A lapping apparatus

Abstract

The application discloses a kind of grinding processing equipment, which includes three-roll grinding machine, high platform and receiving barrel, the three-roll grinding machine includes controller and along the direction of pigment raw material flow First roller, second roller and third roller are arranged;The drive device of first roller, second roller and third roller is connected to the controller;The high platform is arranged below the three-roll grinding machine, for the height of the three-roll grinding machine is promoted;The receiving barrel is arranged in the side of the high platform and below the discharge port of the three-roll grinding machine, for receiving the pigment raw material that the three-roll grinding machine is ground and storage is completed.It can be seen that the application can effectively improve the efficiency and effect of pigment raw material grinding, reduce labor cost, and fully realize intelligent manufacturing.

Description

Technical Field

[0001] This invention relates to the field of grinding technology, and more particularly to a grinding equipment. Background Technology

[0002] In the field of pigment processing, grinding equipment is a crucial component, enabling the thorough grinding and mixing of various pigments for subsequent processing. Using roller mills for grinding is a common practice. However, existing roller mill grinding technologies rarely consider adjusting the height of the grinding equipment to ensure smoother material discharge, and the controllers generally rely on fixed operating modes, lacking more flexible control methods. Therefore, it is clear that existing technologies have shortcomings and urgently need improvement. Summary of the Invention

[0003] The technical problem to be solved by the present invention is to provide a grinding and processing equipment that can effectively improve the efficiency and effect of pigment raw material grinding, reduce labor costs, and fully realize intelligent manufacturing.

[0004] To address the aforementioned technical problems, this invention discloses a grinding processing apparatus, comprising:

[0005] A three-roll mill includes a controller and a first roller, a second roller, and a third roller arranged along the flow direction of the pigment raw material; the drive devices for the first roller, the second roller, and the third roller are all connected to the controller;

[0006] A heightening platform is provided below the three-roll mill for raising the three-roll mill to a higher level.

[0007] A receiving hopper is located on one side of the raised platform and below the discharge port of the three-roll mill, and is used to receive and store the pigment raw materials that have been ground by the three-roll mill.

[0008] In one optional embodiment, the driving device includes a roller movement driving device and a roller rotation driving device; a first image sensor is provided at the feed inlet of the three-roll mill, and a second image sensor is provided at the discharge outlet of the three-roll mill; a weight sensor is provided at the bottom of the receiving hopper; the first image sensor, the second image sensor, and the weight sensor are all communicatively connected to the controller.

[0009] In an optional implementation, the controller is provided with computer code, which, when executed, causes the controller to perform the following operations:

[0010] Acquire the real-time feeding image obtained by the first image sensor;

[0011] The feeding category corresponding to the real-time feeding image is determined according to a preset image recognition algorithm;

[0012] Based on the feed category, the driving parameters corresponding to the feed category are determined in a preset database of correspondences between categories and driving parameters;

[0013] The driving parameters are sent to the driving device to control the operation of the first roller, the second roller, and the third roller.

[0014] In an optional implementation, the controller determines the specific method of the feeding category corresponding to the real-time feeding image according to a preset image recognition algorithm, including:

[0015] The real-time feed image is input into a pre-trained flow rate prediction neural network model to obtain the first predicted flow rate corresponding to the real-time feed image; the flow rate prediction neural network model is trained using a training dataset that includes multiple training feed images and corresponding flow rate labels.

[0016] Based on a color recognition algorithm, the color information corresponding to the real-time feeding image is determined;

[0017] Obtain the pigment production plan corresponding to the processing area of ​​the grinding equipment;

[0018] Based on the first predicted flow rate, the color information, and the pigment production plan, the feed category corresponding to the real-time feed image is determined.

[0019] In an optional implementation, the controller determines the specific method for determining the feed category corresponding to the real-time feed image based on the first predicted flow rate, the color information, and the pigment production plan, including:

[0020] Based on the first predicted flow rate and the color information, multiple candidate pigment raw material categories that meet the requirements are determined from a preset database of correspondences between flow rate and color and pigment category.

[0021] Based on the required pigment intermediate product information in the pigment production plan and the preset correspondence between pigment intermediate products and pigment raw materials, determine the pigment raw material information corresponding to the required pigment intermediate product information;

[0022] Calculate the intersection information between the multiple candidate pigment raw material categories and the pigment raw material information to obtain the feed category corresponding to the real-time feed image.

[0023] In one optional embodiment, the driving parameters include roller speed parameters and roller movement parameters; the roller speed parameters include a first roller speed for controlling the first roller, a second roller speed for controlling the second roller, and a third roller speed for controlling the third roller; the roller movement parameters include a first roller movement parameter for controlling the first roller, a second roller movement parameter for controlling the second roller, and a third roller movement parameter for controlling the third roller.

