Intelligent control method and system for plastic-wood floor production process

Abstract

The embodiment of the application provides a kind of plastic wood floor production process intelligent control method and system, related to plastic wood floor production process control technical field, method includes obtaining the multidimensional real-time information of equipment in plastic wood floor production process, the multidimensional real-time information includes wood powder humidity, operating state information and the processing characteristic information of molten material, the equipment includes extruder, and the extruder includes motor, screw;In the case where the processing characteristic of molten material exists deviation, the multidimensional real-time information is associated analyzed to diagnose the root cause that causes the processing characteristic of molten material to deviate;According to the root cause diagnosed, adjust operating parameter in production process.The application can improve the intelligent level of plastic wood floor production process and product quality stability.

Description

Technical Field

[0001] This application relates to the field of process control technology for wood-plastic composite flooring production, and more specifically, to an intelligent control method and system for the wood-plastic composite flooring production process. Background Technology

[0002] In the continuous production of wood-plastic composite (WPC) flooring, extrusion molding is the core process. Precise control of key parameters such as temperature, pressure, and raw material ratios directly determines product quality and production efficiency. Traditional production methods rely on manual adjustments or macroscopic parameter feedback from intelligent systems. However, wear and tear on internal components is inevitable after long-term operation. This wear is insidious and gradual, failing to be detected in time by conventional macroscopic parameter monitoring. This leads to deviations in melt properties from ideal conditions, affecting product quality. Furthermore, problems often only surface after finished product output, causing fluctuations in production efficiency, uncontrolled energy consumption, and unstable quality. More importantly, changes in geometry and frictional characteristics caused by component wear are not significantly reflected in macroscopic sensor data. The control system maintains stable surface parameters such as die head pressure and barrel temperature by fine-tuning screw speed and heating power, displaying normal readings on the monitoring interface, but masking the continuous deterioration of internal melt properties. Screw wear reduces conveying efficiency, increases backflow, prolongs material residence time, accelerates polymer degradation, and weakens the interfacial bonding between wood fibers and the polymer matrix, resulting in decreased melt homogeneity. Existing control systems cannot directly sense and quantify changes in intrinsic physical properties such as melt viscosity and molecular weight distribution, and lack the ability to assess the health of the equipment in real time. Ultimately, quality problems are often only discovered during quality inspection sampling or customer feedback, by which time a large number of defective products have already been produced, causing economic losses and brand damage. This lag makes predictive maintenance and quality control impossible in production, leaving only reactive remediation. Existing technologies urgently need targeted improvements. Summary of the Invention

[0003] This application aims to address at least one of the technical problems existing in the prior art. To this end, this application proposes an intelligent control method and system for the production process of wood-plastic composite flooring, aiming to solve the problem that traditional control systems cannot promptly detect the hidden effects of equipment wear on melt properties during the production of wood-plastic composite flooring, leading to unstable product quality, fluctuating production efficiency, and inaccurate energy consumption control. In a first aspect, embodiments of this application provide an intelligent control method for the production process of wood-plastic composite flooring, including: The equipment used in the production process of wood-plastic composite flooring can be real-time information in multiple dimensions, including wood powder moisture content, operating status information, and processing characteristics of molten material. The equipment includes an extruder, which includes a motor and a screw. When the processing characteristics of the molten material deviate, the multi-dimensional real-time information is correlated and analyzed to diagnose the root cause of the deviation in the processing characteristics of the molten material. Adjust the operating parameters in the production process based on the root cause described in the diagnosis. According to some embodiments of this application, the moisture content of the wood flour is obtained through the following steps: Near-infrared beams are emitted into the wood powder during the production of wood-plastic composite flooring to obtain spectral signals; The moisture content of the wood flour is obtained by real-time calculation based on the spectral signal. The moisture content of the wood flour is determined based on its moisture content. According to some embodiments of this application, the operating status information is obtained through the following steps, including: Collect the actual operating voltage and current of the motor, and calculate the actual power consumption of the motor; The screw's rotational speed and conveying capacity are collected, and the screw's conveying efficiency is calculated. The operating status information is obtained based on the actual power consumption of the motor and the conveying efficiency of the screw. According to some embodiments of this application, obtaining the operating status information based on the actual power consumption of the motor and the conveying efficiency of the screw includes: The actual power consumption of the motor is compared with a preset power range to obtain a power comparison result. The conveying efficiency of the screw is compared with the preset efficiency to obtain the conveying efficiency comparison result; The operating status information is obtained based on the power comparison result and the transmission efficiency comparison result. According to some embodiments of this application, the processing characteristic information of the molten material is obtained through the following steps, including: Obtain the instantaneous current response of the extruder; The melt viscosity of the molten material is obtained by inferring from the instantaneous current response of the extruder and the preset reference response curve. Based on the viscosity of the melt, the processing characteristics information of the molten material is obtained. According to some embodiments of this application, the step of inferring the melt viscosity state of the molten material based on the instantaneous current response of the extruder and a preset reference response curve includes: The average instantaneous current of the extruder is obtained based on the instantaneous current response of the extruder; When the average instantaneous current of the extruder is greater than the corresponding benchmark of the preset benchmark response curve, the melt viscosity of the molten material is too high. When the average instantaneous current of the extruder is less than the corresponding benchmark of the preset benchmark response curve, the melt viscosity of the molten material is low. According to some embodiments of this application, obtaining processing characteristic information of the molten material based on the state of the melt viscosity includes: When the melt viscosity of the molten material is high, the processing characteristic information of the molten material is determined to be low fluidity; When the melt viscosity of the molten material is low, the processing characteristics of the molten material are determined to be excessively fluid. According to some embodiments of this application, the correlation analysis of the multi-dimensional real-time information to diagnose the root cause of the deviation in the processing characteristics of the molten material includes: When the multi-dimensional real-time information indicates that the moisture content of the wood powder is greater than the preset moisture content, the conveying efficiency of the screw is less than the preset efficiency, the actual power consumption of the motor is within the preset power range, and the processing characteristics of the molten material are low fluidity, the root cause of the deviation in the processing characteristics of the molten material is diagnosed as excessively high raw material moisture content. When the multi-dimensional real-time information indicates that the wood powder moisture content is less than the preset moisture content, the screw conveying efficiency is greater than the preset efficiency, the actual power consumption of the motor is not within the preset power range, and the processing characteristics of the molten material are low fluidity, the root cause of the deviation in the processing characteristics of the molten material is diagnosed as equipment wear. According to some embodiments of this application, adjusting the operating parameters in the production process based on the diagnosed root cause includes: If the root cause diagnosed is excessively high raw material moisture content, the feeding speed during the production process is adjusted according to the degree of deviation of the wood flour moisture content of the extruder and the preset ratio factor. When the root cause diagnosed is equipment wear, the compensation speed increase of the screw is calculated based on the screw's conveying efficiency to adjust the screw speed during the production process. Secondly, this application also discloses an intelligent control system for the production process of wood-plastic composite flooring, comprising: The acquisition module is used to acquire multi-dimensional real-time information of the equipment in the production process of wood-plastic flooring. The multi-dimensional real-time information includes wood powder moisture, operating status information, and processing characteristics information of molten material. The equipment includes an extruder, which includes a motor and a screw. The analysis module is used to perform correlation analysis on the multi-dimensional real-time information when the processing characteristics of the molten material deviate, so as to diagnose the root cause of the deviation in the processing characteristics of the molten material. The adjustment module is used to adjust the operating parameters in the production process based on the root cause diagnosed. The technical solution according to the embodiments of this application has at least the following beneficial effects: The intelligent control method for the production process of wood-plastic composite flooring disclosed in this application acquires multi-dimensional real-time information of the equipment in the production process of wood-plastic composite flooring, including wood powder humidity, operating status information, and processing characteristics information of molten materials. When there is a deviation in the processing characteristics of the molten materials, this method performs correlation analysis on these multi-dimensional real-time information to diagnose the root cause of the deviation in the processing characteristics of the molten materials. Finally, based on the diagnosed root cause, the operating parameters in the production process are adjusted. This method overcomes the shortcomings of traditional control systems in the prior art, which cannot directly perceive and quantify changes in the internal properties of the melt and are difficult to detect the deep impact of equipment wear on material processing behavior. By comprehensively monitoring and correlating the wood powder humidity, equipment operating status, and processing characteristics of molten materials, this application can accurately identify the root cause of product quality problems, such as excessive raw material humidity or equipment wear, rather than merely focusing on the surface stability of macroscopic parameters. Accordingly, the system can perform predictive maintenance and quality control, avoiding the lag of reactive remediation after problems occur. It effectively solves the dilemmas of fluctuating production efficiency, inaccurate energy consumption control, and unstable product quality, and significantly improves the level of intelligence in the production process of wood-plastic composite flooring and the stability of product quality. Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0004] The accompanying drawings are used to provide a further understanding of the technical solutions of this application and constitute a part of the specification. They are used together with the embodiments of this application to explain the technical solutions of this application and do not constitute a limitation on the technical solutions of this application. Figure 1 A flowchart illustrating an intelligent control method for the production process of wood-plastic composite flooring according to an embodiment of this application; Figure 2 This is a schematic diagram of an intelligent control system for the production process of wood-plastic composite flooring provided in one embodiment of this application. Detailed Implementation

