Intelligent monitoring method and system for production process of container floor

Abstract

The application relates to the technical field of intelligent monitoring, and particularly provides a production process intelligent monitoring method and system for a container bottom plate, the method comprising the following steps: when it is monitored that the bending strength of a current plate of the container bottom plate is less than the bending strength of a preset plate, determining a compensation amount of the bending strength based on the bending strength of the current plate and the bending strength of the preset plate; inputting the compensation amount into a preset deep learning model to obtain hot-press forming adjustment parameter data of a hot-press forming device; the hot-press forming adjustment parameter output by the model reasoning is used to send a control instruction to the hot-press forming device in real time, the closed-loop adjustment mechanism completely replaces the trial-and-error adjustment depending on artificial experience in the prior art, the parameter optimization driven by data is used to avoid the plate embrittlement caused by excessive hot pressing, and the mechanical property stability and production consistency of the container bottom plate are significantly improved.

Description

Technical Field

[0001] The present application relates to the field of intelligent monitoring technology, and in particular to a method and system for intelligent monitoring of the production process of container bottom plates. Background Art

[0002] The container floor is a critical component of the container, and its quality directly impacts its overall performance and service life. Traditional container floor production relies primarily on manual labor and empirical judgment, resulting in the following issues: Key parameters in the production process are difficult to monitor and accurately control in real time, leading to unstable product quality; quality inspections rely primarily on manual spot checks, which are inefficient and prone to missed inspections; the operating status of production equipment is not effectively monitored, making it prone to malfunctions and downtime; and production data is stored in a decentralized manner, making traceability analysis and process optimization difficult. Summary of the Invention

[0003] One purpose of the present application is to provide a method and system for intelligently monitoring the production process of container bottom plates, so as to solve the problem of how to improve the production quality of container bottom plates.

[0004] To achieve the above objectives, some embodiments of the present application provide the following aspects:

[0005] In a first aspect, some embodiments of the present application further provide a method for intelligently monitoring a production process of a container floor, the method comprising:

[0006] When it is monitored that the bending strength of the current plate of the container bottom plate is less than the bending strength of the preset plate, a compensation amount for the bending strength is determined based on the bending strength of the current plate and the bending strength of the preset plate;

[0007] Inputting the compensation amount into a preset deep learning model to obtain hot pressing forming adjustment parameter data of the hot pressing forming equipment; wherein the hot pressing forming adjustment parameter data includes hot pressing machine pressure, hot pressing temperature, and hot pressing time;

[0008] Based on the hot pressing forming adjustment parameter data, the hot pressing forming equipment of the container bottom plate is controlled.

[0009] In a second aspect, some embodiments of the present application further provide an intelligent monitoring system for the production process of a container bottom plate, the system comprising:

[0010] The first acquisition module is configured to determine a compensation amount for the bending strength based on the bending strength of the current plate and the bending strength of the preset plate when the bending strength of the current plate of the container bottom plate is monitored to be less than the bending strength of the preset plate;

[0011] a first data processing module configured to input the compensation amount into a preset deep learning model to obtain hot pressing forming adjustment parameter data of the hot pressing forming equipment; wherein the hot pressing forming adjustment parameter data includes hot pressing pressure, hot pressing temperature, and hot pressing time;

[0012] The second data processing module is configured to control the hot pressing forming equipment of the container bottom plate based on the hot pressing forming adjustment parameter data.

[0013] In a third aspect, some embodiments of the present application further provide an electronic device comprising: one or more processors; and a memory storing computer program instructions, wherein the computer program instructions, when executed, cause the processor to perform the steps of the method described above.

[0014] In a fourth aspect, some embodiments of the present application further provide a computer-readable medium having computer program instructions stored thereon, wherein the computer program instructions can be executed by a processor to implement the method described above.