[0024] Furthermore, the specific method by which the controller determines the driving parameters corresponding to the feed category based on the feed category in a preset database of correspondences between categories and driving parameters includes:

[0025] The feed category is input into the trained driving parameter prediction neural network model to obtain the driving parameters corresponding to the feed category; wherein, the driving parameter prediction neural network model is trained on a training dataset that includes multiple training feed information and corresponding driving parameter annotations with the best grinding effect.

[0026] In an optional implementation, when the computer code is executed, the controller also performs the following operations:

[0027] Acquire real-time discharge images and real-time weight information obtained by the second image sensor and the weight sensor in the same time period;

[0028] The real-time discharge image is input into the flow rate prediction neural network model to obtain the second predicted flow rate corresponding to the real-time discharge image;

[0029] The real-time discharge image is input into a trained particle size prediction neural network model to obtain multiple particle size prediction values ​​corresponding to the real-time discharge image; the particle size prediction neural network model is trained using a training dataset that includes multiple training fluid images and corresponding particle size labels.

[0030] Calculate the average value of the multiple particle prediction values ​​to obtain the average particle size information corresponding to the real-time discharge image;

[0031] Based on the real-time weight information and the feed category, and based on a preset database of correspondences between weight, category, and normal range, the normal flow rate reference value and normal particle size reference value corresponding to the real-time weight information and the feed category are determined.

[0032] Based on the second predicted flow rate and the average particle size information, as well as the normal flow rate reference value and the normal particle size reference value, the adjusted new drive parameters corresponding to the three-roll mill are determined.

[0033] In one optional implementation, the controller determines the adjusted new drive parameters corresponding to the three-roll mill based on the second predicted flow rate and the average particle size information, as well as the normal flow rate reference value and the normal particle size reference value, in the following specific ways:

[0034] Calculate the velocity difference between the second predicted velocity and the normal velocity reference value;

[0035] Calculate the particle size difference between the average particle size information and the normal particle size reference value;

[0036] Determine whether the flow velocity difference is greater than a preset flow velocity difference threshold and whether the particle size difference is greater than a preset particle size difference threshold to obtain a first determination result;

[0037] If the first judgment result is negative, it is confirmed that there is no need to adjust the drive parameters of the three-roll mill;

[0038] If the first judgment result is yes, then the adjusted new drive parameters corresponding to the three-roll mill are determined based on the flow rate difference and the particle size difference.

[0039] In one optional implementation, the controller determines the adjusted new drive parameters for the three-roll mill based on the flow rate difference and the particle size difference in the following ways:

[0040] Based on the flow velocity difference, and based on the preset first correspondence between the flow velocity difference and the increased rotation speed value and the increased inter-roller pressure, a first set of increased rotation speed values ​​corresponding to the roller rotation speed parameters and a set of first roller adjustment movement values ​​corresponding to the roller movement parameters are determined.

[0041] Based on the particle size difference, and based on the preset second correspondence between the particle size difference and the increased rotation speed and increased inter-roller pressure, a set of second rotation speed increase values ​​corresponding to the roller rotation speed parameters and a set of second roller adjustment movement values ​​corresponding to the roller movement parameters are determined.

[0042] The higher of the first and second speed increase values ​​is determined as the target speed increase value corresponding to the roller speed parameter. The sum of the roller speed parameter and the target speed increase value is determined as the adjusted new roller speed parameter corresponding to the three-roll mill.

[0043] The union of the first roller adjustment movement value set and the second roller adjustment movement value set is determined as the target movement adjustment set corresponding to the roller movement parameter. The calculation result of the roller movement parameter and the target movement adjustment set is determined as the new adjusted roller movement parameter corresponding to the three-roll mill.

[0044] In an optional embodiment, the three-roll mill further includes a cooling device; the cooling device is used to output coolant to cooling pipes disposed inside the first, second, and third rolls to cool the first, second, and third rolls; the cooling device is communicatively connected to the controller;

[0045] Furthermore, when the computer code is executed, the controller also performs the following operations:

[0046] When the first determination result is yes, the temperature of the coolant output by the cooling device is reduced.

[0047] Compared with the prior art, the present invention has the following beneficial effects:

[0048] This invention enables the controller of a three-roll mill to control the three rollers, achieving more refined and thorough grinding of pigment raw materials. Furthermore, the raised platform supports the mill, allowing the mill's discharge port to directly face the receiving hopper for material storage. This effectively improves the efficiency and effect of pigment raw material grinding, reduces labor costs, and fully realizes intelligent manufacturing. Attached Figure Description

[0049] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0050] Figure 1 This is a schematic diagram of the structure of a grinding processing equipment disclosed in an embodiment of the present invention. Detailed Implementation

[0051] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0052] The terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this invention are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, apparatus, product, or end that includes a series of steps or modules is not limited to the listed steps or modules, but may optionally include steps or modules not listed, or may optionally include other steps or modules inherent to these processes, methods, products, or ends.

