Parameterized simulation method and system for arc of main roller of roller press and storage medium

Abstract

The present application relates to the technical field of roll press main roller simulation, and particularly relates to a parameterized simulation method and system for roll press main roller curvature and a storage medium. The method comprises the following steps: performing surface space scanning on the roll press main roller to obtain main roller space parameters; performing main roller curvature distribution evaluation according to the main roller space parameters to obtain a main roller curvature distribution graph; dividing the main roller curvature distribution graph into regions and importing the graph into a mechanics analysis software to construct a roll press main roller initial state model; obtaining roll press material data; performing material-main roller stress parameterized simulation according to the roll press material data and the roll press main roller initial state model to obtain simulation main roller stress state data; and performing intelligent main roller bending compensation based on the simulation main roller stress state data to obtain main roller bending compensation parameters. The present application realizes intelligent compensation of the roll press main roller curvature state through main roller stress parameterized simulation, effectively improves product quality, reduces energy consumption and prolongs the service life of the equipment.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of roll press main roller simulation, and in particular to a parameterized simulation method and system for roll press main roller arc and a storage medium. BACKGROUND

[0002] In actual production, the roll press main roller bears complex and variable stress distribution, and the wear degree of different regions is different, resulting in uneven change of the main roller surface arc. Such arc deviation can cause uneven pressure distribution in the rolling process, resulting in inconsistent material density, large product quality fluctuations, increased energy consumption and a series of problems. Especially under high pressure and high load conditions, the main roller arc deviation problem is more prominent, which seriously affects the product quality and service life of the equipment. The traditional detection and maintenance method of the roll press main roller mainly relies on periodic shutdown inspection and empirical repair, and cannot master the change of the main roller arc in real time. Such passive maintenance strategy has obvious defects: on the one hand, the shutdown detection period is long, and it is difficult to find arc abnormalities in time; on the other hand, the detection means is simple, the precision is limited and secondary damage is easy to cause. More importantly, the existing technology cannot accurately predict the arc change trend of the main roller under different working conditions, and maintenance personnel can only compensate and adjust according to experience, which is difficult to achieve precise control. SUMMARY

[0003] Therefore, the present application provides a parameterized simulation method and system for roll press main roller arc and a storage medium to solve at least one of the above technical problems.

[0004] To achieve the above purpose, a parameterized simulation method for roll press main roller arc comprises the following steps:

[0005] Step S1: performing surface space scanning on the roll press main roller to obtain main roller space parameters; performing multiple prediction measurements on the working conditions of the roll press main roller to generate main roller running state data;

[0006] Step S2: dividing the main roller into sections according to the main roller running state data, and then analyzing the main roller arc deviation to obtain main roller arc deviation data; calculating the main roller arc change rate according to the main roller arc deviation data; determining the actual working state of the main roller, and evaluating the main roller arc distribution according to the main roller arc deviation data to obtain a main roller arc distribution map;

[0007] Step S3: dividing the main roller arc distribution map into regions by the main roller arc change rate, and marking the arc stress points; importing the main roller space parameters and the arc stress points into a mechanics analysis software to construct a roll press main roller initial state model; obtaining roll press material data; performing material-main roller stress parameterized simulation according to the roll press material data and the roll press main roller initial state model to obtain simulation main roller stress state data;

[0008] Step S4: intelligent main roller bending compensation based on the simulation main roller stress state data, to obtain the main roller bending compensation parameters.

[0009] Preferably, the present application further provides a parameterized simulation system of the main roller arc of a roller press, which executes the parameterized simulation method of the main roller arc of a roller press as described above, and the parameterized simulation system of the main roller arc of a roller press comprises:

[0010] A main roller arc scanning module is configured to perform surface space scanning on the main roller of the roller press to obtain main roller space parameters, and perform multiple prediction measurements on the working condition of the main roller of the roller press to generate main roller running state data.

[0011] An arc analysis module is configured to divide the main roller into sections according to the main roller running state data, and then analyze the main roller arc deviation to obtain main roller arc deviation data, calculate the main roller arc change rate according to the main roller arc deviation data, determine the actual working condition of the main roller, and evaluate the main roller arc distribution according to the main roller arc deviation data to obtain a main roller arc distribution diagram.

[0012] A stress simulation module is configured to divide the main roller arc distribution diagram into regions through the main roller arc change rate, and mark the arc stress points; import the main roller space parameters and the arc stress points into a mechanics analysis software to construct a main roller initial state model of the roller press; obtain roller material data; perform material-main roller stress parameterized simulation according to the roller material data and the main roller initial state model of the roller press to obtain simulation main roller stress state data.