[0015] Compared to related technologies, the solution provided in the embodiments of this application includes an intelligent monitoring method for the production process of container floor panels. When the bending strength of the current container floor panel is detected to be lower than the preset bending strength, the bending strength compensation is calculated by the difference between the current measured value (the current panel's bending strength) and the target value (the preset bending strength). This compensation is then input into a pre-set deep learning model. This model, trained on a massive amount of historical production data (covering hot pressing parameters and corresponding bending strength test results for over 5,000 batches), utilizes an improved LSTM neural network architecture to accurately capture the nonlinear mapping relationship between hot press pressure, temperature, and time, and panel bending strength. For example, the model can identify the differences in the strength effects of two process paths, "high temperature, high pressure, short time" versus "medium temperature, medium pressure, long time," on substrates of varying densities, and prioritize the optimization solution with lower energy consumption. The hot press adjustment parameters (including press pressure, temperature, and time) output by the model are used to send control instructions to the hot press equipment in real time. This closed-loop adjustment mechanism completely replaces the traditional trial-and-error adjustment that relies on manual experience. Through data-driven parameter optimization, the failure rate of bending strength has been reduced from 3.2% to below 0.8%, while avoiding the problem of plate embrittlement caused by excessive hot pressing, significantly improving the mechanical performance stability and production consistency of the container bottom plate. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] One or more embodiments are exemplarily illustrated by pictures in the corresponding drawings. These exemplifications do not constitute limitations on the embodiments. Elements with the same reference numerals in the drawings are represented as similar elements. Unless otherwise stated, the figures in the drawings do not constitute proportional limitations.

[0017] Figure 1 A schematic diagram of a process flow of an intelligent monitoring method for a container bottom plate production process provided in an embodiment of the present application;

[0018] Figure 2 This is a schematic diagram of the structure of an intelligent monitoring system for the production process of a container bottom plate according to an embodiment of the present application;

[0019] Figure 3 is a schematic diagram of an exemplary structure of a processor and memory according to the present application;

[0020] Figure 4 Schematic diagram of an exemplary structure of an electronic device according to the present application. DETAILED DESCRIPTION

[0021] To make the purpose, technical solutions, and advantages of the embodiments of this application more clear, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the drawings in the embodiments of this application. Obviously, the described embodiments are part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0022] See attached Figure 1 , Figure 1 This is a flow chart of an intelligent monitoring method for the production process of a container bottom plate provided in an embodiment of the present application. Figure 1 As shown, the main methods of the intelligent monitoring method for the production process of the container bottom plate in the embodiment of the present application include:

[0023] Step S100: When it is monitored that the bending strength of the current plate of the container bottom plate is less than the bending strength of the preset plate, a compensation amount for the bending strength is determined based on the bending strength of the current plate and the bending strength of the preset plate;

[0024] Step S102: Inputting the compensation amount into a preset deep learning model to obtain hot pressing forming adjustment parameter data of the hot pressing forming equipment; wherein the hot pressing forming adjustment parameter data includes hot pressing machine pressure, hot pressing temperature, and hot pressing time;

[0025] Step S104: Controlling the hot pressing forming equipment of the container bottom plate based on the hot pressing forming adjustment parameter data.

[0026] In this embodiment, when the bending strength of the current container floor sheet is detected to be lower than the target bending strength, the compensation for this bending strength is calculated as the difference between the measured value (the current sheet's bending strength) and the target value (the target bending strength). This compensation is then input into a pre-set deep learning model. This model, trained on a massive amount of historical production data (covering over 5,000 batches of hot-pressing parameters and corresponding bending strength test results), utilizes an improved LSTM neural network architecture to accurately capture the nonlinear mapping between press pressure, temperature, and time, and sheet bending strength. For example, the model can identify the differences in the strength effects of two process paths, "high temperature, high pressure, short time," and "medium temperature, medium pressure, long time," on substrates of varying densities, prioritizing the optimization option with lower energy consumption. The hot-pressing adjustment parameters (including press pressure, temperature, and time) derived from the model's inference are then used to send control commands to the hot-pressing equipment in real time. This closed-loop adjustment mechanism completely replaces the traditional trial-and-error adjustment that relies on manual experience. Through data-driven parameter optimization, the failure rate of bending strength has been reduced from 3.2% to below 0.8%, while avoiding the problem of plate embrittlement caused by excessive hot pressing, significantly improving the mechanical performance stability and production consistency of the container bottom plate.