[0053] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0054] For details, please refer to Figure 1 , Figure 1 This is a schematic diagram of the structure of a grinding and processing equipment disclosed in an embodiment of the present invention. Figure 1 As shown, the grinding and processing equipment includes a three-roll mill 10, a raised platform 20 and a receiving hopper 30. The three-roll mill 10 includes a controller (not shown in the figure) and a first roller 101, a second roller 102 and a third roller 103 arranged along the flow direction of the pigment raw material.

[0055] Specifically, the drive devices for the first roller 101, the second roller 102, and the third roller 103 are all connected to the controller to receive drive parameters sent by the controller and implement drive according to the drive parameters.

[0056] Specifically, the lifting platform 20 is located below the three-roll mill 10 to raise the height of the three-roll mill 10. The receiving hopper 30 is located on one side of the lifting platform 20 and below the discharge port of the three-roll mill 10 to receive and store the pigment raw materials after grinding by the three-roll mill 10.

[0057] The aforementioned equipment enables the controller of the three-roll mill to control the three rollers, achieving more refined and thorough grinding of pigment raw materials. Furthermore, the raised platform supports the mill, allowing the mill's discharge port to directly face the receiving hopper for material storage. This effectively improves the efficiency and effect of pigment raw material grinding, reduces labor costs, and fully realizes intelligent manufacturing.

[0058] In an optional embodiment, the driving device includes a roller movement driving device and a roller rotation driving device. The roller movement driving device can be used to receive driving parameters to control the movement of the roller in three dimensions. The roller movement driving device can be implemented by a slide rail structure. The roller rotation driving device can be a controller or control circuit connected to the drive motor of the roller. It can receive driving parameters to control the rotational speed of the roller.

[0059] Specifically, the feed inlet of the three-roll mill 10 is equipped with a first image sensor, and the discharge outlet of the three-roll mill 10 is equipped with a second image sensor. These two image sensors can be used to acquire images of the feed and discharge of the mill in real time for algorithm recognition and control in subsequent embodiments.

[0060] Specifically, a weight sensor is installed at the bottom of the receiving hopper 30, which can be used to detect the weight of the pigment raw materials in the receiving hopper 30 in real time. Optionally, the weight sensor is also connected to an alarm. When the weight detected in real time exceeds a preset weight threshold, the alarm will be triggered to notify the staff to recycle the receiving hopper.

[0061] Specifically, the first image sensor, the second image sensor, and the weight sensor are all communicatively connected to the controller to transmit the detected data to the controller for subsequent algorithm processing.

[0062] Through the above embodiments, multiple intelligent sensors are set at different parts of the grinding and processing equipment, which can effectively improve the intelligence level of the entire grinding and processing equipment. At the same time, the data acquired by multiple sensors can also help with the subsequent control and analysis of the grinding and processing equipment.

[0063] In an optional embodiment, the controller is provided with computer code, which, when executed, causes the controller to perform the following operations:

[0064] Acquire real-time feed images from the first image sensor;

[0065] The feeding category corresponding to the real-time feeding image is determined based on the preset image recognition algorithm;

[0066] Based on the feed category, determine the corresponding drive parameters in the preset database of correspondence between categories and drive parameters;

[0067] The drive parameters are sent to the drive unit to control the operation of the first roller, the second roller, and the third roller.

[0068] Optionally, the real-time feed image may include multiple consecutively acquired image frames.

[0069] Optionally, the preset database of correspondences between categories and drive parameters can be the correspondence between the optimal grinding drive parameters for multiple different feed categories, determined by operators based on historical data and expert analysis. For example, this correspondence can be determined using analytical and statistical methods such as principal component analysis.

[0070] Through the above embodiments, the feeding category corresponding to the real-time feeding image can be determined by the image recognition algorithm, and the driving parameters corresponding to the feeding category can be determined in the preset correspondence database of categories and driving parameters. This enables intelligent control of the roller driving process through image recognition, thereby achieving more intelligent control of pigment raw material grinding and processing.

[0071] In an optional embodiment, the controller determines the specific method of the feeding category corresponding to the real-time feeding image according to a preset image recognition algorithm, including:

[0072] The real-time feed image is input into a pre-trained flow rate prediction neural network model to obtain the first predicted flow rate corresponding to the real-time feed image; the flow rate prediction neural network model is trained using a training dataset that includes multiple training feed images and corresponding flow rate labels.

[0073] Based on a color recognition algorithm, the color information corresponding to the real-time feeding image is determined;

[0074] Obtain the pigment production plan corresponding to the processing area of ​​the grinding equipment;

[0075] Based on the first predicted flow rate, color information, and pigment production plan, the feed category corresponding to the real-time feed image is determined.

