding area, such as one side being directly exposed to sunlight and the other side being damp. Geomembranes are often laid on open slopes or ditches, where environmental parameters vary significantly laterally. Single-point measurements cannot reflect the uniformity of the weld width, easily leading to incomplete welds on one side and charring on the other.

[0020] The interface sensor unit is embedded in the non-working area at the front end of the welding roller, specifically 5–10 mm ahead of the contact surface between the roller and the geomembrane. A micro-aperture exposes the sensing window, positioned 5–15 mm vertically from the membrane surface. A high-temperature resistant, micro-distance infrared / humidity composite probe is integrated into the sealed cavity at the roller's shaft end, covered by a quartz glass protective window. It moves synchronously with the roller but does not bear pressure. Located before the roller's contact area, it only monitors the interface state to be welded, without altering the roller's pressure distribution or heat conduction path. The interface sensor unit directly acquires the true state of the welding interface (membrane surface temperature, surface humidity). Ambient temperature and humidity do not equate to the membrane surface state; at low temperatures, the membrane surface is prone to frost formation, and in high humidity environments, the membrane surface adsorbs a layer of water molecules, directly affecting heat conduction and molecular diffusion. Only by directly measuring the interface can an accurate determination be made regarding the need for remediation.

[0021] All sensing units are located outside the welding heat-force zone. Through structural isolation and spatial avoidance, the movement freedom and process stability of the welding actuators (heat source, pressure roller, film feeding mechanism) are ensured to be unimpeded. It should be noted that all sensor housings adopt a low-profile streamlined design (height < 20mm), positioned below the lowest working position of the pressure roller; the mounting brackets use high-strength, lightweight alloys with rigidity sufficient to meet vibration suppression requirements, avoiding resonance interference with welding accuracy; the sensing windows are coated with hydrophobic and oleophobic materials to prevent molten polymer adhesion. Sensing signals are transmitted via optical fiber or shielded twisted-pair cable, physically isolated from the high-current welding loop to avoid electromagnetic interference; the sampling frequency (10Hz) is much lower than that of the welding control loop (100Hz+), not consuming main control real-time resources. A modular, quick-release structure is adopted, allowing for rapid replacement in case of damage without affecting the operation of the main equipment.

[0022] Welding heat sources (250–320℃) generate strong infrared radiation. If sensors are placed close to the heat source, temperature and humidity readings will be significantly inaccurate. By employing front-mounted, side-mounted, and shielded designs, distance attenuation and physical obstruction are utilized to ensure that measurements reflect the actual environment rather than process interference. Geomembrane welding speeds are typically 1–3 m / min. Forward-looking sensing units acquire data 2–5 seconds before the heat source arrives, providing sufficient response time for remediation systems (such as hot air drying) to achieve a closed loop of prediction, intervention, and verification. The welding quality of HDPE geomembranes depends on the degree of interdiffusion of interfacial molecular chains, and the diffusion rate is determined by interfacial temperature and moisture content. Interfacial sensing units directly monitor this critical state variable, which is more reliable than indirect estimation. In non-planar scenarios such as slopes and ditches, lateral environmental differences can reach over 10℃ or 30%RH. Bilateral sensing can trigger asymmetric remediation strategies, such as intensifying drying only on the wet side, avoiding overtreatment that leads to energy waste or membrane damage.

[0023] Compared to conventional deployment methods that rely on engineers' experience to visually select sensor installation locations or simply avoid visible areas of heat sources, this approach uses thermal radiation interference simulation to quantitatively analyze the thermal field distribution and scientifically select a set of locations that meet thermal interference suppression standards. This fundamentally eliminates the interference sources of welding heat radiation on sensor measurements, significantly improving the authenticity and stability of environmental parameter acquisition. This deployment method performs multi-objective collaborative optimization of mechanical structure constraints, airflow dynamics characteristics, and measurement representativeness requirements. It enables three types of sensing units—look-ahead, lateral, and interface—to accurately capture the predicted environment in front of the welding point, the lateral environmental gradient, and the direct state of the interface, respectively. This constructs a three-dimensional perception network covering the time dimension (look-ahead prediction), the spatial dimension (lateral gradient), and the process dimension (interface state), providing irreplaceable data support for the refined identification of extreme environmental types. At the same time, it transforms sensor installation from being driven by subjective experience to being driven by simulation data, achieving standardization and repeatability of installation locations. This avoids measurement data fluctuations caused by installation deviations and enhances the robustness and environmental adaptability of the entire welding parameter optimization control system, especially under extreme conditions such as low temperature and high humidity, ensuring the reliability of the environmental perception link.

[0024] In one embodiment of this specification, after the geomembrane edge welding operation is started, the raw sensor data stream output by the multi-dimensional environmental perception module deployed on the welding execution equipment is received in real time. This includes environmental parameters of the undisturbed area in front of the welding heat source, symmetrically collected lateral environmental parameters on both sides of the heat source, and direct state parameters of the welding interface in front of the pressure roller contact area, thereby determining the multi-dimensional micro-environment parameters.