[0013] A bending compensation module is configured to perform intelligent main roller bending compensation based on the simulation main roller stress state data to obtain main roller bending compensation parameters.

[0014] Preferably, the present application further provides a computer readable storage medium storing a computer program, which is executed to realize the parameterized simulation method of the main roller arc of a roller press as described in any one of the above.

[0015] The present application overcomes the defects of sampling detection and limited precision of traditional detection methods by surface space scanning of the main roller of the roller press to obtain complete and accurate main roller space parameters, thereby ensuring the reliability of the analysis results. Meanwhile, the collection of multiple predictive measurements of the working condition of the main roller generates main roller operating state data, which not only considers the current geometric shape of the main roller, but also fully considers the dynamic changes of the main roller under different working conditions, so that the evaluation of the state of the main roller is more comprehensive and accurate. The main roller section division and radian deviation analysis based on the main roller operating state data can accurately locate the specific position and degree of the radian deviation. By calculating the radian change rate of the main roller, the law of the radian change of the main roller with time can be revealed. By dividing the main roller radian distribution map into regions through the radian change rate of the main roller and marking the radian stress points, the key stress areas on the surface of the main roller can be accurately identified. These information together with the main roller space parameters are imported into the mechanical analysis software to construct a highly realistic initial state model of the main roller of the roller press. This model not only contains the geometric information of the main roller, but also reflects the stress state of the main roller under actual working conditions. Combined with the obtained material data, material-main roller stress parameterized simulation is carried out to accurately simulate the complex interaction between the material and the main roller during the rolling process and obtain the simulation main roller stress state data. This parameterized simulation method fully considers the characteristics of the material and the actual state of the main roller, overcomes the limitations of the traditional simulation method of simplifying the model and ignoring nonlinear factors, and makes the simulation results closer to the actual situation. Through analysis of the simulation results, the deformation trend of the main roller under different working conditions can be accurately predicted, and the optimal bending compensation scheme can be developed accordingly. This intelligent compensation method can effectively offset the negative effects of the radian deviation of the main roller, ensure the uniformity of the pressure distribution during the rolling process, and thus improve the product quality, reduce energy consumption, and prolong the service life of the equipment. Compared with the traditional empirical compensation method, the intelligent compensation method of the present application is more scientific and accurate, can realize active control of the state of the main roller of the roller press, and avoids secondary damage or performance degradation caused by improper compensation. Practical application shows that after adopting the method, the product pass rate is increased by more than 15%, the energy consumption is reduced by about 12%, the service life of the main roller is prolonged by more than 30%, and the annual economic benefits are significantly improved. At the same time, the method reduces the dependence on manual experience, reduces maintenance costs, improves equipment reliability, provides technical support for intelligent and refined operation and management of the roller press, and has a wide application prospect. Therefore, the parameterized simulation method of the radian of the main roller of the roller press of the present application significantly improves the control level of the radian deviation of the main roller through the closed-loop logic of real-time monitoring, accurate analysis, simulation prediction and intelligent compensation, greatly improves the stability and consistency of the product quality, effectively reduces the energy consumption caused by uneven pressure, significantly prolongs the service life of the main roller and related components by reducing local overload and optimizing stress, reduces the non-scheduled downtime and maintenance costs, and ultimately improves the efficiency and economic benefits of the entire rolling production process. BRIEF DESCRIPTION OF DRAWINGS

[0016] Figure 1 A schematic diagram of the step flow of the parameterization simulation method of the main roller arc of the roll press of the present application is shown in the figure.

[0017] Figure 2 A schematic diagram of the step flow of the parameterization simulation method of the main roller arc of the roll press of the present application is shown in the figure. Figure 1 A schematic diagram of the detailed implementation step flow of step S4 is shown in the figure.

[0018] The implementation, functional features and advantages of the present application will be further described with reference to the embodiments and the accompanying drawings. DETAILED DESCRIPTION

[0019] The technical method of the present application will be described clearly and completely below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all the other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0020] In addition, the accompanying drawings are only schematic illustrations of the present application, and are not necessarily drawn to scale. The same reference numerals in the drawings represent the same or similar parts, and thus repeated descriptions thereof will be omitted. Some of the block diagrams shown in the drawings are functional entities, which do not necessarily have to correspond to physically or logically independent entities. The functional entities can be implemented in the form of software, or in one or more hardware modules or integrated circuits, or in different network and / or processor methods and / or microcontroller methods.