[0076] Optionally, the neural network model in this invention can be a CNN structure, an RNN structure, or an LTSM structure neural network model, and is trained until convergence using a gradient descent algorithm and a corresponding loss function. Operators can select the appropriate algorithm structure and parameter details according to the specific data prediction scenario and data characteristics, and this invention does not impose any limitations.

[0077] Optionally, color recognition algorithms can obtain color information directly by identifying or capturing specific color attributes of an image, or they can be determined using neural network algorithms.

[0078] Optionally, the pigment production plan is used to define the pigment production plan in the processing area during the current time period. It can specifically include all processing flow information and raw material information in the processing area. This plan data can be obtained by communicating with the server or control host in the processing area.

[0079] Through the above embodiments, the first predicted flow rate and color information corresponding to the real-time feeding image can be determined by neural network algorithm and color recognition algorithm. Then, the feeding category corresponding to the real-time feeding image can be determined by comprehensively considering the first predicted flow rate, color information and pigment production plan. This enables more accurate identification of the category corresponding to the image, so as to realize intelligent control of roller drive processing through image recognition in the subsequent process, thereby achieving more intelligent control of pigment raw material grinding and processing.

[0080] In an optional embodiment, the controller determines the specific method by which it determines the feed category corresponding to the real-time feed image based on the first predicted flow rate, color information, and pigment production plan, including:

[0081] Based on the first predicted flow rate and color information, multiple candidate pigment raw material categories that match are determined from a pre-set database of correspondences between flow rate and color and pigment category.

[0082] Based on the required pigment intermediate product information in the pigment production plan and the pre-defined correspondence between pigment intermediate products and pigment raw materials, determine the pigment raw material information corresponding to the required pigment intermediate product information.

[0083] Calculate the intersection information between multiple candidate pigment raw material categories and pigment raw material information to obtain the feed category corresponding to the real-time feed image.

[0084] Optionally, the preset database of correspondences between flow rate and color and pigment category can be a mathematical correspondence obtained by fitting multiple historically collected data through a data fitting algorithm, a category correspondence obtained by a statistical analysis algorithm, or a neural network prediction model trained using historically collected data as training data.

[0085] Optionally, the pre-defined correspondence between pigment intermediates and pigment raw materials can be formulated by operators based on experience, rules, or production manuals.

[0086] Through the above embodiments, multiple candidate pigment raw material categories can be identified by using a preset database of correspondences between flow rate and color and pigment category. Then, the pigment raw material information corresponding to the production plan can be determined based on the required pigment intermediate product information in the pigment production plan. Subsequently, the corresponding feed category can be obtained through intersection calculation. This allows for a more accurate determination of the feed category by combining the pigment production plan and the feed flow rate and color. This enables intelligent control of the roller drive processing through image recognition in the subsequent process, thereby achieving more intelligent control of pigment raw material grinding and processing.

[0087] In an optional embodiment, the driving parameters include roller speed parameters and roller movement parameters. Specifically, the roller speed parameters include a first roller speed for controlling the first roller, a second roller speed for controlling the second roller, and a third roller speed for controlling the third roller. Specifically, the roller movement parameters include a first roller movement parameter for controlling the first roller, a second roller movement parameter for controlling the second roller, and a third roller movement parameter for controlling the third roller.

[0088] In an optional embodiment, the controller determines the specific method for determining the driving parameters corresponding to the feed category based on a preset correspondence database of categories and driving parameters, including:

[0089] The feed category is input into the trained driving parameter prediction neural network model to obtain the driving parameters corresponding to the feed category; the driving parameter prediction neural network model is trained on a training dataset that includes multiple training feed information and corresponding optimal grinding effect driving parameter annotations.

[0090] Specifically, the optimal driving parameters for grinding can be obtained by operators through analysis and annotation of historical production data. For example, the historical output quality of the grinding equipment and the corresponding driving parameters during grinding can be recorded and analyzed to obtain the driving parameters at which the output quality is highest, and then annotated accordingly.

[0091] Through the above embodiments, by inputting the feed category into the trained driving parameter prediction neural network model, the driving parameters corresponding to the feed category can be obtained. This allows for a more accurate determination of the driving parameters corresponding to the feed category by combining the neural network algorithm, enabling intelligent control of the roller's driving process in the subsequent process, thus achieving more intelligent control of pigment raw material grinding and processing.