[0025] The current welding environment is identified and its type is determined based on multi-dimensional micro-environment parameters. This is achieved through the following methods: A sliding window time-series filtering process is applied to the multi-dimensional microenvironment parameters to generate filtered microenvironment parameter time-series data. The window length is dynamically set according to the equipment's travel speed. For the same physical quantity within the window, such as the weighted and fused sequence of temperature values ​​at various locations, a moving average is calculated. The window slides continuously along the time axis, outputting a smooth and continuous parameter sequence, generating filtered microenvironment parameter time-series data, effectively eliminating high-frequency noise from sensors and instantaneous environmental disturbances. The comprehensive temperature data sequence extracted from the filtered data (a weighted sequence fusing temperature information from look-ahead, lateral, and interface locations) is continuously compared with a preset low-temperature threshold (set based on geomembrane material welding process specifications and historical engineering verification data) pre-stored in the system parameter library through a continuous window comparison to generate a temperature environment judgment result. A judgment window is used to slide and scan the temperature sequence, statistically analyzing the percentage of data points with temperatures below the threshold within the window. If this percentage consistently meets the preset judgment condition and holds true for multiple consecutive windows, the temperature environment judgment result indicates that the low-temperature condition is met; otherwise, it indicates that the condition is not met. Similarly, the comprehensive humidity data sequence (a weighted sequence integrating temperature information from three locations: look-ahead, lateral, and interface) is compared with a preset high humidity threshold (set based on geomembrane material welding process specifications and historical engineering verification data) using the same logic in a continuous window comparison. The percentage of data points with humidity values ​​higher than the preset humidity threshold within the window is counted. If this percentage consistently meets the preset judgment condition and is true for multiple consecutive windows, the temperature environment judgment result is generated as meeting the high humidity condition; otherwise, it is not met. Based on the logical combination of the temperature environment judgment result and the humidity environment judgment result, a current welding environment type identifier is generated. The temperature and humidity judgment results are combined using Boolean logic. When the temperature judgment is met and the humidity judgment is not met, the current welding environment type is generated as a low temperature environment; when the temperature judgment is not met and the humidity judgment is met, the current welding environment type is generated as a high humidity environment; when both are met, it is identified as a composite environment; when neither is met, it is identified as a normal welding environment.

[0026] By employing a dual processing approach of sliding window temporal filtering and continuous window comparison, random environmental fluctuations and transient interferences (such as brief parameter jumps caused by gusts or local shading) are effectively filtered out, significantly improving the stability and anti-misjudgment capability of environmental assessment. Simultaneously, a logical combination based on temperature and humidity assessment results enables refined classification of extreme welding environments, clearly distinguishing between low-temperature, high-humidity, and composite environments. This overcomes the coarse-grained limitations of traditional binary judgments of normal and abnormal conditions, providing precise and specific environmental type guidance for subsequent interface management, allowing management strategies to be tailored to actual environmental challenges. Furthermore, deep integration of multi-source spatiotemporal data collected by the multi-dimensional environmental perception module comprehensively reflects the overall situation of the welding point's microenvironment through data fusion and trend analysis, avoiding the one-sidedness and randomness of single-point measurements. Especially in complex scenarios prone to environmental gradients, such as open-air operations and slope ditches, it can accurately capture the persistence and spatial distribution characteristics of environmental anomalies, ensuring that the welding parameter optimization control system can make scientific and reliable environmental response decisions under various working conditions, fundamentally improving the environmental adaptability and quality robustness of the geomembrane edge welding process.

[0027] Step S102: Based on the current welding environment type, dynamically adjust the working parameters of the treatment equipment to determine an adaptively optimized interface treatment parameter set, so as to determine the welding parameter compensation value through the interface treatment parameter set.

[0028] The interface parameters include hot air temperature parameters, wind speed parameters, and treatment stage switching instructions. Based on the current welding environment type, the operating parameters of the treatment equipment are dynamically adjusted to determine an adaptive and optimized set of interface treatment parameters, which is achieved through the following methods: Based on the current welding environment type and pre-acquired geomembrane material property data, the system matches the corresponding current basic remediation mode from a pre-defined remediation strategy library to generate initial remediation parameters. According to the geomembrane material batch identifier, the system queries a material property parameter library for matching geomembrane material property data, including material type (e.g., HDPE), thickness, surface treatment status, heat capacity coefficient, and moisture conductivity coefficient. The pre-defined remediation strategy library is a database stored in the control system's non-volatile memory. It records validated basic remediation modes for different environment types and material combinations, such as low-temperature remediation mode, high-humidity remediation mode, and composite remediation mode, along with their corresponding initial parameter ranges. Using the type identifier corresponding to the current welding environment type and the material type and thickness from the geomembrane material properties as the joint query key, the system retrieves the current basic remediation mode from the remediation strategy library and calculates specific initial remediation parameters, including initial hot air temperature, initial wind speed, and expected remediation time, based on built-in rules, such as for a specific thickness of HDPE membrane in a low-temperature environment.

[0029] Subsequently, the control and treatment execution equipment performs interface treatment operations according to the initial treatment parameters. That is, the central processing unit converts the initial treatment parameters into control signals, driving the hot air generator and fan to start working and purify the geomembrane welding interface. At the same time, it collects real-time time-series data of the interface status and the treatment equipment operating parameters. The interface status time-series data is continuously measured and reported by the interface sensing unit (such as a micro-infrared temperature sensor and a humidity sensor) at a fixed sampling frequency, forming the interface temperature time series and the interface humidity time series. The treatment equipment operating parameter time-series data is fed back by the sensors built into the treatment equipment, forming the time series of the actual hot air temperature, the actual fan speed, etc.

[0030] Next, the dynamic control phase begins. Based on the time-series data of the interface state during the governance process, the time-series data of the interface state change rate is determined. Inner-loop control instructions are then generated using this time-series data, specifically through the following methods: First-order difference operations are performed on the interface temperature time-series data from the interface state time-series data of this treatment process to generate interface temperature change rate time-series data. This involves calculating the difference between the temperature value at each sampling moment and the temperature value at the previous moment, then dividing by the sampling time interval to obtain a series of numerical sequences reflecting the instantaneous rate of change of interface temperature. Similarly, first-order difference operations are performed on the interface humidity time-series data from the interface state time-series data of this treatment process to generate interface humidity change rate time-series data; the calculation method is the same, yielding a numerical sequence reflecting the instantaneous rate of change of interface humidity.

[0031] The time-series data of the interface temperature change rate is compared in real time with a preset temperature change rate threshold range to generate an inner-loop control command. The preset temperature change rate threshold range is a reasonable temperature change range pre-set based on the material's thermal tolerance and ideal heating curve. If the temperature exceeds the upper limit of the temperature change rate threshold range, a hot air temperature attenuation command is generated. This command includes a temperature reduction value calculated based on the overshoot to prevent overheating damage to the membrane surface caused by excessively rapid heating. In one example, attenuation = k1 × overshoot. If the temperature is below the lower limit of the temperature change rate threshold range, a hot air temperature enhancement command is generated. This command includes a temperature increase value calculated based on the undershoot to address situations where heating is too slow and efficiency is low. In one example, enhancement = k2 × undershoot. It should be noted that k1 and k2 are related to the heating capacity of the treatment equipment and can be obtained through experimental data and / or historical treatment process data analysis.