[0005] To make the objectives, technical methods, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application. It should be noted that the meaning of "multiple" (or "more than") in the description of the embodiments of this application refers to two or more, and "greater than," "less than," "exceeding," etc. are understood to exclude the number itself, while "above," "below," "within," etc. are understood to include the number itself. If "first," "second," etc. are used in the description, they are only for the purpose of distinguishing technical features and should not be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated or the order of the technical features indicated. In this application embodiment, "at least one" refers to one or more, and "more than one" refers to two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent the existence of A alone, the simultaneous existence of A and B, or the existence of B alone. A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one of the following" and similar expressions refer to any combination of these items, including any combination of singular or plural items. For example, at least one of a, b, and c can represent: the existence of a alone, the existence of b alone, the existence of c alone, the simultaneous existence of a and b, the simultaneous existence of a and c, the simultaneous existence of b and c, or the simultaneous existence of a, b, and c, where a, b, and c can be single or multiple. In the description of this application, unless otherwise expressly defined, terms such as "setup," "installation," and "connection" should be interpreted broadly, and those skilled in the art can reasonably determine the specific meaning of the above terms in this application in conjunction with the specific content of the technical solution. The intelligent control method for the production process of wood-plastic composite flooring provided in this application can be applied to a terminal, a server, or software running on either a terminal or a server. In some embodiments, the terminal can be a smartphone, tablet, laptop, desktop computer, etc.; the server can be configured as an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (CDN), and big data and artificial intelligence platforms; the software can be an application that implements the intelligent control method for the production process of wood-plastic composite flooring, but is not limited to the above forms. The embodiments of this application can be used in numerous general-purpose or special-purpose computer system environments or configurations. Examples include: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics devices, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, etc. This application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform specific tasks or implement specific abstract data types. This application can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In a distributed computing environment, program modules can reside in local and remote computer storage media, including storage devices. See Figure 1 , Figure 1 This is a flowchart illustrating an intelligent control method for the production process of wood-plastic composite flooring according to an embodiment of this application. The intelligent control method for the production process of wood-plastic composite flooring provided in this embodiment includes, but is not limited to, steps S110 to S130, which are described in detail below. Step S110: Obtain multi-dimensional real-time information of the equipment in the production process of wood-plastic flooring. The multi-dimensional real-time information includes wood powder moisture, operating status information and processing characteristics information of molten material. The equipment includes an extruder, which includes a motor and a screw. Step S120: When the processing characteristics of the molten material deviate, perform correlation analysis on multi-dimensional real-time information to diagnose the root cause of the deviation in the processing characteristics of the molten material. Step S130: Adjust the operating parameters in the production process according to the root cause of the diagnosis. It should be noted that "WPC flooring production process" refers to the entire process of producing WPC flooring by mixing wood flour, plastics (such as polyethylene, polypropylene, etc.), and additives, and then using an extrusion molding process. "Equipment" mainly refers to the extruder, which is the core equipment in WPC flooring production, responsible for heating, melting, plasticizing, and extruding the mixture into shape. "Multi-dimensional real-time information" refers to various types of data reflecting the production status and material characteristics acquired through various sensors and monitoring methods during the production process. This data is updated in real time to facilitate timely detection and problem solving. "Wood flour moisture content" refers to the moisture content of the wood flour raw material, which has a significant impact on the plasticizing behavior of the material and the quality of the final product. "Operating status information" refers to various performance indicators of the extruder during operation, such as the motor's operating status and the screw's conveying efficiency. "Processing characteristics information of molten material" refers to the physicochemical properties of the material after melting in the extruder, such as fluidity and viscosity; these characteristics directly relate to the molding quality. "Correlation analysis" refers to using data analysis methods to find the inherent connections and causal relationships between different dimensions of information, thereby diagnosing the root cause of problems. In one embodiment, firstly, it is necessary to acquire multi-dimensional real-time information about the equipment in the wood-plastic flooring production process. This information includes wood flour moisture content, operating status information, and processing characteristics of the molten material. The equipment mainly refers to the extruder, which typically includes a motor and a screw. Wood flour moisture content can be determined by periodically sampling the wood flour raw material manually and then measuring its moisture content using traditional methods such as laboratory drying or Karl Fischer titration. While this method offers high accuracy, it suffers from latency and cannot achieve real-time monitoring. Alternatively, a resistance humidity sensor can be installed in the wood flour conveying pipeline or hopper to indirectly reflect the moisture content by measuring the resistance value of the wood flour. Resistance humidity sensors are low-cost, but their measurement accuracy is easily affected by factors such as wood flour bulk density and temperature. Finally, the actual power consumption of the motor can be calculated by reading the motor's nameplate parameters from the extruder control cabinet and manually measuring the motor's actual operating voltage and current under specific conditions using a multimeter or clamp meter, and then calculating the motor's actual power consumption according to the power calculation formula. Meanwhile, the screw conveying efficiency can be manually calculated by observing the screw's tachometer reading and combining it with the theoretical and actual material conveying capacity. This method relies on manual operation and calculation, resulting in low efficiency and difficulty in capturing instantaneous changes. Alternatively, melt pressure and temperature sensors can be installed at the extruder die head or mold to monitor the pressure and temperature of the molten material in real time. Then, based on this data and a pre-set rheological model, the viscosity and other processing characteristics of the molten material can be manually calculated or obtained from tables. This method requires complex calculations and model support, and the sensors are susceptible to material wear and contamination. Another method involves installing an infrared thermometer on the extruder barrel to monitor the barrel's outer wall temperature and judging the processing characteristics of the molten material based on experience. This method is simple and easy to implement, but it measures the barrel's outer wall temperature, which deviates significantly from the actual temperature and processing characteristics inside the melt. It should be noted that when the processing characteristics of the molten material deviate, multi-dimensional real-time information needs to be correlated and analyzed to diagnose the root cause of the deviation. When the viscosity value of the molten material obtained through the above methods is significantly higher or lower than the normal range, a deviation in processing characteristics is considered. At this point, previously obtained information such