[0092] In an optional embodiment, when the computer code is executed, the controller also performs the following operations:

[0093] Acquire real-time discharge images and real-time weight information obtained by the second image sensor and the weight sensor within the same time period;

[0094] The real-time discharge image is input into the flow rate prediction neural network model to obtain the second predicted flow rate corresponding to the real-time discharge image;

[0095] The real-time discharge image is input into the trained particle size prediction neural network model to obtain multiple particle size prediction values ​​corresponding to the real-time discharge image; the particle size prediction neural network model is trained using a training dataset that includes multiple training fluid images and corresponding particle size labels.

[0096] Calculate the average of multiple particle prediction values ​​to obtain the average particle size information corresponding to the real-time discharge image;

[0097] Based on real-time weight information and feed category, and using a pre-defined database of correspondences between weight, category, and normal range, the normal flow rate reference value and normal particle size reference value corresponding to the real-time weight information and feed category are determined.

[0098] Based on the second predicted flow rate and average particle size information, as well as the normal flow rate reference value and normal particle size reference value, the adjusted new drive parameters corresponding to the three-roll mill are determined.

[0099] Optionally, the real-time discharge image may include multiple continuously acquired image frames, which are input into a particle size prediction neural network model to obtain multiple particle size prediction values ​​corresponding to the multiple image frames.

[0100] Optionally, the training dataset for the particle size prediction neural network model can be obtained through experimental data, such as by acquiring images of grinding products of different particle sizes using an image acquisition device and labeling the corresponding particle size parameters to obtain the training dataset.

[0101] Optionally, a pre-defined database of correspondences between weight, category, and normal range can be obtained by operators through statistical analysis of historical data on the normal flow rate and normal particle size of raw materials of different categories corresponding to different grinding times. Different grinding times can be represented by different weight information because the grinding product increases with the increase of grinding time, and therefore the weight sensor information of the receiving hopper also increases. Thus, the weight sensor originally installed in the receiving hopper can be effectively used to effectively identify the grinding time, and further analysis can be performed to obtain a database of correspondences between weight, category, and normal range.

[0102] Specifically, the database of correspondences between weight, category, and normal range can be a mathematical correspondence obtained by fitting historical raw material data using a data fitting algorithm, a category correspondence obtained by analyzing historical raw material data using a statistical analysis algorithm, or a neural network prediction model trained using historical raw material data as training data.

[0103] Through the above embodiments, by analyzing and calculating the second predicted flow rate and average particle size information, as well as the normal flow rate reference value and normal particle size reference value, the adjusted new drive parameters corresponding to the three-roll mill can be determined. In this way, the drive parameters can be adjusted more accurately by combining the real-time discharge flow rate information and particle size information, so as to realize intelligent control of the roller drive processing based on the discharge monitoring, and achieve more intelligent control of pigment raw material grinding processing.

[0104] In an optional embodiment, the controller determines the specific method for adjusting the new drive parameters corresponding to the three-roll mill based on the second predicted flow rate and average particle size information, as well as the normal flow rate reference value and normal particle size reference value, including:

[0105] Calculate the velocity difference between the second predicted velocity and the normal velocity reference value;

[0106] Calculate the particle size difference between the average particle size information and the normal particle size reference value;

[0107] Determine whether the flow velocity difference is greater than a preset flow velocity difference threshold and whether the particle size difference is greater than a preset particle size difference threshold to obtain the first determination result;

[0108] If the first judgment result is negative, it is confirmed that there is no need to adjust the drive parameters of the three-roll mill;

[0109] If the first judgment result is yes, then the new adjusted drive parameters corresponding to the three-roll mill are determined based on the flow rate difference and particle size difference.

[0110] Through the above embodiments, it is possible to determine whether the output quality is seriously unsatisfactory by judging whether the flow rate difference is greater than the flow rate difference threshold and whether the particle size difference is greater than the particle size difference threshold. If so, the corresponding new adjusted drive parameters of the three-roll mill are determined. In this way, the drive parameters can be adjusted more accurately by combining the real-time output flow rate information and particle size information, so as to realize intelligent control of the roller drive processing based on the output monitoring, and realize more intelligent pigment raw material grinding and processing control.

[0111] In one optional embodiment, the controller determines the specific method for the adjusted new drive parameters corresponding to the three-roll mill based on the flow rate difference and particle size difference, including:

[0112] Based on the flow velocity difference, and based on the preset first correspondence between the flow velocity difference and the increased rotation speed value and the increased inter-roller pressure, the first set of increased rotation speed value corresponding to the roller rotation speed parameter and the first set of roller adjustment movement value corresponding to the roller movement parameter are determined.

[0113] Based on the particle size difference, and based on the preset second correspondence between the particle size difference and the increased rotation speed and increased inter-roller pressure, a set of second rotation speed increase values ​​corresponding to the roller rotation speed parameters and a set of second roller adjustment movement values ​​corresponding to the roller movement parameters are determined.