[0032] The time-series data of the interface humidity change rate is compared in real time with a preset humidity change rate threshold range. The preset humidity change rate threshold range is a set of dual-boundary thresholds pre-stored in the system parameter library to define the reasonable range of dynamic changes in interface humidity, including an upper threshold and a lower threshold. This range is not a fixed constant, but is generated by comprehensively calibrating based on the moisture conductivity parameters of the geomembrane material (such as the moisture conductivity coefficient range corresponding to the material type and nominal thickness), the statistical distribution characteristics of the interface humidity change rate in historical successful treatment cases (by analyzing the time-series data of humidity change rate of compliant samples in the treatment process monitoring database and extracting the fluctuation range of its stable convergence stage), and the welding process specification requirements. If the humidity change rate exceeds the upper threshold of the threshold range, a wind speed attenuation command is generated to prevent localized overcooling or membrane disturbance that may result from excessively rapid dehumidification. The wind speed attenuation command includes a humidity reduction value calculated based on the overshoot. In one example, attenuation = k3 × overshoot. If the humidity is below the lower threshold of the threshold range, a wind speed enhancement command is generated to accelerate the dehumidification process. The wind speed enhancement command includes a humidity increase value calculated based on the undershoot. In one example, enhancement = k4 × undershoot. It should be noted that k3 and k4 here are related to the dehumidification parameters of the treatment equipment and can be obtained through experimental data and / or historical treatment process data analysis. The hot air temperature attenuation command or the hot air temperature enhancement command, the wind speed attenuation command or the wind speed enhancement command are combined into an inner loop control command.

[0033] The inner loop uses the interface state change rate (first derivative) as feedback to construct a local closed loop with millisecond-level fast response. Its control principle originates from the thermodynamic transient response theory, where the interface temperature / humidity change rate directly characterizes the instantaneous matching degree between the current treatment intensity and the material's heat and moisture transfer capacity. By constraining the change rate within a preset threshold range, the hot air temperature or wind speed is dynamically fine-tuned to make the interface state approach the target along a smooth trajectory. Essentially, this suppresses high-frequency disturbances during the treatment process (such as environmental gusts and equipment fluctuations), avoiding interface thermal shock caused by overshoot or insufficient treatment caused by undershoot. Its advantages lie in its rapid response, strong anti-interference ability, and ensuring the instantaneous stability of the treatment process and the smoothness of the interface state transition.

[0034] In parallel, the energy efficiency ratio time-series data of the treatment equipment's operating parameters is determined using the energy efficiency ratio time-series data. Based on this energy efficiency ratio time-series data, a switching instruction for the outer ring treatment phase is generated, specifically through the following method: Based on the time-series data of the treatment equipment's operating parameters, a time-integration calculation is performed to generate cumulative treatment energy consumption data. This involves integrating the curves of hot air power and / or fan power over time to obtain the total energy consumed from the start of treatment to the current moment; this is the cumulative treatment energy consumption data. Based on this cumulative treatment energy consumption data, pre-acquired initial interface state parameters, and current interface state parameters, the unit energy consumption state improvement is calculated. The initial interface state parameters are the interface temperature and humidity values ​​recorded at the start of treatment, and the current interface state parameters are the latest temperature and humidity values ​​acquired in real time. The calculation method is (initial humidity - current humidity) / cumulative energy consumption, or (current temperature - initial temperature) / cumulative energy consumption. This ratio is the unit energy consumption state improvement. The unit energy consumption state improvement is used to determine the treatment energy efficiency ratio time-series data, representing the degree of interface state improvement achieved for each unit of energy consumed; a higher value indicates higher current treatment efficiency.

[0035] A sliding window slope calculation is performed on the time-series data of the governance energy efficiency ratio to generate time-series data of energy efficiency ratio change slope. That is, within a sliding time window, the time-series data of the governance energy efficiency ratio is linearly fitted, and its slope reflects the changing trend of governance efficiency—increasing, remaining flat, or decreasing. If the slope values ​​of multiple consecutive windows in the time-series data of energy efficiency ratio change slope are all less than a preset decay slope threshold, then a corresponding governance stage switching instruction is generated based on the current basic governance mode, and this governance stage switching instruction is determined as the outer loop control instruction. It should be noted that when the slope values ​​of multiple consecutive windows in the time-series data of energy efficiency ratio change slope are all less than the preset decay slope threshold, it indicates that the rate of increase in the governance energy efficiency ratio (the amount of interface state improvement brought by unit energy consumption) has significantly decayed and tended to stabilize. That is, the marginal benefit of the current governance stage has entered a plateau period. Continuing to maintain the current governance intensity will lead to a serious imbalance between energy input and interface improvement effect, with the risk of ineffective energy consumption accumulation and interface overtreatment, such as thermal damage to the membrane surface or deterioration of material properties. This phenomenon reflects the phased characteristics of the heat and moisture transfer process at the material interface. In the initial stage, the energy efficiency ratio rises rapidly (the period of high-efficiency treatment). Subsequently, due to the interface state approaching the target value or the influence of material thermal inertia, the improvement rate naturally slows down. The determination of the preset attenuation slope threshold is based on two criteria: first, inflection point clustering analysis is performed on the time series curves of energy efficiency ratios from a large number of successful cases in the historical treatment database to extract the lower limit of the statistical distribution of the slope when the energy efficiency ratio changes from rapid increase to flattening; second, dynamic correction is performed in combination with the current geomembrane material property data (such as heat capacity coefficient and moisture conductivity coefficient) to ensure that the threshold matches the actual thermal response characteristics of the material, thereby reliably capturing the inflection point of treatment efficiency.