[0027] In one embodiment, when it is monitored that the moisture content of the current plate material of the container bottom plate is less than a preset first moisture content or greater than a preset second preset moisture content;

[0028] Input the current moisture content of the board into a preset moisture content adjustment model to obtain dryer adjustment parameter data; wherein the dryer adjustment parameter data includes dryer temperature, dryer wind speed, dryer drying time, and dryer humidity;

[0029] The container floor dryer is controlled based on the dryer adjustment parameter data.

[0030] In this embodiment, when the moisture content of the container floor panels is detected to be lower than a preset first moisture content (e.g., 8%) or higher than a preset second moisture content (e.g., 12%), a dynamic adjustment process is immediately initiated. The current moisture content data is then fed into a pre-set moisture content adjustment model to calculate appropriate dryer adjustment parameters. These parameters include dryer temperature (dynamically adjusted within the range of 60-100°C), dryer wind speed (adaptable within the range of 1-3 m / s), dryer drying time, and dryer humidity. The system uses these parameters to control the dryer in real time. If the moisture content is low, the temperature is appropriately lowered and the ambient humidity is increased to minimize moisture loss. If the moisture content is high, the temperature is increased, the wind speed is increased, and the drying time is extended to accelerate moisture evaporation. This precise control mechanism effectively stabilizes the moisture content of the panels within the optimal range of 8%-12%, significantly reducing the risk of floor deformation and cracking caused by abnormal moisture content.

[0031] In one embodiment, the preset moisture content adjustment model is constructed by the following formula:

[0032] ;

[0033] Where, is the moisture content of the current board, To balance the moisture content, is the target moisture content, is the first model constant, is the second model constant, is the dryer temperature, is the third model constant, is the first fitting coefficient, is the second fitting coefficient, is the dryer wind speed, is the third fitting coefficient, is the saturated humidity at drying temperature, is the dryer humidity, is the drying time of the dryer, is the fourth fitting coefficient, is the fifth fitting coefficient.

[0034] In this embodiment, the innovative feature of this moisture content adjustment model lies in overcoming the limitations of "isolated single-parameter adjustment" to achieve multi-parameter coupled modeling. Existing techniques often use a single-parameter trial-and-error approach to adjust dryer parameters (e.g., reducing moisture content by simply increasing temperature, or accelerating drying by simply reducing humidity). This approach ignores the synergistic effects of temperature, wind speed, and humidity (for example, the drying effect of high temperature + low wind speed does not equal high temperature + high wind speed, as wind speed affects the surface moisture evaporation rate, thereby altering the temperature-driven internal diffusion efficiency). This formula, however, integrates the influencing factors of each parameter through a multiplication process (temperature × wind speed × humidity), accurately capturing the coupled mechanism of "temperature promoting internal moisture diffusion, wind speed enhancing surface evaporation, and humidity determining the driving force for mass transfer." This allows the adjustment of multiple parameters to achieve a synergistic effect of "1+1>2." This multi-parameter coupling modeling method solves the problems of mutual interference and low precision in parameter adjustment in traditional methods (for example, avoiding the phenomenon of "dry outside and wet inside" where only heating causes excessive drying of the surface while the internal moisture content still exceeds the standard). In addition, the integration of "mechanism derivation and data calibration" takes into account both theoretical rigor and production practicality. Traditional drying models have two types of defects: pure mechanism models (such as the diffusion equation based on Fick's law): only consider the physical mass transfer process, and do not combine the differences in material batches in actual production (such as bamboo density, initial moisture content fluctuations), resulting in large deviations between theory and practice; pure empirical formulas (such as "moisture content = initial value - temperature × time × coefficient"): rely on a large amount of experimental data fitting, lack physical meaning support, and change the base material (such as from pine to bamboo) The formula is completely invalid after the drying process is completed (for example, wood / bamboo). This formula innovatively adopts a hybrid modeling approach of "mechanism framework + data calibration": the basic framework is based on the core mechanism of drying dynamics (Fick's second law describes moisture diffusion, and the Arrhenius equation describes the influence of temperature on the diffusion coefficient) to ensure theoretical rigor; through fitting and optimization of actual production data (1000+ batches), the model can adapt to production scenarios of different substrates (wood / bamboo) and different seasons (fluctuations in ambient humidity). This fusion approach avoids the "ideal assumption bias" of pure mechanism models and solves the "scenario limitations" of pure empirical formulas. The prediction accuracy (±0.5%) in the specific scenario of container bottom plates (bamboo-wood composite materials) far exceeds that of existing technologies (usually more than ±2%). is the first model constant, is the second model constant, is the third model constant, is the first fitting coefficient, is the second fitting coefficient, is the third fitting coefficient, is the fourth fitting coefficient, The fifth fitting coefficient is obtained by fitting the data through multiple experiments.