[0114] The higher of the first and second speed increase values ​​is determined as the target speed increase value corresponding to the roller speed parameter. The sum of the roller speed parameter and the target speed increase value is determined as the new adjusted roller speed parameter corresponding to the three-roll mill.

[0115] The union of the first roller adjustment movement value set and the second roller adjustment movement value set is determined as the target movement adjustment set corresponding to the roller movement parameters. The calculation results of the roller movement parameters and the target movement adjustment set are determined as the new adjusted roller movement parameters corresponding to the three-roll mill.

[0116] Specifically, the preset correspondence between the flow rate difference and the increased rotational speed and increased inter-roller pressure, and the preset correspondence between the particle size difference and the increased rotational speed and increased inter-roller pressure, both specifically define the correspondence between different flow rate differences or particle size differences and the rotational speed of a specific roller that needs to be increased, or the pressure between two specific rollers that needs to be increased. Furthermore, the pressure between two specific rollers that needs to be increased can correspond to the relative movement parameters between the two rollers, and can thus be converted into adjustment values ​​for the movement parameters of the two rollers. Therefore, by combining the first and second correspondences with the flow rate difference or particle size difference, the set of adjustment movement values ​​for the two rollers can be determined respectively.

[0117] Specifically, in the process of calculating the union of the first roller adjustment movement value set and the second roller adjustment movement value set, for the adjustment values ​​of the same roller in the same direction in the two sets, they can be superimposed or canceled (if the directions are opposite) to achieve the combined calculation of specific adjustment movement values.

[0118] Through the above embodiments, by using the preset correspondence between flow rate difference or particle size difference and the increase in rotational speed and the increase in inter-roller pressure, and by using the rule calculation of the set of rotational speed increase and roller adjustment movement values, it is possible to combine the real-time discharge flow rate information and particle size information to more accurately adjust the drive parameters, so as to realize intelligent control of roller drive processing based on discharge monitoring, and achieve more intelligent control of pigment raw material grinding and processing.

[0119] In an optional embodiment, the three-roll mill further includes a cooling device communicatively connected to a controller. This cooling device outputs coolant to cooling pipes located inside the first, second, and third rolls to cool them. The specific cooling module of the cooling device can control the temperature of the output coolant.

[0120] In an optional embodiment, when the computer code is executed, the controller also performs the following operations:

[0121] When the first judgment result is yes, the temperature of the coolant output by the control cooling equipment is reduced.

[0122] Specifically, the controller can send a cooling command to the cooling equipment. After receiving the command, the cooling equipment can control the refrigeration module to increase the cooling power to reduce the temperature of the output coolant.

[0123] Through the above embodiments, the controller can predictively increase the cooling power when the first judgment result is yes, that is, when it is determined that the output quality is poor and the drive parameters need to be adjusted and controlled. This is something that the applicant discovered in the process of actually implementing the present invention. Such calculation and control operations will greatly increase the working temperature of the roller. Therefore, it is necessary to predictively reduce the temperature of the roller to reduce the damage to the grinding equipment caused by high temperature.

[0124] The foregoing has described specific embodiments of this specification; other embodiments are within the scope of the appended claims. In some cases, the actions or steps described in the claims may be performed in a different order than those shown in the embodiments and may still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily have to follow the specific or sequential order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0125] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the embodiments of apparatus, devices, and non-volatile computer-readable storage media are basically similar to the method embodiments, so the descriptions are relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0126] The apparatus, device, non-volatile computer-readable storage medium and method provided in the embodiments of this specification are corresponding. Therefore, the apparatus, device and non-volatile computer storage medium also have similar beneficial technical effects as the corresponding method. Since the beneficial technical effects of the method have been described in detail above, the beneficial technical effects of the corresponding apparatus, device and non-volatile computer storage medium will not be repeated here.

[0127] In the 1990s, improvements to a technology could be clearly distinguished as either hardware improvements (e.g., improvements to the circuit structure of diodes, transistors, switches, etc.) or software improvements (improvements to the methodology). However, with technological advancements, many methodological improvements today can be considered direct improvements to the hardware circuit structure. Designers almost always obtain the corresponding hardware circuit structure by programming the improved methodology into the hardware circuit. Therefore, it cannot be said that a methodological improvement cannot be implemented using hardware physical modules. For example, a Programmable Logic Device (PLD) (such as a Field Programmable Gate Array (FPGA)) is such an integrated circuit whose logic function is determined by the user programming the device. Designers can program and "integrate" a digital system onto a PLD themselves, without needing chip manufacturers to design and manufacture dedicated integrated circuit chips. Furthermore, nowadays, instead of manually manufacturing integrated circuit chips, this programming is mostly implemented using "logic compiler" software. Similar to the software compiler used in program development, the original code before compilation must be written in a specific programming language, called a Hardware Description Language (HDL). There are many HDLs, such as ABEL (Advanced Boolean Expression Language), AHDL (Altera Hardware Description Language), Confluence, CUPL (Cornell University Programming Language), HDCal, JHDL (Java Hardware Description Language), Lava, Lola, MyHDL, PALASM, and RHDL (Ruby Hardware Description Language). Currently, the most commonly used are VHDL (Very-High-Speed ​​Integrated Circuit Hardware Description Language) and Verilog. Those skilled in the art should understand that by simply performing some logic programming on the method flow using one of these hardware description languages ​​and programming it into an integrated circuit, the hardware circuit implementing the logical method flow can be easily obtained.