as wood flour moisture content, actual motor power consumption, and screw conveyor efficiency can be comprehensively analyzed. If the wood flour moisture content is found to be too high, while the motor power and screw efficiency are normal, but the melt viscosity is high, it is initially judged that the high wood flour moisture content may be causing poor material plasticization. If the motor power is found to be abnormally high, the screw efficiency to be low, and the melt viscosity to be high, it may be related to equipment wear. This correlation analysis can be based on preset expert experience rules or can be judged by manually comparing historical data and abnormal patterns. Finally, based on the diagnosed root cause, the operating parameters in the production process are adjusted. For example, if the diagnosis is that the wood flour moisture content is too high, the feeder speed can be manually adjusted to reduce the amount of wood flour fed, or the operating time of the drying equipment can be increased to reduce the wood flour moisture content. This application discloses an intelligent control method for the production process of wood-plastic composite (WPC) flooring. By acquiring multi-dimensional real-time information about the equipment during the WPC flooring production process, including wood powder moisture content, operating status information, and processing characteristics of the molten material, it can comprehensively and in real-time grasp various key parameters of the production process. When the processing characteristics of the molten material deviate, correlation analysis of this multi-dimensional real-time information can diagnose the root cause of the deviation. For example, by comprehensively analyzing information such as wood powder moisture content, motor power, screw efficiency, and melt viscosity, it can accurately determine whether the abnormal processing characteristics of the molten material are caused by raw material problems (such as excessive wood powder moisture content), equipment problems (such as screw wear), or improper process parameter settings. Finally, based on the diagnosed root cause, the operating parameters in the production process can be adjusted in a targeted manner, such as adjusting the feeding speed, screw speed, or heating temperature. This application, by introducing multi-dimensional real-time information, especially the comprehensive acquisition and correlation analysis of wood powder moisture content, operating status information, and processing characteristics of the molten material, can gain a deep understanding of the internal changes in the production process. This method can identify hidden problems that are difficult to detect using traditional methods. For example, by comprehensively analyzing motor power, screw efficiency, and melt viscosity, it can accurately diagnose deviations in melt properties caused by screw wear. By diagnosing the root cause and making precise adjustments, this application enables predictive maintenance and proactive quality control of the production process, avoiding the drawbacks of delayed problem detection and reactive remediation in traditional methods, and significantly improving product quality stability and production efficiency. Specifically, in the aforementioned intelligent control method for the production process of wood-plastic flooring, the method for obtaining wood flour moisture content is further refined. Wood flour moisture content is obtained through the following steps: Near-infrared beams are emitted into the wood powder during the production of wood-plastic composite flooring to obtain spectral signals; The moisture content of the wood flour is obtained by real-time calculation based on the spectral signal. The moisture content of the wood flour is determined based on its moisture content. The process of emitting near-infrared beams to obtain spectral signals from wood powder during the production of wood-plastic composite flooring refers to the non-destructive testing of wood powder using near-infrared spectroscopy. Specifically, near-infrared sensors or spectrometers can be used to scan the wood powder on the production line. By emitting near-infrared beams of specific wavelengths and receiving the reflected or transmitted spectral signals, these signals contain information about the internal moisture content of the wood powder. The aim is to obtain the physicochemical information of the wood powder, especially its moisture content, in a real-time and accurate manner through a non-contact method. Further, the real-time calculation of the wood powder moisture content based on the spectral signals involves data processing and analysis of the acquired spectral signals. Pre-established calibration models (such as partial least squares, multiple linear regression, etc.) can be used to process the spectral data, correlating spectral characteristics with the actual moisture content of the wood powder to calculate the current moisture content value in real time. Therefore, determining the wood powder humidity based on its moisture content means using the calculated moisture content directly or indirectly as a characterization of the wood powder's humidity. In practical applications, moisture content is a crucial indicator of material humidity, directly reflecting the dryness or dampness of wood flour. This application's solution utilizes near-infrared spectroscopy to achieve non-contact, real-time, and precise acquisition of wood flour humidity during the production of wood-plastic composite flooring. By emitting a near-infrared beam and analyzing the spectral signal generated by its interaction with the wood flour, the characteristic absorption information of water molecules in the wood flour can be effectively captured. Subsequently, based on these spectral signals, real-time calculations can accurately quantify the moisture content of the wood flour. Ultimately, this moisture content serves as a direct characterization of wood flour humidity, providing reliable input data for subsequent intelligent control systems. This technical solution enables online, rapid, and non-destructive detection of wood flour humidity during the production of wood-plastic composite flooring, avoiding the time-consuming, lagging, and potentially disruptive problems associated with traditional offline detection methods. This method improves the real-time performance and accuracy of humidity data, providing more reliable raw data for diagnosing deviations in the processing characteristics of molten materials, thereby contributing to enhancing the intelligence level of the entire production process and the stability of product quality. Specifically, the above-mentioned operational status information is obtained through the following steps: Collect the actual operating voltage and current of the motor, and calculate the actual power consumption of the motor; Collect the screw speed and conveying capacity, and calculate the screw conveying efficiency; Operating status information is obtained based on the actual power consumption of the motor and the conveying efficiency of the screw. The acquisition of the motor's actual operating voltage and current, and the calculation of the motor's actual power consumption, refers to the real-time monitoring of the extruder motor's voltage and current values ​​using sensors. This real-time data is used to calculate the motor's actual power consumption under the current operating conditions. This actual power consumption is an important indicator for measuring the motor's load and energy input, and its changes reflect the material resistance, friction, and equipment stability during the extrusion process. Furthermore, the acquisition of the screw's rotational speed and conveying capacity, and the calculation of the screw's conveying efficiency, refers to obtaining the extruder screw's real-time rotational speed through a speed sensor, and calculating the actual material conveying capacity using a material metering device or based on the screw's geometric parameters and rotational speed. The screw's conveying efficiency is then calculated by comparing the actual conveying capacity with the theoretical conveying capacity or through a specific algorithm. Conveying efficiency is a key parameter for evaluating the screw's ability to push materials; its level directly affects the material's residence time in the extruder, mixing uniformity, and final output. Therefore, this application, by comprehensively considering the motor's actual power consumption and the screw's conveying efficiency, can comprehensively and accurately characterize the extruder's operating status in the production process of wood-plastic composite flooring. The actual power consumption of the motor reflects the workload of the drive system, while the screw conveying efficiency reflects the effectiveness of material transfer. Combining