[0035] In one embodiment, when it is monitored that the bonding strength of the current plate material of the container bottom plate is less than the preset bonding strength;

[0036] Input the current bonding strength of the board into a preset bonding strength adjustment model to obtain glue application adjustment parameter data; wherein the glue application adjustment parameter data includes the glue application amount of the glue application machine and the thickness of the glue layer;

[0037] Based on the gluing adjustment parameter data, the gluing machine of the container bottom plate is controlled.

[0038] In this embodiment, when the bond strength of the container floor panel is detected to be below a preset bond strength (e.g., ≥1.2 MPa, in compliance with the GB / T17657 standard), a dynamic gluing parameter adjustment process is immediately triggered. This current bond strength data is then fed into a pre-set bond strength adjustment model. This model, trained on thousands of production batches, integrates the adhesive wetting mechanism and accurately maps the relationship between bond strength and gluing parameters. The model outputs the gluing adjustment parameters, including the glue application rate (corrected value within the range of 150-250 g / m²) and the glue layer thickness (optimized value within the range of 30-100 μm). If insufficient glue application results in insufficient strength, the glue application rate is increased to increase the wetting area. If the curing effect is affected by an excessively thin or thick glue layer, the glue layer thickness is precisely adjusted to the optimal range of approximately 60 μm. Based on these parameters, the system controls the gluing machine in real time, effectively stabilizing the bond strength within the acceptable range, significantly reducing the risk of interlaminar delamination and improving the structural stability of the floor panel.

[0039] In one embodiment, the preset bonding strength adjustment model is constructed by the following formula:

[0040] ;

[0041] Where, is the glue coating amount of the glue coating machine, is the thickness of the adhesive layer, is the optimal glue layer thickness, is the target bonding strength, is the sixth fitting coefficient, is the seventh fitting coefficient, is the base value per unit area, is the solid content of the adhesive, is the eighth fitting coefficient, is the ninth fitting coefficient, is the bonding strength of the current board, is the minimum effective adhesive layer thickness.

[0042] The key to this embodiment lies in its precise reverse calculation from bond strength to adhesive application parameters, fundamentally changing the traditional empirically-based adjustment method. This is embodied in three key breakthroughs: First, it establishes for the first time a quantitative closed-loop process: "Insufficient strength → Parameter adjustment." In traditional production, when insufficient bond strength is detected, workers rely solely on intuition to increase the amount of glue applied or adjust the thickness of the adhesive layer. This often results in either too little (still failing to meet strength standards) or too much (causing cracking or costly waste). This formula, however, directly substitutes the current strength value (e.g., 1.0 MPa) and the target value (e.g., 1.2 MPa) into the calculation, instantly determining the required adhesive application amount from 180 g / m² to 210 g / m² and the adhesive layer thickness from 50 μm to 60 μm. This eliminates the need for trial and error, and allows for adjustment accuracy within ±5 g / m² and ±2 μm, respectively. Second, it resolves the problem of interference between adhesive layer thickness and application amount. Too thick a layer of glue can cause cracking after curing, while too much glue can make the layer too thick. Adjusting these two parameters independently can easily compromise one over the other. The formula uses two linked equations to ensure that the adjusted glue amount precisely matches the glue layer thickness. For example, the glue layer thickness is first set at the optimal 60μm (neither too thin to adhere firmly nor too thick to crack). The amount of glue required to achieve this thickness while ensuring strength is then calculated, allowing the two parameters to work together rather than interfere with each other. Third, it is specifically optimized for the high-strength requirements of container floors. Container floors must withstand several tons of cargo and the vibrations of long-distance transportation. Unprecedented bonding strength is crucial. Errors in traditional adjustment methods (e.g., a 10μm difference in glue layer thickness) can lead to delamination during transportation. This formula sets a minimum glue layer thickness of 30μm (to ensure coverage of micropores on the wood surface) and an optimal thickness of 60μm (to minimize stress after curing), precisely tailored to the characteristics of bamboo-wood composite materials. This stabilizes the bond strength above 1.2MPa after adjustment, reducing the failure rate from 4% to below 1%. Simply put, this formula transforms guessing parameters based on experience into calculating them based on data. This precise and efficient formula perfectly addresses the core challenge of controlling bond strength in container floor production.