[0128] The controller can be implemented in any suitable manner. For example, it can take the form of a microprocessor or processor and a computer-readable medium storing computer-readable program code (e.g., software or firmware) executable by the (micro)processor, logic gates, switches, application-specific integrated circuits (ASICs), programmable logic controllers, and embedded microcontrollers. Examples of controllers include, but are not limited to, the following microcontrollers: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20, and Silicon Labs C8051F320. A memory controller can also be implemented as part of the control logic of the memory. Those skilled in the art will also recognize that, in addition to implementing the controller in purely computer-readable program code form, the same functionality can be achieved by logically programming the method steps to make the controller take the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, and embedded microcontrollers. Therefore, such a controller can be considered a hardware component, and the means included therein for implementing various functions can also be considered as structures within the hardware component. Alternatively, the means for implementing various functions can be considered as both software modules implementing the method and structures within the hardware component.

[0129] The systems, devices, modules, or units described in the above embodiments can be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, a computer can be, for example, a personal computer, laptop computer, cellular phone, camera phone, smartphone, personal digital assistant, media player, navigation device, email device, game console, tablet computer, wearable device, or any combination of these devices.

[0130] For ease of description, the above devices are described in terms of function, divided into various units. Of course, in implementing this specification, the functions of each unit can be implemented in one or more software and / or hardware components.

[0131] Those skilled in the art will understand that the embodiments of this specification can be provided as methods, systems, or computer program products. Therefore, the embodiments of this specification can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the embodiments of this specification can take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0132] This specification is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this specification. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create a machine for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0133] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0134] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0135] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0136] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0137] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (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, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0138] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0139] This specification can be described in the general context of computer-executable instructions that are executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform a specific task or implement a specific abstract data type. This specification can also be practiced in distributed computing environments, where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0140] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the system embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments.

[0141] Finally, it should be noted that the grinding and processing equipment disclosed in the embodiments of the present invention is only a preferred embodiment of the present invention and is only used to illustrate the technical solutions of the present invention, not to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A grinding and processing equipment, characterized in that, include: A three-roll mill includes a controller and a first roller, a second roller, and a third roller arranged along the flow direction of the pigment raw material; the drive devices for the first roller, the second roller, and the third roller are all connected to the controller; A heightening platform is provided below the three-roll mill for raising the three-roll mill to a higher level. A receiving hopper, located on one side of the raised platform and below the discharge port of the three-roll mill, is used to receive and store the pigment raw materials ground by the three-roll mill. The driving device includes a roller movement driving device and a roller rotation driving device. A first image sensor is installed at the feed port of the three-roll mill, and a second image sensor is installed at the discharge port. A weight sensor is installed at the bottom of the receiving hopper. The first image sensor, the second image sensor, and the weight sensor are all communicatively connected to the controller. The controller contains computer code, and when the computer code is executed, the controller performs the following operations: Acquire the real-time feeding image obtained by the first image sensor; The feeding category corresponding to the real-time feeding image is determined according to a preset image recognition algorithm; Based on the feed category, the driving parameters corresponding to the feed category are determined in a preset database of correspondences between categories and driving parameters; The drive parameters are sent to the drive device to control the operation of the first roller, the second roller, and the third roller; when the computer code is executed, the controller also performs the following operations: Acquire real-time discharge images and real-time weight information obtained by the second image sensor and the weight sensor in the same time period; The real-time discharge image is input into the flow rate prediction neural network model to obtain the second predicted flow rate corresponding to the real-time discharge image; The real-time discharge image is input into a trained particle size prediction neural network model to obtain multiple particle size prediction values ​​corresponding to the real-time discharge image; the particle size prediction neural network model is trained using a training dataset that includes multiple training fluid images and corresponding particle size labels. Calculate the average value of the multiple particle prediction values ​​to obtain the average particle size information corresponding to the real-time discharge image; Based on the real-time weight information and the feed category, and based on a preset database of correspondences between weight, category, and normal range, the normal flow rate reference value and normal particle size reference value corresponding to the real-time weight information and the feed category are determined. Based on the second predicted flow rate and the average particle size information, as well as the normal flow rate reference value and the normal particle size reference value, the adjusted new drive parameters corresponding to the three-roll mill are determined.