these two key indicators can form a multi-dimensional operational status information, thus providing a solid data foundation for subsequent correlation analysis and fault diagnosis. It should be noted that the application provides access to more detailed and comprehensive equipment operating status information. This detailed operating status information, such as the actual power consumption of the motor and the screw conveying efficiency, can more accurately reflect the mechanical load and material transfer status inside the extruder. When this information is incorporated into multi-dimensional real-time information for correlation analysis, it can significantly improve the accuracy and reliability of diagnosing the root causes of deviations in the processing characteristics of molten materials. This provides strong support for timely and precise adjustment of operating parameters in the production process, effectively avoiding fluctuations in production quality or decreases in efficiency caused by abnormal equipment operating status. In some of the embodiments described above in this application, the information on obtaining the operating status based on the actual power consumption of the motor and the conveying efficiency of the screw can be further refined. Specifically, the above-mentioned operating status information, obtained based on the actual power consumption of the motor and the conveying efficiency of the screw, includes: The actual power consumption of the motor is compared with the preset power range to obtain the power comparison result; The conveying efficiency of the screw is compared with the preset efficiency to obtain the conveying efficiency comparison result; Based on the power comparison results and transmission efficiency comparison results, the operating status information is obtained. The comparison between the actual power consumption of the motor and the preset power range involves comparing the real-time power consumption of the motor during actual operation with a pre-defined power range representing normal operating conditions. This preset power range can be determined based on historical data of the extruder under normal production conditions, equipment specifications, or expert experience. Its purpose is to determine whether the motor is overloaded, underloaded, or experiencing abnormal fluctuations. This yields a power comparison result, indicating whether the motor power is within the normal range, such as "normal," "high," or "low." Further, the comparison between the screw's conveying efficiency and the preset efficiency involves comparing the real-time calculated screw conveying efficiency with a preset conveying efficiency value under ideal or normal operating conditions. This preset efficiency can be set based on the screw's design parameters, material characteristics, and expected production efficiency. Its purpose is to assess whether the screw's material conveying capacity meets requirements. This yields a conveying efficiency comparison result, indicating whether the screw conveying efficiency is at a normal level, such as "normal," "high," or "low." Based on the power comparison results and the conveying efficiency comparison results, a comprehensive judgment and operational status information can be obtained. When both motor power and screw conveying efficiency are normal, the operating status information can be judged as "normal." When motor power is high but screw conveying efficiency is low, it may indicate some mechanical resistance or wear in the equipment. When both motor power and screw conveying efficiency are low, it may indicate insufficient feeding or insufficient motor drive force. By combining these judgments, the operating status of the extruder can be reflected more comprehensively and accurately. It should be noted that the solution in this application, by quantitatively comparing the actual power consumption of the motor and the conveying efficiency of the screw, transforms the abstract operating state into a concrete and verifiable comparison result. This comparison mechanism allows the system to evaluate equipment performance based on clear thresholds or ranges, thereby avoiding errors from subjective judgment. It is precisely this refined comparison that enables subsequent operating status information to more accurately reflect the true working condition of the equipment, providing a reliable data foundation for subsequent correlation analysis and fault diagnosis. Through the above technical solution, the operating status information of the extruder can be obtained in a structured and standardized manner. This comparison method based on preset ranges and efficiency makes the evaluation of operating status more objective and accurate, helping to promptly detect potential abnormalities in the equipment, such as motor overload, screw wear, or decreased conveying efficiency. Therefore, the accuracy and reliability of operating status information acquisition are improved, providing more solid data support for the intelligent control of the plastic flooring production process. Specifically, the processing characteristics information of the aforementioned molten material can be obtained through the following steps. The processing characteristics information of the molten material can be obtained through the following steps, including: The instantaneous current response of the extruder is obtained; based on the instantaneous current response of the extruder and the preset reference response curve, the melt viscosity state of the molten material is inferred. Based on the viscosity of the melt, information on the processing characteristics of the molten material is obtained. The acquisition of the extruder's instantaneous current response refers to real-time monitoring of current changes during extruder operation using sensors or ammeters. When processing molten material, the motor load of the extruder varies with the material's viscosity, flowability, and other processing characteristics, directly reflected in the motor's instantaneous current response. Furthermore, the melt viscosity of the molten material is inferred from the extruder's instantaneous current response and a preset reference response curve. The preset reference response curve is established based on the extruder's current response data when processing a specific material under standard or ideal processing conditions. By comparing the real-time instantaneous current response with this reference curve, it can be determined whether the current melt viscosity is too high, too low, or within the normal range. When the instantaneous current response is higher than the reference curve, it may indicate that the material viscosity is too high, requiring the extruder to generate more power to propel the material; conversely, when the instantaneous current response is lower than the reference curve, it may indicate that the material viscosity is too low. Therefore, information on the processing characteristics of the molten material can be obtained based on the melt viscosity. Melt viscosity is a key indicator reflecting the flowability of materials, and its state directly determines the processing characteristics of materials during extrusion, such as whether the flowability is too low or too high. It should be noted that the solution in this application indirectly and effectively reflects the actual viscosity state of the molten material during the extrusion process by monitoring the instantaneous current response of the extruder in real time and comparing it with a preset reference response curve. When processing wood-plastic composite flooring materials, the viscosity of the material directly affects the load of the extruder, which is reflected in the change of motor current. By capturing these current changes and combining them with a preset current reference under normal processing conditions, it is possible to accurately infer whether the current melt viscosity of the molten material is in an ideal state, too high, or too low. This inference method based on current response avoids the complexity and lag of directly measuring melt viscosity, providing a real-time and reliable data foundation for subsequent processing characteristic information acquisition. Through the above technical solution, processing characteristic information of molten materials, especially the state of melt viscosity, can be obtained in real time and accurately. This method utilizes the extruder's own operating data, eliminating the need for additional complex sensors to directly measure material viscosity, thus reducing system costs and maintenance difficulty. Meanwhile, by analyzing the instantaneous current response, abnormalities in the processing characteristics of molten materials can be detected in a timely manner, providing key input data for subsequent correlation analysis and adjustment of operating