[0043] In one embodiment, the preset deep learning model is obtained by training a preset deep learning network using labeled historical compensation amounts.

[0044] In this embodiment, the training process of the preset deep learning model is as follows: the historical compensation amounts recorded in massive production are used as input samples, and the corresponding optimal thermal pressure adjustment parameters (pressure, temperature, time) are used as labels. The preset improved LSTM deep learning network is iteratively trained to finally obtain a model that can accurately fit the mapping relationship between the compensation amount and the adjustment parameters.

[0045] In one embodiment, the preset deep learning model is an LSTM model.

[0046] In this embodiment, the pre-defined deep learning model uses an LSTM (Long Short-Term Memory) model. This model is chosen for its ability to effectively capture long-term and short-term dependencies in time series data, particularly adapting to the dynamic relationship between hot-pressing parameters and bending strength compensation. By learning the temporal characteristics of how pressure, temperature, and time change with compensation in historical production, it accurately outputs the appropriate hot-pressing adjustment parameters, significantly improving the timeliness and accuracy of parameter prediction.

[0047] See attached Figure 2 , Figure 2 This is a schematic diagram of the structure of an intelligent monitoring system for the production process of a container bottom plate according to the embodiment of the present application. Figure 2 As shown, the intelligent monitoring system for the production process of container bottom plates in the embodiment of the present invention mainly includes:

[0048] The first acquisition module 200 is configured to determine a compensation amount for the bending strength based on the bending strength of the current plate and the bending strength of the preset plate when the bending strength of the current plate of the container bottom plate is monitored to be less than the bending strength of the preset plate;

[0049] The first data processing module 202 is configured to input the compensation amount into a preset deep learning model to obtain hot pressing forming adjustment parameter data of the hot pressing forming equipment; wherein the hot pressing forming adjustment parameter data includes hot pressing pressure, hot pressing temperature, and hot pressing time;

[0050] The second data processing module 204 is configured to control the hot pressing forming equipment of the container bottom plate based on the hot pressing forming adjustment parameter data.

[0051] In one embodiment of the present invention, the system further includes a third data processing module, and the third data processing module is configured to perform the following operations:

[0052] When it is monitored that the moisture content of the current plate of the container bottom plate is less than the preset first moisture content or greater than the preset second preset moisture content;

[0053] Inputting the moisture content of the current plate into a preset moisture content adjustment model to obtain dryer adjustment parameter data; wherein the dryer adjustment parameter data includes dryer temperature, dryer wind speed, dryer drying time, and dryer humidity;

[0054] The dryer of the container bottom plate is controlled based on the dryer adjustment parameter data.

[0055] In one embodiment of the present invention, the preset moisture content adjustment model is constructed by the following formula:

[0056]

[0057] Where, is the moisture content of the current board, To balance the moisture content, is the target moisture content, is the first model constant, is the second model constant, is the dryer temperature, is the third model constant, is the first fitting coefficient, is the second fitting coefficient, is the dryer wind speed, is the third fitting coefficient, is the saturated humidity at drying temperature, is the dryer humidity, is the drying time of the dryer, is the fourth fitting coefficient, is the fifth fitting coefficient.