[0036] When the current basic treatment mode is low-temperature treatment mode, a command is generated to switch from the rapid heating stage to the constant-temperature infiltration stage. The command in the constant-temperature infiltration stage lowers the hot air temperature setpoint to a preset mild level, emphasizing temperature uniformity and heat penetration. When the current basic treatment mode is high-humidity treatment mode, a command is generated to switch from the strong-wind drying stage to the mild dehumidification stage. The command in the mild dehumidification stage reduces the wind speed and may slightly increase the air temperature for deep dehumidification. When the current basic treatment mode is a composite treatment mode, a command is generated to switch from the current treatment sub-stage to the next treatment sub-stage. For example, switching from the strong-wind dehumidification sub-stage to the low-temperature preheating sub-stage.

[0037] The outer loop uses the slope of the change in the energy efficiency ratio as the decision-making basis to construct a global closed loop for second-level strategy optimization. Its design principle is based on energy optimization theory and the thermal history characteristics of materials. The decay of the energy efficiency ratio slope indicates that the physical benefits of the current treatment stage have reached a bottleneck, requiring a switch to a more suitable treatment sub-stage based on the phased laws of heat and moisture transfer in materials (e.g., in low-temperature treatment, rapid heating followed by constant-temperature infiltration is needed to promote heat conduction into the material's interior). Its advantage lies in macro-level strategy optimization from the dual dimensions of energy utilization efficiency and material process adaptability, avoiding energy waste and interface damage caused by a one-size-fits-all approach to intensive treatment, and achieving deep coupling between the treatment path and the material's physical processes.

[0038] The inner-loop control command is merged with the outer-loop treatment stage switching command to generate updated treatment parameters. If an outer-loop control command exists, the inner-loop control command is suspended, and the stage switching operation in the outer-loop control command is executed first. After the stage switching is completed, the inner-loop control command is regenerated based on the preset parameter benchmark of the new stage. If no outer-loop control command exists, the hot air temperature adjustment and wind speed adjustment in the inner-loop control command are superimposed on the current treatment equipment working parameter benchmark value to generate a treatment equipment working parameter adjustment command.

[0039] The process iterates until the preset treatment convergence conditions are met, thus determining the treatment parameter set for the interface. This process is repeated until the preset treatment convergence conditions are satisfied. These conditions are typically set as follows: the interface temperature reaches the target range and its rate of change approaches zero; the interface humidity is below the target value and its rate of change approaches zero, and these conditions remain stable for a period of time. When these conditions are simultaneously met, the iteration stops, and the treatment parameter set for the interface is determined. This parameter set includes the final effective hot air temperature and wind speed parameters at the time of treatment compliance, as well as the sequence of treatment stage switching instructions experienced. This complete record of the treatment process is output to the subsequent welding parameter compensation stage.

[0040] The inner-loop control command enables rapid response and fine adjustment to instantaneous changes in the interface state, effectively suppressing over- or under-adjustment of interface temperature / humidity caused by sudden changes in heat input or wind speed during the treatment process, thus avoiding thermal damage to the material surface or insufficient treatment. The outer-loop treatment stage switching command intelligently identifies the inflection point of treatment efficiency based on the dynamic evolution characteristics of the treatment energy efficiency ratio, significantly improving energy utilization efficiency and conforming to the physical laws of heat and moisture transfer of geomembrane materials at different treatment stages. The integrated control mechanism of inner and outer-loop commands deeply couples the dynamic characteristics of the process with energy efficiency feedback, overcoming the problems of parameter rigidity and large fluctuations in treatment effect caused by ignoring the dynamic response of the treatment process in traditional methods. Especially in extreme environments such as low temperature and high humidity, it can adaptively adjust the treatment intensity and stage according to the actual thermal history of the material and the interface response characteristics, providing a highly consistent and reliable interface state basis for the dynamic correction of subsequent welding parameters, fundamentally improving the intrinsic quality uniformity and long-term seepage prevention reliability of the geomembrane edge welding joint, while reducing ineffective energy consumption and the risk of engineering rework.

[0041] The welding parameter compensation values ​​are determined by using this interface to manage the parameter set, and this is achieved in the following way: The first step involves extracting a thermal history feature vector based on the interface governance parameter set and pre-acquired governance process monitoring data. This governance process monitoring data is a complete time-series dataset collected and stored in real-time by the interface sensing unit during the aforementioned interface governance iteration process. It includes time-series data of interface temperature, interface humidity, and the output power of governance equipment (such as a hot air blower) throughout the entire process from governance initiation to achieving the target. The specific implementation process for this step is as follows: Complete thermal history data from the start-up time to the time of achieving the treatment standard is obtained from the monitoring data of the treatment process. Specifically, a complete thermal history window is extracted from the monitoring data from the start-up time (timestamp of the start signal fed back by the treatment execution equipment) to the time of achieving the treatment standard (timestamp corresponding to the standard achievement signal output by the interface treatment convergence judgment module). This window data consists of continuously collected time-series data of interface temperature, interface humidity, and treatment equipment operating parameters. Based on this complete thermal history window data, the cumulative heat input data, interface temperature response slope data, thermal response stability data, and humidity removal efficiency data are calculated. Among them, the cumulative heat input data is obtained by numerically integrating the time-series data of the output power of the treatment equipment within the thermal history window, representing the total energy applied in the treatment process. For example, cumulative heat input = ∫(output power of treatment equipment × time) dt (numerical integration); the interface temperature response slope data is obtained by calculating (interface temperature at the time of treatment compliance - initial interface temperature at the time of treatment compliance) / time taken to meet the treatment compliance, reflecting the average rate of overall temperature rise; the thermal response stability data is obtained by calculating the standard deviation of the interface temperature time-series data in the last preset proportion of the thermal history window, characterizing the degree of temperature fluctuation at the end of the treatment period. For example, thermal response stability = standard deviation of the interface temperature time-series data in the last 10% period before compliance; the humidity removal efficiency data is obtained by calculating (initial interface humidity at the time of treatment compliance - interface humidity at the time of treatment compliance) / time taken to meet the treatment compliance, reflecting the average rate of overall dehumidification.