parameters, thereby improving the level of intelligent control and product quality stability in the production process of wood-plastic composite flooring. Specifically, the above inference based on the instantaneous current response of the extruder and the preset reference response curve to obtain the melt viscosity state of the molten material can be further refined into the following steps: The average instantaneous current of the extruder is obtained based on the instantaneous current response of the extruder; When the average instantaneous current of the extruder is greater than the corresponding benchmark of the preset benchmark response curve, the melt viscosity of the molten material is too high. When the average instantaneous current of the extruder is less than the corresponding benchmark of the preset benchmark response curve, the melt viscosity of the molten material is too low. The instantaneous current response of the extruder refers to the real-time current change exhibited by its motor during operation. To eliminate the influence of instantaneous fluctuations on the judgment result, the instantaneous current response can be averaged to obtain the average instantaneous current of the extruder. This average instantaneous current can more stably reflect the actual working load of the extruder within a certain time period. The corresponding benchmark of the preset reference response curve can be understood as the reference value or range of the instantaneous current of the extruder motor when the molten material is in an ideal viscosity state under normal production conditions. This benchmark is usually set through historical data analysis, experimental testing, or expert experience. The solution in this application compares the real-time acquired average instantaneous current of the extruder with the corresponding benchmark of the preset reference response curve to infer the viscosity state of the molten material. When the viscosity of the molten material is high, the extruder needs to overcome greater resistance during conveying and plasticizing, resulting in an increased motor load, which manifests as an average instantaneous current higher than the preset benchmark. Conversely, when the viscosity of the molten material is low, the resistance experienced by the extruder decreases, the motor load decreases, and the average instantaneous current will be lower than the preset benchmark. This direct and quantitative comparison method effectively determines the actual viscosity of the molten material. It should be noted that, through the above technical solution, this application provides a clear and quantifiable method for determining the melt viscosity state of molten materials. This comparison based on the instantaneous average current and a preset benchmark makes the evaluation of the processing characteristics of molten materials more objective and accurate, avoiding errors from subjective judgment. Therefore, abnormalities in the viscosity of molten materials can be detected more promptly and accurately, providing a reliable data foundation for subsequent correlation analysis and operating parameter adjustments, thereby improving the precision and efficiency of intelligent control in the production process of wood-plastic composite flooring. Specifically, obtaining the processing characteristics information of the molten material based on the melt viscosity can include the following steps: When the melt viscosity of the molten material is high, the processing characteristics of the molten material are determined to be low fluidity. When the melt viscosity of the molten material is low, the processing characteristics of the molten material are determined to be excessively fluid. Melt viscosity refers to the ability of a material to resist flow in its molten state. High melt viscosity indicates poor material flowability during extrusion, potentially leading to extrusion difficulties, rough product surfaces, or internal defects. Conversely, low melt viscosity indicates excessive material flowability, which may result in reduced conveying efficiency in the extruder screw, poor product molding, or dimensional instability. By directly mapping the state of melt viscosity to specific processing characteristics (low or excessive flowability), clear guidance can be provided for subsequent production parameter adjustments. This application's solution directly transforms the qualitative state of melt viscosity (high or low) into processing characteristics with clear physical meaning (low or excessive flowability), establishing a direct correlation between the material's microscopic properties and macroscopic processing behavior. This transformation mechanism makes the understanding of material processing characteristics more intuitive and concrete. When melt viscosity is high, its intrinsic physical manifestation is enhanced intermolecular forces or high molecular chain entanglement, making the material difficult to flow under shear force, thus being judged as having low flowability. Conversely, when the melt viscosity is low, it means that the intermolecular forces are weak or the molecular chain entanglement is low, making the material easy to flow under shear force, and thus it is judged to have excessive fluidity. This direct mapping relationship simplifies the understanding of complex rheological parameters, enabling production control systems to identify the processing state of materials more quickly and accurately. It should be noted that the above technical solution can transform the abstract melt viscosity state into specific and understandable processing characteristic information, thereby providing a clearer and more direct basis for intelligent control of the production process. This clear judgment mechanism helps operators or automated systems quickly identify the actual processing behavior of materials, avoiding misjudgments caused by a vague understanding of the viscosity state, thus improving the response speed and adjustment accuracy of the production process and ensuring the quality stability of the wood-plastic composite flooring products. In some embodiments described above in this application, a correlation analysis of multi-dimensional real-time information is proposed to diagnose the root causes of deviations in the processing characteristics of molten materials. However, in actual production processes, the root causes of deviations in the processing characteristics of molten materials may be diverse, and different causes may exhibit similar symptoms. Without clear diagnostic rules, this could lead to low diagnostic efficiency or misjudgment, thereby affecting the timely adjustment and control of the production process. Therefore, this application further proposes the steps described above for performing correlation analysis of multi-dimensional real-time information to diagnose the root causes of deviations in the processing characteristics of molten materials, specifically including: When multi-dimensional real-time information indicates that the moisture content of the wood flour is greater than the preset moisture content, the conveying efficiency of the screw is less than the preset efficiency, the actual power consumption of the motor is within the preset power range, and the processing characteristics of the molten material are low fluidity, the root cause of the deviation in the processing characteristics of the molten material is that the raw material moisture content is too high. When multi-dimensional real-time information indicates that the moisture content of the wood powder is less than the preset moisture content, the conveying efficiency of the screw is greater than the preset efficiency, the actual power consumption of the motor is not within the preset power range, and the processing characteristics of the molten material are low fluidity, the root cause of the deviation in the processing characteristics of the molten material is diagnosed as equipment wear. Specifically, the aforementioned "multi-dimensional real-time information indicating wood flour moisture content is greater than the preset moisture content" refers to the real-time monitoring of wood flour moisture content, revealing that its value exceeds the upper limit allowed by the production process, indicating an abnormally high moisture content in the raw material. The aforementioned "screw conveying efficiency is less than the preset efficiency" means that the actual efficiency of the extruder screw conveying material per unit time is lower than the normal or expected level, potentially indicating obstruction during material conveying or a decline in the screw's own performance. The aforementioned "actual power consumption of the motor is within the preset power range" means that the electrical power consumed by the motor driving the extruder screw remains within the power range required for normal operation, indicating