[0058] In one embodiment of the present invention, the system further includes a fourth data processing module, and the fourth data processing module is configured to perform the following operations:

[0059] When it is monitored that the current bonding strength of the container bottom plate is less than the preset bonding strength;

[0060] Inputting the current bonding strength of the board into a preset bonding strength adjustment model to obtain glue application adjustment parameter data; wherein the glue application adjustment parameter data includes the glue application amount of the glue application machine and the thickness of the glue layer;

[0061] Based on the gluing adjustment parameter data, the gluing machine of the container bottom plate is controlled.

[0062] In one embodiment of the present invention, the preset bonding strength adjustment model is constructed by the following formula:

[0063]

[0064] Where, is the glue coating amount of the glue coating machine, is the thickness of the adhesive layer, is the optimal adhesive layer thickness, is the target bonding strength, is the sixth fitting coefficient, is the seventh fitting coefficient, is the base value per unit area, is the solid content of the adhesive, is the eighth fitting coefficient, is the ninth fitting coefficient, is the bonding strength of the current board, is the minimum effective adhesive layer thickness.

[0065] In one embodiment of the present invention, the preset deep learning model is obtained by training a preset deep learning network using historical compensation amounts with labels.

[0066] In one embodiment of the present invention, the preset deep learning model is an LSTM model.

[0067] The steps of the various methods above are divided only for the purpose of clear description. When implemented, they can be combined into one step or some steps can be split and decomposed into multiple steps. As long as they include the same logical relationship, they are within the scope of protection of the present invention; adding insignificant modifications or introducing insignificant designs to the algorithm or process without changing the core design of the algorithm and process are all within the scope of protection of this application.

[0068] It is not difficult to find that this embodiment is a method embodiment corresponding to the system embodiment, and this embodiment can be implemented in conjunction with the system embodiment. The relevant technical details mentioned in the system embodiment are still valid in this embodiment, and to reduce repetition, they are not repeated here. Accordingly, the relevant technical details mentioned in this embodiment can also be applied to the system embodiment.

[0069] It is worth mentioning that all modules involved in this embodiment are logical modules. In actual applications, a logical unit can be a physical unit, a part of a physical unit, or a combination of multiple physical units. In addition, to highlight the innovation of this application, this embodiment does not include units that are not closely related to solving the technical problems proposed by this application. However, this does not mean that other units do not exist in this embodiment.

[0070] In addition, some embodiments of the present application further provide an electronic device. The electronic device may be various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, etc. The electronic device may also be various forms of mobile devices, such as personal digital assistants, cellular phones, smartphones, wearable devices, and other similar computing devices.

[0071] The electronic device includes: one or more processors; and a memory storing computer program instructions, wherein the computer program instructions, when executed, enable the processor to perform the steps of the method provided in any one or more of the above embodiments. Figure 4 An exemplary structural diagram of the electronic device is disclosed. Figure 3 As shown, the electronic device includes: one or more processors 1101, memory 1102, and interfaces for connecting various components, including high-speed and low-speed interfaces. The various components are interconnected using different buses and can be mounted on a common motherboard or in other ways as needed. The processor can process instructions executed within the electronic device, including instructions stored in or on the memory for displaying graphical information of a GUI on an external input / output device (such as a display device coupled to the interface). In some other embodiments, if desired, multiple processors and / or multiple buses can be used with multiple memories and multiple storage devices. Similarly, multiple electronic devices can be connected, with each device providing some of the necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system). The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present application described and / or claimed herein.

[0072] The electronic device may further include: an input device 1103 and an output device 1104. The processor 1101, the memory 1102, the input device 1103 and the output device 1104 may be connected via a bus or other means. Figure 4 The bus connection is taken as an example.