[0042] The cumulative heat input data, interface temperature response slope data, thermal response stability data, and humidity removal efficiency data are each compared with their corresponding normalized baseline parameters to generate a set of normalized feature parameters. These normalized baseline parameters are standard values ​​pre-calculated based on a large amount of historical treatment data, used to eliminate dimensions and scale the values ​​of different characteristics to a comparable range. They include typical heat input baseline values, typical response slope baseline values, typical stability baseline values, and typical removal efficiency baseline values. This set of normalized feature parameters is then combined in a preset order to generate a thermal history feature vector for the treatment process. This vector is a structured numerical sequence that quantitatively characterizes the dynamic thermodynamic features of this treatment process.

[0043] Multidimensional dynamic features such as cumulative heat input, interface temperature response slope, thermal response stability, and humidity removal efficiency are quantitatively extracted from the complete thermal history window and normalized to generate structured feature vectors. This comprehensively characterizes the thermal history behavior and individual response differences of materials during the treatment process (such as changes in thermal inertia caused by aging and pollution). This provides a high-information, high-discrimination input basis for the thermal inertia mapping model, enabling subsequent parameter corrections to accurately reflect the actual state of the material and significantly enhancing the pertinence and environmental adaptability of the welding parameter optimization scheme.

[0044] The second step involves inputting the thermal history feature vector of the treatment process and the nominal thermal property parameter set of the geomembrane material into a pre-trained thermal inertia mapping model to generate the interface thermal inertia compensation coefficient. The nominal thermal property parameter set of the geomembrane material is extracted from a material database bound to the current welded membrane roll (accessed by scanning RFID tags to obtain material batch identifiers), including parameters such as the standard thermal conductivity and specific heat capacity of the material type. The pre-trained thermal inertia mapping model is a regression model trained based on neural networks or other machine learning algorithms, utilizing the correspondence between the treatment thermal history features and the actual welding thermal response deviation in historical welding data. In one example, the thermal inertia mapping model structure is a lightweight neural network (3 fully connected layers). The number of nodes in the input layer equals the thermal history feature dimension plus the nominal parameter dimension. The output layer is a single node. The model's function is to output the interface thermal inertia compensation coefficient (range 0.0–2.0). A coefficient > 1.0 indicates that the actual thermal response is slower than the nominal value, suggesting excessive thermal inertia and requiring increased thermal input. A coefficient < 1.0 indicates that the actual thermal response is faster than the nominal value, suggesting insufficient thermal inertia and requiring reduced thermal input.

[0045] The third step involves calling the corresponding dedicated compensation model based on the current welding environment type, and inputting the interface state parameters at the time of achieving the treatment standard into the dedicated compensation model to calculate and generate a set of basic compensation values ​​for welding parameters. The current welding environment type is determined by the previous identification steps, such as low temperature, high humidity, or a combination thereof. The interface state parameters at the time of achieving the treatment standard are the final temperature and humidity values ​​confirmed by the interface sensing unit after the treatment is completed. By inputting the interface state parameters at the time of achieving the treatment standard into the corresponding dedicated compensation model, the set of basic compensation values ​​for welding parameters, which does not consider the influence of thermal inertia, can be calculated, including basic compensation values ​​for welding temperature, welding pressure, and welding speed.

[0046] The dedicated compensation model adopts a nonlinear mapping structure based on physical principles. Three types of dedicated compensation models are constructed for three welding environment types: low-temperature environment, high-humidity environment, and combined environment. Each model is a piecewise nonlinear function based on the physical characteristics of heat conduction and interface diffusion. Its structure consists of the following technical features: a model type identifier corresponds one-to-one with the welding environment type (low-temperature environment, high-humidity environment, combined environment), serving as the entry condition for model invocation; the input layer receives interface state parameters (interface temperature data, interface humidity data) at the time of achieving the treatment standard. These parameters are obtained in real time through the interface sensing unit and preprocessed. The feature extraction layer performs physical feature transformation on the input interface state parameters, including calculating the interface heat conduction distance, interface water molecule diffusion rate, and interface thermal stress. These features are derived from the thermodynamic equations and interface diffusion models of the geomembrane material. The compensation coefficient calculation layer calculates the weight coefficients of each feature through a preset physical parameter association table (stored in the system parameter library). The weight coefficients are dynamically generated based on the nominal thermophysical parameters (heat capacity coefficient, thermal conductivity) of the geomembrane material and welding process specifications. The output layer generates basic compensation values ​​for welding temperature, welding pressure, and welding speed. The basic compensation value for welding temperature is a parameter used to adjust the welding heat source temperature, and its value is calculated based on interface temperature data and heat conduction characteristics. The basic compensation value for welding pressure is a parameter used to adjust the welding roller pressure, and its value is calculated based on interface humidity data and interface diffusion characteristics. The basic compensation value for welding speed is a parameter used to adjust the welding travel speed, and its value is calculated based on a combination of interface thermal stress and interface diffusion characteristics. This dedicated compensation model maps environmental types and interface state parameters to specific thermodynamic physical characteristics. Compared with traditional compensation methods based on empirical lookup tables, it avoids the ambiguity of the environment and parameter mapping, and achieves a precise correspondence between compensation parameters and the actual thermodynamic state of the welding interface. This effectively solves the problem of welding parameter mismatch caused by differences in the thermal response characteristics of materials under extreme environments.