that the motor itself is operating relatively stably without overload or no-load abnormalities. The aforementioned "processing characteristics of the molten material are low fluidity" means that the molten wood-plastic composite material exhibits high viscosity or poor fluidity, which affects the plasticization and molding quality of the material. When the above conditions are combined—that is, excessively high wood flour moisture content, low screw conveying efficiency, normal motor power, and poor molten material fluidity—it can be clearly diagnosed that "the root cause of the deviation in the processing characteristics of the molten material is excessively high raw material moisture content." This is because excessively high wood flour moisture content increases friction and resistance within the extruder, reducing the screw's effective conveying capacity. It also leads to increased viscosity and decreased fluidity of the molten material. The fact that the motor power is normal eliminates the possibility of motor failure. In one embodiment, the phrase "multi-dimensional real-time information characterizing wood flour moisture content is less than the preset moisture content" refers to the wood flour moisture content being at a normal or low level. The phrase "screw conveying efficiency is greater than the preset efficiency" refers to the screw's actual conveying efficiency being higher than normal. In some cases, this may mean that the friction between the material and the screw and barrel is reduced, causing the material to be "pushed" faster but with insufficient plasticization. The phrase "the actual power consumption of the motor is not within the preset power range" usually refers to the motor power being too low, possibly due to an increased gap between the screw and barrel, resulting in reduced material resistance and a lighter motor load. When these conditions combine—that is, wood flour moisture is normal or low, screw conveying efficiency is high, motor power is low, and the molten material has poor flowability—it can be diagnosed that "the root cause of the deviation in the processing characteristics of the molten material is equipment wear." This is because long-term wear of the extruder screw or barrel leads to increased gaps, weakening the shearing and mixing action on the material within the extruder, resulting in poor plasticization, manifested as poor molten material flowability, while simultaneously reducing the motor load and causing abnormal conveying efficiency. It should be noted that the solution proposed in this application effectively addresses the ambiguity and uncertainty that may exist in traditional methods when diagnosing deviations in the processing characteristics of molten materials from their root causes by establishing diagnostic rules that correlate multi-dimensional real-time information with specific root causes. By comprehensively analyzing information on wood flour moisture content, screw conveying efficiency, actual motor power consumption, and the processing characteristics of the molten material, this application can identify specific combination patterns. For example, when the wood flour moisture content is abnormally high, and the screw conveying efficiency decreases while the motor power is normal but the fluidity of the molten material deteriorates, these characteristics collectively point to the root cause of excessively high raw material moisture content. This is because high-moisture raw materials absorb a large amount of heat during extrusion, affecting the plasticizing effect and increasing the material viscosity, while also increasing the screw conveying resistance. However, as long as the motor can maintain normal power output, the problem is not with the motor itself. Conversely, when the wood flour moisture content is normal, but the screw conveying efficiency is abnormally high (or decreases only slightly), the motor power is low, and the fluidity of the molten material is poor, these characteristics collectively point to equipment wear. This is because wear on the screw or barrel leads to increased clearance, weakened shearing action on the material, poor plasticizing, and reduced motor load, resulting in decreased power. This mechanism, based on multi-dimensional information collaborative judgment, makes the diagnostic results more accurate and reliable. This application enables precise and rapid diagnosis of the root causes of deviations in the processing characteristics of molten materials during the production of wood-plastic composite flooring. Compared to judgments based solely on a single parameter or experience, this application, through systematic correlation analysis of wood powder moisture, operating status information (including motor power and screw conveyor efficiency), and processing characteristic information of the molten material, can effectively distinguish deviations in processing characteristics caused by different factors (such as excessively high raw material moisture or equipment wear), avoiding misjudgments and delays. This precise diagnostic capability provides a solid foundation for subsequent targeted adjustments to operating parameters, thereby significantly improving the intelligence level and control efficiency of the production process, reducing scrap rates and production costs, and ensuring the stability of product quality. In some embodiments, assuming that on a wood-plastic composite flooring production line, the system detects that the flowability of the molten material output from the extruder is consistently low. At this time, the intelligent control system further acquires and analyzes multi-dimensional real-time information. The system detects that the moisture content value reported by the wood flour moisture sensor is 15%, far exceeding the preset upper limit of 8%; simultaneously, the screw conveying efficiency is calculated at 80 kg / h, lower than the preset efficiency standard of 100 kg / h; while the actual power consumption of the motor driving the screw is 50 kW, within the preset normal power range of 45 kW-55 kW. Based on the low flowability of the molten material, the system, according to preset diagnostic rules, determines that the root cause of the deviation in the processing characteristics of the molten material is excessively high raw material moisture. In another production cycle, the system similarly detects low flowability of the molten material. At this time, the moisture content reported by the wood flour moisture sensor is 7%, within the preset normal range; the screw conveying efficiency is calculated at 110 kg / h, slightly higher than the preset efficiency standard of 100 kg / h; while the actual power consumption of the motor driving the screw is 35 kW, lower than the preset normal power range of 45 kW-55 kW. Based on the low fluidity of the molten material, the system, according to preset diagnostic rules, determines that the root cause of the deviation in the processing characteristics of the molten material is equipment wear. In response, this application further proposes adjusting operating parameters in the production process based on the root cause of the diagnosis, including: When the root cause of the diagnosis is excessively high raw material moisture content, the feeding speed during the production process is adjusted according to the degree of deviation of the wood flour moisture content in the extruder and the preset ratio factor. When the root cause of the diagnosis is equipment wear, the amount of compensation for the screw speed increase is calculated based on the screw's conveying efficiency in order to adjust the screw speed during the production process. Specifically, when the system diagnoses that the root cause of the deviation in the processing characteristics of the molten material is excessively high raw material moisture content, the adjustment strategy is set to adjust the feeding speed during the production process. The deviation in wood flour moisture content refers to the difference between the currently measured wood flour moisture content and the target or ideal wood flour moisture content; this difference can be an absolute value or a percentage. A preset proportional factor is a pre-determined coefficient used to translate the deviation in wood flour moisture content into a specific adjustment amount for the feeding speed. For example, when the deviation in wood flour moisture content is large, the feeding speed can be significantly reduced accordingly based on this proportional factor to extend the residence time of the material in the extruder, allowing it more time for heating and plasticizing, thereby effectively compensating for the adverse effects of excessive moisture content on the material's melting characteristics. When the system diagnoses that the root cause of the deviation in the processing characteristics of the molten material is equipment