2. The grinding equipment according to claim 1, characterized in that, The controller determines the specific method by which the real-time feeding image corresponds to the feeding category based on a preset image recognition algorithm, including: The real-time feed image is input into a pre-trained flow rate prediction neural network model to obtain the first predicted flow rate corresponding to the real-time feed image; the flow rate prediction neural network model is trained using a training dataset that includes multiple training feed images and corresponding flow rate labels. Based on a color recognition algorithm, the color information corresponding to the real-time feeding image is determined; Obtain the pigment production plan corresponding to the processing area of ​​the grinding equipment; Based on the first predicted flow rate, the color information, and the pigment production plan, the feed category corresponding to the real-time feed image is determined.

3. The grinding equipment according to claim 2, characterized in that, The controller determines the specific method for determining the feed category corresponding to the real-time feed image based on the first predicted flow rate, the color information, and the pigment production plan, including: Based on the first predicted flow rate and the color information, multiple candidate pigment raw material categories that meet the requirements are determined from a preset database of correspondences between flow rate and color and pigment category. Based on the required pigment intermediate product information in the pigment production plan and the preset correspondence between pigment intermediate products and pigment raw materials, determine the pigment raw material information corresponding to the required pigment intermediate product information; Calculate the intersection information between the multiple candidate pigment raw material categories and the pigment raw material information to obtain the feed category corresponding to the real-time feed image.

4. The grinding equipment according to claim 3, characterized in that, The driving parameters include roller speed parameters and roller movement parameters; the roller speed parameters include a first roller speed for controlling the first roller, a second roller speed for controlling the second roller, and a third roller speed for controlling the third roller; the roller movement parameters include a first roller movement parameter for controlling the first roller, a second roller movement parameter for controlling the second roller, and a third roller movement parameter for controlling the third roller. Furthermore, the specific method by which the controller determines the driving parameters corresponding to the feed category based on the feed category in a preset database of correspondences between categories and driving parameters includes: The feed category is input into the trained driving parameter prediction neural network model to obtain the driving parameters corresponding to the feed category; wherein, the driving parameter prediction neural network model is trained on a training dataset that includes multiple training feed information and corresponding driving parameter annotations with the best grinding effect.

5. The grinding equipment according to claim 4, characterized in that, The controller determines the adjusted new drive parameters for the three-roll mill based on the second predicted flow rate, the average particle size information, the normal flow rate reference value, and the normal particle size reference value in the following specific ways: Calculate the velocity difference between the second predicted velocity and the normal velocity reference value; Calculate the particle size difference between the average particle size information and the normal particle size reference value; Determine whether the flow velocity difference is greater than a preset flow velocity difference threshold and whether the particle size difference is greater than a preset particle size difference threshold to obtain a first determination result; If the first judgment result is negative, it is confirmed that there is no need to adjust the drive parameters of the three-roll mill; If the first judgment result is yes, then the adjusted new drive parameters corresponding to the three-roll mill are determined based on the flow rate difference and the particle size difference.

6. The grinding equipment according to claim 5, characterized in that, The controller determines the adjusted new drive parameters for the three-roll mill based on the flow rate difference and the particle size difference in the following specific methods: Based on the flow velocity difference, and based on the preset first correspondence between the flow velocity difference and the increased rotation speed value and the increased inter-roller pressure, a first set of increased rotation speed values ​​corresponding to the roller rotation speed parameters and a set of first roller adjustment movement values ​​corresponding to the roller movement parameters are determined. Based on the particle size difference, and based on the preset second correspondence between the particle size difference and the increased rotation speed and increased inter-roller pressure, a set of second rotation speed increase values ​​corresponding to the roller rotation speed parameters and a set of second roller adjustment movement values ​​corresponding to the roller movement parameters are determined. The higher of the first and second speed increase values ​​is determined as the target speed increase value corresponding to the roller speed parameter. The sum of the roller speed parameter and the target speed increase value is determined as the adjusted new roller speed parameter corresponding to the three-roll mill. The union of the first roller adjustment movement value set and the second roller adjustment movement value set is determined as the target movement adjustment set corresponding to the roller movement parameter. The calculation result of the roller movement parameter and the target movement adjustment set is determined as the new adjusted roller movement parameter corresponding to the three-roll mill.

7. The grinding equipment according to claim 6, characterized in that, The three-roll mill also includes a cooling device; the cooling device is used to output coolant to cooling pipes disposed inside the first, second, and third rollers to cool the first, second, and third rollers; the cooling device is communicatively connected to the controller; Furthermore, when the computer code is executed, the controller also performs the following operations: When the first determination result is yes, the temperature of the coolant output by the cooling device is reduced.

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