[0047] The fourth step involves nonlinearly correcting the basic compensation values ​​for welding temperature and welding speed in the basic compensation values ​​for welding parameters based on the interface thermal inertia compensation coefficient, thereby generating thermally inertia-corrected welding parameter compensation values. The specific implementation process in this step is as follows: Obtain the temperature correction weighting coefficient and velocity correction weighting coefficient that match the current geomembrane material type. These weighting coefficients are pre-set in the material library based on the material's sensitivity to temperature and velocity. Based on the interface thermal inertia compensation coefficient, the temperature correction weighting coefficient, and the basic welding temperature compensation value, calculate the corrected welding temperature compensation value using a preset temperature correction calculation formula. The temperature correction calculation formula is: Corrected temperature compensation value = Basic temperature compensation value × [1 + Temperature weighting coefficient × (Thermal inertia compensation coefficient - 1)]. The principle is that when the thermal inertia is large (coefficient > 1), the temperature compensation is appropriately increased to overcome the heating inertia.

[0048] Based on the interface thermal inertia compensation coefficient, the speed correction weighting coefficient, and the basic welding speed compensation value, a corrected welding speed compensation value is calculated using a preset speed correction formula. The speed correction formula is: Corrected speed compensation value = Basic speed compensation value × [1 - Speed ​​weighting coefficient × (Thermal inertia compensation coefficient - 1)]. The principle is that when thermal inertia is high, the welding speed is appropriately reduced to prolong the heat treatment time. The weighting coefficients for temperature and speed are designed to be complementary to ensure reasonable adjustment of the total heat input energy. The basic welding pressure compensation value is generally considered insensitive to thermal inertia and therefore remains unchanged. Finally, the corrected welding temperature compensation value, the corrected welding speed compensation value, and the basic welding pressure compensation value are combined to generate the welding parameter compensation value.

[0049] Based on the interface thermal inertia compensation coefficient and the correction weight coefficient for material matching, correction values ​​for welding temperature and speed are generated through a preset nonlinear correction logic. When the thermal inertia is large, the heat input is enhanced and the action time is extended; when the thermal inertia is small, the adjustment is reversed, achieving precise coupling between the welding heat input strategy and the actual thermal response characteristics of the material. This effectively avoids the risks of surface melting and internal incomplete melting in low-temperature environments, as well as the problem of film overheating and degradation in high-humidity environments. It significantly improves the matching accuracy between welding parameters and material thermal history, providing core technical support for the stable control of welding quality under extreme conditions.

[0050] By deeply mining the dynamic information of material thermal response throughout the entire treatment process using the thermal history feature vector, the interface treatment process is transformed from an isolated procedure into a key knowledge source for welding parameter decision-making. The introduction of the thermal inertia mapping model enables implicit perception and quantitative characterization of the actual thermodynamic state of the geomembrane material on site (such as differences in thermal response caused by aging, pollution, or microstructural changes), effectively overcoming the problem of the disconnect between nominal material parameters and actual working conditions. Based on the nonlinear correction of the interface thermal inertia compensation coefficient, the welding parameters can adaptively match the current real thermal inertia state of the material, accurately avoiding the risk of surface melting and internal incomplete welding in low-temperature environments, and effectively preventing membrane overheating degradation caused by abnormal interface thermal response in high-humidity environments. The treatment effect is transformed into a precise feedforward basis for the welding process, significantly improving the coupling accuracy and process robustness of welding heat input strategy and material thermal history in extreme environments, greatly reducing the welding defect rate caused by parameter mismatch, and providing personalized and adaptive parameter optimization capabilities for geomembranes of different batches and service conditions, fundamentally enhancing the quality consistency and long-term reliability of geomembrane edge welding joints.

[0051] Step S103: Based on the welding parameter compensation value, determine the welding parameter adjustment command, and send the welding parameter adjustment command to the control unit of the welding execution equipment to achieve welding parameter optimization.

[0052] In one embodiment of this specification, welding parameter adjustment instructions are determined based on welding parameter compensation values. Here, the welding parameter compensation values ​​are a data set, specifically including the corrected welding temperature compensation value (ΔT_final), the basic welding pressure compensation value (ΔP), and the corrected welding speed compensation value (ΔV_final). Subsequently, a welding parameter baseline value set matching the current welding task is retrieved from the system parameter library. This baseline value set is based on the geomembrane material type and thickness, and the initial welding temperature (T_base), welding pressure (P_base), and welding speed (V_base) are set under standard conditions. The compensation values ​​are algebraically superimposed with the baseline values, and the synthesized welding temperature set value (T_set) is equal to the sum of T_base and ΔT_final; the synthesized welding pressure set value (P_set) is equal to the sum of P_base and ΔP; and the synthesized welding speed set value (V_set) is equal to the sum of V_base and ΔV_final. After the synthesis calculation is completed, the calculated T_set, P_set, and V_set are compared one by one with the allowable ranges of welding parameters (including upper and lower limits of temperature, pressure, and speed) stored in the safety database. If any synthesized value exceeds its allowable range, the parameter is automatically truncated to the nearest boundary value, and a parameter limit warning record is generated at the same time.

[0053] After verification, the final T_set, P_set, V_set, and related control mode codes (such as constant temperature control and constant speed movement) are packaged and encapsulated into a standardized digital communication message that the equipment control unit can parse, which is the welding parameter adjustment command. This welding parameter adjustment command is then sent to the control unit of the welding execution equipment. Through a preset industrial communication bus (such as CAN bus, EtherCAT, or real-time Ethernet) and a specified communication protocol, the encapsulated command message is sent, either broadcast or point-to-point, to the heat source control unit responsible for heat source management and the motion control unit responsible for mechanical movement within the welding execution equipment. Specifically, T_set is sent to the heat source control unit, where its internal PID controller adjusts the power of the heating element or the hot air temperature to precisely track the setpoint; P_set and V_set are sent to the motion control unit, which respectively regulates the operation of the servo pressure valve and the drive motor.

[0054] Finally, welding parameters are optimized. Once the heat source control unit and motion control unit receive and execute the new set values, the welding execution equipment starts or continues welding operations under the optimized parameters. During the welding process, the welding process monitoring modules (such as molten pool infrared thermometers, weld vision sensors, and pressure sensors) collect real-time data on molten pool temperature, weld morphology, and pressure, which are then fed back to the central processing unit, forming a closed loop. If the subsequent real-time welding quality evaluation model determines quality fluctuations, dynamic fine-tuning can be performed based on the aforementioned parameter increments, thereby continuously maintaining the optimal state of the welding process window in both spatial and temporal dimensions. Ultimately, this ensures the formation of high-quality, highly reliable geomembrane welded joints in complex environments.