wear, the adjustment strategy is set to adjust the screw speed during the production process. The screw conveying efficiency refers to the ratio between the actual amount of material conveyed by the screw per unit time and the theoretical conveying amount. Equipment wear, particularly wear between the extruder screw and barrel, leads to increased material leakage during conveying, thus reducing the screw's actual conveying efficiency. The compensation speed increase refers to the additional screw speed required to compensate for the decreased conveying efficiency caused by equipment wear. This increase can be calculated by comparing the current screw conveying efficiency with that under normal operating conditions, combined with a preset compensation model or empirical formula. By increasing the screw speed, insufficient material conveying caused by wear can be compensated for to some extent, ensuring that the material's filling degree and residence time within the extruder remain within a reasonable range, thereby maintaining the stable processing characteristics of the molten material. It should be noted that the solution in this application achieves refined control of the plastic-wood flooring production process by adopting differentiated adjustment strategies for different root causes. When excessive raw material moisture is diagnosed, reducing the feeding speed can effectively prolong the heating and plasticizing time of the material in the extruder, thereby offsetting the adverse effects of excessive moisture on the melting characteristics of the material and ensuring that the viscosity of the molten material is within the ideal range. This adjustment method directly affects the input amount of material, adjusting the processing conditions of the material in the extruder from the source. When equipment wear is diagnosed, wear will lead to a decrease in screw conveying efficiency, which, if not compensated, will affect the filling and plasticizing of the material. By calculating and increasing the compensation speed of the screw, the insufficient material conveying caused by wear can be effectively compensated, maintaining a stable filling state and processing pressure of the material in the extruder, thereby ensuring that the processing characteristics of the molten material are not significantly affected by equipment wear. This targeted adjustment mechanism avoids the negative effects that may result from blind adjustment or single parameter adjustment, enabling the control system to respond more accurately and effectively to abnormal situations in the production process. This application can provide more precise and effective operating parameter adjustment strategies based on different root causes. To address the issue of excessively high raw material moisture content, adjusting the feeding speed directly affects the material's residence time and heating uniformity within the extruder, effectively improving the processing characteristics of the molten material and preventing problems such as low fluidity caused by excessive moisture. Regarding equipment wear, calculating and adjusting the screw's compensating speed effectively compensates for the decrease in conveying efficiency caused by mechanical wear, ensuring stable material conveying and full plasticization. This maintains the good processing performance of the molten material, extends equipment lifespan, and reduces production fluctuations caused by equipment wear. This differentiated and refined adjustment method significantly improves the intelligent control level of the plastic wood flooring production process, reduces the scrap rate, and improves product quality and production efficiency. In some embodiments, it is assumed that during the production of wood-plastic composite flooring, multi-dimensional real-time information correlation analysis diagnoses the root cause of the deviation in the processing characteristics of the molten material as excessively high raw material moisture content. Specifically, the moisture content of the wood powder measured by the extruder is 15%, while the preset ideal moisture content is 10%. At this point, the deviation in wood powder moisture content is 5%. Based on a preset proportional factor (e.g., for every 1% deviation in moisture content, the feeding speed decreases by 0.5 kg / h), the system calculates that the feeding speed needs to be reduced by 2.5 kg / h. The control system then sends a command to the feeding device to adjust the current feeding speed from 100 kg / h to 97.5 kg / h. By reducing the feeding speed, the residence time of the material in the extruder increases, allowing the high-moisture wood powder more time to dry and plasticize, thereby restoring the viscosity of the molten material to a normal level and ensuring the quality of the wood-plastic composite flooring. In another production cycle, the system diagnoses the root cause of the deviation in the processing characteristics of the molten material as equipment wear. Specifically, by collecting the screw's rotational speed and conveying capacity, the system calculates that the current screw conveying efficiency is 85%, while the normal conveying efficiency should be 95%. Based on the decrease in conveying efficiency (10%) and a preset compensation model, the system calculates that the screw needs to be increased by 50 rpm to compensate. The control system then sends a command to the extruder motor to adjust the current screw speed from 1000 rpm to 1050 rpm. By increasing the screw speed, insufficient material conveying caused by screw wear can be effectively compensated, ensuring that the material filling degree in the extruder remains stable, thereby maintaining good flowability of the molten material and avoiding production instability caused by equipment wear. See Figure 2 , Figure 2 This is a schematic diagram of an intelligent control system for the production process of wood-plastic composite flooring according to one embodiment of this application. The intelligent control system 200 for the production process of wood-plastic composite flooring includes: The acquisition module 210 is used to acquire multi-dimensional real-time information of the equipment in the production process of wood-plastic flooring. The multi-dimensional real-time information includes wood powder moisture, operating status information and processing characteristics information of molten material. The equipment includes an extruder, which includes a motor and a screw. Analysis module 220 is used to perform correlation analysis on multi-dimensional real-time information when there is a deviation in the processing characteristics of molten material, in order to diagnose the root cause of the deviation in the processing characteristics of molten material. The adjustment module 230 is used to adjust the operating parameters in the production process based on the root cause of the diagnosis. It should be noted that the information interaction and execution process between the above modules are based on the same concept as the method embodiments of this application. For details on their specific functions and technical effects, please refer to the method embodiments section, and they will not be repeated here. It will be understood by those skilled in the art that all or some of the steps and systems in the methods disclosed above can be implemented as software, firmware, hardware, and suitable combinations thereof. Some or all of the physical components can be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application-specific integrated circuit. Such software can be distributed on a computer-readable medium, which can include computer storage media (or non-transitory media) and communication media (or transient media). As is known to those skilled in the art, the term computer storage media includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, program modules, or other data). Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technologies, CD-ROM, digital versatile disc (DVD) or other optical disc storage, magnetic cartridges, magnetic tape, disk storage or other magnetic storage devices, or any other medium that can be used to store desired information and is accessible to a computer. Furthermore, as is known to those skilled in the art, communication media typically include computer-readable instructions, data structures, program modules, or other data in modulated data signals such as carrier waves or other transmission mechanisms, and may include any information delivery medium. The foregoing has provided a detailed description of the preferred embodiments of this application. However, this application is not limited to the above-described embodiments. Those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of this application. All such equivalent modifications or substitutions are included within the scope defined in this application.