[0073] Input device 1103 can receive input digital or character information and generate key signal input related to user settings and function control of the electronic device. Examples include a touch screen, keypad, mouse, trackpad, touchpad, pointing stick, one or more mouse buttons, trackball, joystick, and other input devices. Output device 1104 may include a display device, auxiliary lighting devices (e.g., LEDs), and tactile feedback devices (e.g., vibration motors). Display devices may include, but are not limited to, liquid crystal displays (LCDs), light-emitting diode (LED) displays, and plasma displays. In some embodiments, the display device may be a touch screen.

[0074] To provide user interaction, the electronic device may be a computer. The computer includes a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user, as well as a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices may also be used to provide user interaction; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback), and input from the user may be received in any form, including acoustic input, voice input, or tactile input.

[0075] In the embodiments of the present application, a computer program / instruction is stored on a computer-readable medium. When executed by a processor, the computer program / instruction implements the steps of the method provided in any one or more of the above embodiments. The computer-readable medium may be included in the electronic device described in the above embodiments, or it may exist independently and not be incorporated into the device. The computer-readable medium carries one or more computer-readable instructions.

[0076] The memory 1102 can be used as a non-transitory computer-readable storage medium to store non-transitory software programs, non-transitory computer executable programs, and modules. The processor 1101 executes the non-transitory software programs, instructions, and modules stored in the memory 1102 to execute various functional applications and data processing of the server, thereby implementing the program instructions / modules corresponding to the method provided in any one or more of the above embodiments of the present application.

[0077] The memory 1102 may include a program storage area and a data storage area, wherein the program storage area may store an operating system and applications required for at least one function; the data storage area may store data created based on the use of the electronic device, etc. In addition, the memory 1102 may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory 1102 may optionally include a memory remotely located relative to the processor 1101, and these remote memories may be connected to the electronic device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0078] It should be noted that the computer-readable medium described in this application may be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. Computer-readable media may include, for example, but not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or components, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to, an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In this application, a computer-readable medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, device, or component.

[0079] Computer-readable media include both permanent and non-permanent, removable and non-removable media, and can be implemented by any method or technology for information storage. Information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase-change RAM (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc-read only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic tape, disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device.

[0080] Computer program code for performing the operations of the present application may be written in one or more programming languages, or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0081] In the above embodiments, all or part of the embodiments may be implemented using software, hardware, firmware, or any combination thereof. For example, implementation may be achieved using an application-specific integrated circuit (ASIC), a general-purpose computer, or any other similar hardware device. In some embodiments, the software program of the present application may be executed by a processor to implement the above steps or functions. Similarly, the software program of the present application (including related data structures) may be stored in a computer-readable recording medium, such as a RAM memory, a magnetic or optical drive, a floppy disk, or the like. In addition, some steps or functions of the present application may be implemented using hardware, for example, as a circuit that cooperates with a processor to perform the various steps or functions.

[0082] The computer program product provided in the embodiments of the present application includes one or more computer programs / instructions that, when executed by a processor, fully or partially produce the processes or functions described in the embodiments of the present application. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via a wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) method. The computer-readable storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server or data center that includes one or more available media. The available medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state drive (SSD)).

[0083] The flowcharts or block diagrams in the accompanying drawings illustrate the possible architectures, functions and operations of the devices, methods and computer program products according to various embodiments of the present application. In this regard, each box in the flowchart or block diagram can represent a module, program segment or part of code, and the module, program segment or part of code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in an order different from that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, as well as the combination of boxes in the block diagram and / or flowchart, can be implemented with a dedicated hardware-specific system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0084] The scope of this application is defined by the appended claims rather than the foregoing description and is therefore intended to encompass within this application all changes that come within the meaning and range of equivalents of the claims. Any reference signs in the claims should not be construed as limiting the claims to which they relate. In addition, it is clear that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or devices stated in a device claim may also be implemented by one unit or device through software or hardware. Words such as "first" and "second" are only used to distinguish the description and do not indicate any particular order, nor should they be understood as indicating or implying relative importance.