[0055] The above-described at least one technical solution adopted in the embodiments of this specification can achieve the following beneficial effects: In the environmental perception stage, the welding execution equipment integrates three types of sensors—forward-looking, lateral, and interface—to collaboratively collect multi-dimensional parameters of the microenvironment at the welding point. This effectively avoids interference from heat source radiation and instantaneous environmental disturbances, ensuring that the data accurately reflects the real-time state of the welding interface. It not only distinguishes between ordinary and extreme welding environments but also precisely identifies specific types such as low-temperature, high-humidity, and composite environments, overcoming the coarse-grained limitations of traditional binary normal / abnormal judgments and providing precise decision-making basis for subsequent differentiated strategies. In the interface treatment stage, the open-loop treatment mode with fixed parameters and fixed durations is abandoned. A dual closed-loop dynamic control system is adopted, consisting of inner-loop control commands and outer-loop treatment stage switching commands. This allows the treatment process to both instantaneously suppress overshoot / undershoot fluctuations and adaptively switch treatment strategies based on the phased laws of material heat and moisture transfer, significantly improving the consistency of treatment effects and energy utilization efficiency, and avoiding over-processing or under-treatment of the interface. In the welding parameter generation stage, the material thermal history information contained in the monitoring data of the treatment process is deeply mined, enabling welding parameters to accurately match the actual thermal response characteristics of the geomembrane caused by aging, pollution, or microenvironmental differences. This avoids the problem of parameters being out of sync with the actual state of the material in conventional static compensation. By organically integrating environmental perception, interface management, and welding parameter adjustment into a synergistic optimization, the incidence of defects such as incomplete welds, bubbles, and edge warping under extreme working conditions is significantly reduced. The final welding parameter commands not only respond to the environment and management results but also deeply couple the thermodynamic behavior of the material under the current specific state, achieving precise matching between welding energy input and instantaneous material demand, and significantly improving the scientific nature and adaptability of parameter settings. At the same time, it greatly reduces the reliance on human experience and enhances the adaptability and process robustness of automated welding systems in complex field environments.

[0056] This specification also provides an embodiment of a device for optimizing and controlling the welding parameters of geomembrane edging, such as... Figure 2 As shown, the device includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the above-described method.

[0057] This specification also provides a non-volatile computer storage medium storing computer-executable instructions configured to perform the above-described method.

[0058] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the embodiments of apparatus, devices, and non-volatile computer storage media are basically similar to the method embodiments, so the descriptions are relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0059] The above description is merely one or more embodiments of this specification and is not intended to limit this specification. Various modifications and variations can be made to the one or more embodiments of this specification by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principle of one or more embodiments of this specification should be included within the scope of the claims of this specification.

Claims

1. A method for optimizing and controlling welding parameters for geomembrane edge wrapping, characterized in that, The method includes: By integrating a multi-dimensional environmental sensing module into the welding execution equipment, multi-dimensional micro-environmental parameters of the current welding point are collected. Based on the multi-dimensional micro-environmental parameters, the current welding environment is identified and the current welding environment type is determined. The current welding environment type includes ordinary welding environment and extreme welding environment. The extreme welding environment includes low temperature environment, high humidity environment and composite environment. Based on the current welding environment type, the working parameters of the treatment equipment are dynamically adjusted to determine an adaptive and optimized set of interface treatment parameters. The welding parameter compensation value is determined through the set of interface treatment parameters, wherein the interface treatment parameters include hot air temperature parameters, wind speed parameters, and treatment stage switching instructions. Based on the welding parameter compensation value, a welding parameter adjustment command is determined and sent to the control unit of the welding execution device to achieve welding parameter optimization.

2. The method for optimizing and controlling the welding parameters of geomembrane edge wrapping according to claim 1, characterized in that, Before collecting multi-dimensional micro-environmental parameters of the current welding point through a multi-dimensional environmental sensing module integrated in the welding execution equipment, the method further includes: Obtain the three-dimensional structural model data and welding heat source operating parameters corresponding to the welding execution equipment; The three-dimensional structural model data and the welding heat source operating parameters are input into the pre-constructed thermal radiation interference simulation model to calculate the thermal radiation intensity distribution data of the welding heat source on the preset sensing area. Based on the thermal radiation intensity distribution data and the preset thermal interference suppression threshold, a set of sensor unit installation locations that meet the thermal interference suppression conditions is determined. The forward-looking sensing unit, the lateral sensing unit, and the interface sensing unit are respectively installed at corresponding positions in the set of sensor unit installation positions to realize the deployment of the multi-dimensional environment perception module. The forward-looking sensing unit is located in front of the welding heat source in the direction of travel, the lateral sensing units are located on both sides of the welding heat source, and the interface sensing unit is located in front of the pressure roller contact area of ​​the welding pressure roller.

3. The method for optimizing and controlling the welding parameters of geomembrane edge wrapping according to claim 1, characterized in that, Based on the current welding environment type, the operating parameters of the treatment equipment are dynamically adjusted to determine an adaptively optimized set of interface treatment parameters, specifically including: Based on the current welding environment type and the pre-acquired geomembrane material property data, the corresponding current basic treatment mode is matched from the preset treatment strategy library to generate initial treatment parameters, so as to control the treatment execution equipment to perform interface treatment operations according to the initial treatment parameters, and collect the interface status time-series data and treatment equipment working parameter time-series data in real time. Based on the interface state time series data of the governance process, determine the interface state change rate time series data, and generate inner loop control instructions through the interface state change rate time series data. By using the time-series data of the operating parameters of the treatment equipment, the time-series data of the treatment energy efficiency ratio is determined, and an outer ring treatment stage switching instruction is generated based on the time-series data of the treatment energy efficiency ratio. The inner-loop control command and the outer-loop governance stage switching command are merged to generate updated governance parameters, and the process is iteratively executed until the preset governance convergence condition is met, thereby determining the interface governance parameter set.