Claims

1. A method for intelligent control of the production process of wood-plastic composite flooring, characterized in that, include: The equipment used in the production process of wood-plastic composite flooring can be real-time information in multiple dimensions, including wood powder moisture content, operating status information, and processing characteristics of molten material. The equipment includes an extruder, which includes a motor and a screw. When the processing characteristics of the molten material deviate, the multi-dimensional real-time information is correlated and analyzed to diagnose the root cause of the deviation in the processing characteristics of the molten material. Adjust the operating parameters in the production process based on the root cause described in the diagnosis.

2. The method according to claim 1, characterized in that, The moisture content of the wood flour is obtained through the following steps: Near-infrared beams are emitted into the wood powder during the production of wood-plastic composite flooring to obtain spectral signals; The moisture content of the wood flour is obtained by real-time calculation based on the spectral signal. The moisture content of the wood flour is determined based on its moisture content.

3. The method according to claim 1, characterized in that, The operational status information is obtained through the following steps: Collect the actual operating voltage and current of the motor, and calculate the actual power consumption of the motor; The screw's rotational speed and conveying capacity are collected, and the screw's conveying efficiency is calculated. The operating status information is obtained based on the actual power consumption of the motor and the conveying efficiency of the screw.

4. The method according to claim 3, characterized in that, The step of obtaining the operating status information based on the actual power consumption of the motor and the conveying efficiency of the screw includes: The actual power consumption of the motor is compared with a preset power range to obtain a power comparison result. The conveying efficiency of the screw is compared with the preset efficiency to obtain the conveying efficiency comparison result; The operating status information is obtained based on the power comparison result and the transmission efficiency comparison result.

5. The method according to claim 1, characterized in that, The processing characteristics information of the molten material is obtained through the following steps, including: Obtain the instantaneous current response of the extruder; The melt viscosity of the molten material is obtained by inferring from the instantaneous current response of the extruder and the preset reference response curve. Based on the viscosity of the melt, the processing characteristics information of the molten material is obtained.

6. The method according to claim 5, characterized in that, The step of inferring the melt viscosity state of the molten material based on the instantaneous current response of the extruder and a preset reference response curve includes: The average instantaneous current of the extruder is obtained based on the instantaneous current response of the extruder; When the average instantaneous current of the extruder is greater than the corresponding benchmark of the preset benchmark response curve, the melt viscosity of the molten material is too high. When the average instantaneous current of the extruder is less than the corresponding benchmark of the preset benchmark response curve, the melt viscosity of the molten material is low.

7. The method according to claim 5, characterized in that, The step of obtaining processing characteristic information of the molten material based on the viscosity of the melt includes: When the melt viscosity of the molten material is high, the processing characteristic information of the molten material is determined to be low fluidity; When the melt viscosity of the molten material is low, the processing characteristics of the molten material are determined to be excessively fluid.

8. The method according to claim 1, characterized in that, The correlation analysis of the multi-dimensional real-time information to diagnose the root cause of the deviation in the processing characteristics of the molten material includes: When the multi-dimensional real-time information indicates that the moisture content of the wood powder is greater than the preset moisture content, the conveying efficiency of the screw is less than the preset efficiency, the actual power consumption of the motor is within the preset power range, and the processing characteristics of the molten material are low fluidity, the root cause of the deviation in the processing characteristics of the molten material is diagnosed as excessively high raw material moisture content. When the multi-dimensional real-time information indicates that the wood powder moisture content is less than the preset moisture content, the screw conveying efficiency is greater than the preset efficiency, the actual power consumption of the motor is not within the preset power range, and the processing characteristics of the molten material are low fluidity, the root cause of the deviation in the processing characteristics of the molten material is diagnosed as equipment wear.

9. The method according to claim 1, characterized in that, Adjusting operating parameters in the production process based on the diagnosed root cause includes: If the root cause diagnosed is excessively high raw material moisture content, the feeding speed during the production process is adjusted according to the degree of deviation of the wood flour moisture content of the extruder and the preset ratio factor. When the root cause diagnosed is equipment wear, the compensation speed increase of the screw is calculated based on the screw's conveying efficiency to adjust the screw speed during the production process.

10. An intelligent control system for the production process of wood-plastic composite flooring, characterized in that, include: The acquisition module is used to acquire multi-dimensional real-time information of the equipment in the production process of wood-plastic flooring. The multi-dimensional real-time information includes wood powder moisture, operating status information, and processing characteristics information of molten material. The equipment includes an extruder, which includes a motor and a screw. The analysis module is used to perform correlation analysis on the multi-dimensional real-time information when the processing characteristics of the molten material deviate, so as to diagnose the root cause of the deviation in the processing characteristics of the molten material. The adjustment module is used to adjust the operating parameters in the production process based on the root cause diagnosed.

Images