[0085] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any person skilled in the art may easily propose variations or substitutions within the technical scope disclosed in the present application, and such variations or substitutions shall be encompassed within the scope of protection of the present application. Therefore, the scope of protection of the present application shall be subject to the scope of protection of the claims, and the above embodiments shall be regarded as exemplary and non-limiting.

Claims

1. A method for intelligently monitoring the production process of container bottom plates, characterized in that: The method comprises: When it is monitored that the bending strength of the current plate of the container bottom plate is less than the bending strength of the preset plate, a compensation amount for the bending strength is determined based on the bending strength of the current plate and the bending strength of the preset plate; Inputting the compensation amount into a preset deep learning model to obtain hot pressing forming adjustment parameter data of the hot pressing forming equipment; wherein the hot pressing forming adjustment parameter data includes hot pressing machine pressure, hot pressing temperature, and hot pressing time; Controlling the hot pressing forming equipment for the container bottom plate based on the hot pressing forming adjustment parameter data; When it is monitored that the moisture content of the current plate of the container bottom plate is less than the preset first moisture content or greater than the preset second preset moisture content; Inputting the moisture content of the current plate into a preset moisture content adjustment model to obtain dryer adjustment parameter data; wherein the dryer adjustment parameter data includes dryer temperature, dryer wind speed, dryer drying time, and dryer humidity; Controlling the dryer of the container floor based on the dryer adjustment parameter data; The preset moisture content adjustment model is constructed by the following formula: ； Where, is the moisture content of the current board, To balance the moisture content, is the target moisture content, is the first model constant, is the second model constant, is the dryer temperature, is the third model constant, is the first fitting coefficient, b is the second fitting coefficient, is the dryer wind speed, c is the third fitting coefficient, is the saturated humidity at drying temperature, is the dryer humidity, is the drying time of the dryer, is the fourth fitting coefficient, is the fifth fitting coefficient.

2. The method according to claim 1, characterized in that The method further comprises: When it is monitored that the current bonding strength of the container bottom plate is less than the preset bonding strength; Inputting the current bonding strength of the board into a preset bonding strength adjustment model to obtain glue application adjustment parameter data; wherein the glue application adjustment parameter data includes the glue application amount of the glue application machine and the thickness of the glue layer; Based on the gluing adjustment parameter data, the gluing machine of the container bottom plate is controlled.

3. The method according to claim 2, characterized in that The preset bonding strength adjustment model is constructed by the following formula: ； Where, is the glue coating amount of the glue coating machine, is the thickness of the adhesive layer, is the optimal adhesive layer thickness, is the target bonding strength, is the sixth fitting coefficient, is the seventh fitting coefficient, is the base value per unit area, is the solid content of the adhesive, is the eighth fitting coefficient, is the ninth fitting coefficient, is the bonding strength of the current board, is the minimum effective adhesive layer thickness.

4. The method according to claim 1, wherein Also includes: The preset deep learning model is obtained by training a preset deep learning network using historical compensation amounts with labels.

5. The method according to claim 4, characterized in that Also includes: The preset deep learning model is an LSTM model.

6. An intelligent monitoring system for the production process of container bottom plates, characterized in that: Applied to the method according to any one of claims 1 to 5, the system comprising: The first acquisition module is configured to determine a compensation amount for the bending strength based on the bending strength of the current plate and the bending strength of the preset plate when the bending strength of the current plate of the container bottom plate is monitored to be less than the bending strength of the preset plate; a first data processing module configured to input the compensation amount into a preset deep learning model to obtain hot pressing forming adjustment parameter data of the hot pressing forming equipment; wherein the hot pressing forming adjustment parameter data includes hot pressing pressure, hot pressing temperature, and hot pressing time; The second data processing module is configured to control the hot pressing forming equipment of the container bottom plate based on the hot pressing forming adjustment parameter data.

7. An electronic device, characterized in that: The device comprises: one or more processors; and A memory storing computer program instructions, wherein when the computer program instructions are executed, the processor is caused to perform the method according to any one of claims 1 to 5.

8. A computer-readable medium, characterized in that Computer program instructions are stored thereon, and the computer program instructions can be executed by a processor to implement the method according to any one of claims 1 to 5.

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