4. The method for optimizing and controlling the welding parameters of geomembrane edge wrapping according to claim 3, characterized in that, Based on the time-series data of the interface state during the governance process, time-series data of the interface state change rate are determined, and an inner-loop control command is generated using the time-series data of the interface state change rate, specifically including: Perform a first-order difference operation on the interface temperature time series data in the interface state time series data of the treatment process to generate interface temperature change rate time series data. Perform a first-order difference operation on the interface humidity time series data in the interface state time series data of the treatment process to generate interface humidity change rate time series data. The interface temperature change rate time series data is compared with the preset temperature change rate threshold range in real time. If it exceeds the upper threshold of the temperature change rate threshold range, a hot air temperature attenuation command is generated. If it is lower than the lower threshold of the temperature change rate threshold range, a hot air temperature enhancement command is generated. The interface humidity change rate time series data is compared with the preset humidity change rate threshold range in real time. If it exceeds the upper limit of the humidity change rate threshold range, a wind speed attenuation command is generated; if it is lower than the lower limit of the humidity change rate threshold range, a wind speed increase command is generated. The hot air temperature decrease command or the hot air temperature increase command, the wind speed decrease command or the wind speed increase command are combined into an inner loop control command.

5. The method for optimizing and controlling the welding parameters of geomembrane edge wrapping according to claim 3, characterized in that, By using the time-series data of the operating parameters of the treatment equipment, the treatment energy efficiency ratio time-series data is determined, and an outer ring treatment phase switching instruction is generated based on the treatment energy efficiency ratio time-series data, specifically including: Based on the time-series data of the working parameters of the treatment equipment, a time integration calculation is performed to generate cumulative treatment energy consumption data; Based on the cumulative energy consumption data, the pre-acquired initial interface state parameters, and the current interface state parameters, the unit energy consumption state improvement is calculated, and the energy efficiency ratio time series data is determined using the unit energy consumption state improvement. The sliding window slope is calculated on the time series data of the governance energy efficiency ratio to generate time series data of energy efficiency ratio change slope; If the slope values ​​of multiple consecutive windows in the time series data of the energy efficiency ratio change slope are all less than the preset attenuation slope threshold, then a corresponding governance stage switching instruction is generated according to the current basic governance mode, and the governance stage switching instruction is determined as the outer loop control instruction. When the current basic governance mode is the low temperature governance mode, an instruction is generated to switch from the rapid heating stage to the constant temperature infiltration stage; When the current basic governance mode is the high humidity governance mode, an instruction is generated to switch from the strong wind drying stage to the mild dehumidification stage; When the current basic governance mode is a composite governance mode, an instruction is generated to switch from the current governance sub-stage to the next governance sub-stage.

6. The method for optimizing and controlling the welding parameters of geomembrane edge wrapping according to claim 1, characterized in that, The welding parameter compensation values ​​are determined using the interface treatment parameter set, specifically including: Based on the interface governance parameter set and the pre-acquired governance process monitoring data, extract the governance thermal history feature vector; The thermal history feature vector of the treatment and the nominal thermal property parameter set of the geomembrane material are input into the pre-trained thermal inertia mapping model to generate the interface thermal inertia compensation coefficient. The corresponding dedicated compensation model is invoked according to the current welding environment type, and the interface state parameters at the time of achieving the treatment standard are input into the dedicated compensation model to calculate and generate a set of basic compensation values ​​for welding parameters. Based on the interface thermal inertia compensation coefficient, the basic compensation values ​​of welding temperature and welding speed in the basic compensation values ​​of welding parameters are nonlinearly corrected to generate thermal inertia-corrected welding parameter compensation values.

7. The method for optimizing and controlling the welding parameters of geomembrane edge wrapping according to claim 6, characterized in that, Based on the interface governance parameter set and pre-acquired governance process monitoring data, a governance thermal history feature vector is extracted, specifically including: Obtain complete thermal history window data from the start time of governance to the time when governance standards are met from the monitoring data of the governance process; Based on the complete thermal history window data, calculate the cumulative heat input data, interface temperature response slope data, thermal response stability data, and humidity removal efficiency data. The cumulative heat input data, interface temperature response slope data, thermal response stability data, and humidity removal efficiency data are compared with their corresponding normalized benchmark parameters to generate a set of normalized feature parameters. The normalized feature parameter set is combined in a preset order to generate the governance heat history feature vector.

8. The method for optimizing and controlling the welding parameters of geomembrane edge wrapping according to claim 6, characterized in that, Based on the interface thermal inertia compensation coefficient, the basic compensation values ​​for welding temperature and welding speed in the basic compensation values ​​for welding parameters are nonlinearly corrected to generate thermal inertia-corrected welding parameter compensation values, specifically including: Obtain the temperature correction weighting coefficient and velocity correction weighting coefficient that match the current geomembrane material type; Based on the interface thermal inertia compensation coefficient, the temperature correction weighting coefficient, and the basic welding temperature compensation value, the corrected welding temperature compensation value is calculated and generated using a preset temperature correction calculation formula. Based on the interface thermal inertia compensation coefficient, the speed correction weight coefficient, and the basic welding speed compensation value, the corrected welding speed compensation value is calculated and generated using a preset speed correction calculation formula. The corrected welding temperature compensation value, the corrected welding speed compensation value, and the basic welding pressure compensation value are combined to generate the welding parameter compensation value.

9. A device for optimizing and controlling welding parameters for geomembrane edge wrapping, characterized in that, The device includes: At least one processor; and, A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the method as described in any one of claims 1-8.

10. A non-volatile computer storage medium storing computer-executable instructions, characterized in that, The computer-executable instructions are configured to perform the method as described in any one of claims 1-8.

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