[0021] It should be understood that although the terms "first", "second" and the like can be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another. For example, without departing from the scope of the exemplary embodiments, a first element can be referred to as a second element, and similarly a second element can be referred to as a first element. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0022] To achieve the above-mentioned purpose, please refer to Figures 1 to 2 The present application provides a parameterization simulation method of the main roller arc of the roll press, comprising the following steps:

[0023] Step S1: surface space scanning is performed on the main roller of the roll press to obtain the main roller space parameters; the main roller of the roll press is measured multiple times under working conditions to generate main roller running state data;

[0024] Step S2: According to the main roller running state data, the main roller section is divided, and then the main roller arc deviation analysis is carried out to obtain the main roller arc deviation data; the main roller arc change rate is calculated according to the main roller arc deviation data; the actual working state of the main roller is determined, and the main roller arc distribution evaluation is carried out according to the main roller arc deviation data to obtain the main roller arc distribution diagram;

[0025] Step S3: The main roller arc distribution diagram is divided into regions by the main roller arc change rate, and the arc stress points are marked; the main roller space parameters and the arc stress points are introduced into the mechanics analysis software to construct the initial state model of the main roller of the roller press; the rolled material data is obtained; the material-main roller stress parameterization simulation is carried out according to the rolled material data and the initial state model of the main roller of the roller press to obtain the simulation main roller stress state data;

[0026] Step S4: Based on the simulation main roller stress state data, the intelligent main roller bending compensation is carried out to obtain the main roller bending compensation parameters.

[0027] In the embodiment of the application, the parameterization simulation method of the main roller arc of the roller press comprises the following steps:

[0028] Step S1: The surface space of the main roller of the roller press is scanned to obtain the main roller space parameters; the working condition of the main roller of the roller press is measured multiple times to generate the main roller running state data;

[0029] In the embodiment of the present application, for example, on a 1200mmx2000mm high pressure roller press in a certain cement production plant, MLS-2100 laser ranging device is used to perform accurate spatial scanning on the surface of the main roller. During the scanning process, the main roller is fixed on a rotating tool, and axial scanning is performed every 10mm, and the next scanning is performed every 5 degrees of rotation. After 360 degrees of scanning, a data set containing about 15,000 three-dimensional coordinate points is formed. The least square method is used to perform circle fitting processing on the coordinate points obtained by scanning, and the center coordinates and radius values of 21 axial positions are obtained. These data together constitute the spatial profile characteristics of the main roller. Seven measuring points are set on the main roller according to the axial position, and four measuring points are arranged circumferentially at each position (at 0 degrees, 90 degrees, 180 degrees, and 270 degrees, respectively), totaling 28 measuring points. PT100 temperature sensors, eddy current displacement sensors, and torque strain gauges are installed at the measuring point positions, and the sensor measurement accuracies are ±0.1℃, 0.001mm, and 2.1 sensitivity coefficient, respectively. Subsequently, three working condition prediction measurements are performed: the first is performed 10 seconds after the roller press is started under no load and data is collected for 5 seconds; the second is performed 10 seconds after the material enters the roller gap after loading and data is collected for 5 seconds; and the third is performed 10 seconds after stable operation for 5 minutes. The collected raw data includes the temperature distribution of the main roller at different positions (ranging from 25℃ to 80℃), torque values (from no load to full load state), and radial displacement data. The collected data is processed using digital filtering technology to remove abnormal values and noise interference, and a data set accurately reflecting the running state of the main roller is generated.

[0030] Step S2: dividing the main roller into sections according to the main roller running state data, then performing main roller arc deviation analysis to obtain main roller arc deviation data; calculating the main roller arc change rate according to the main roller arc deviation data; determining the actual working state of the main roller, and evaluating the main roller arc distribution according to the main roller arc deviation data to obtain a main roller arc distribution map;

[0031] In the embodiment of the present application, based on the main roller running state data obtained in step S1, first, data classification matrix processing is performed, and the measurement data is separated into two matrices of axial measurement data and circumferential measurement data according to spatial attributes. Then, the 2000mm long main roller is accurately divided into three sections: the first end section (0-700mm), the central section (700-1300mm) and the second end section (1300-2000mm), which respectively account for 35%, 30% and 35% of the total length. Corresponding to the distribution of measurement points, the 1st, 2nd and 3rd measurement points are located in the first end section, the 4th and 5th measurement points are located in the central section, and the 6th and 7th measurement points are located in the second end section. The least square circle fitting method is used to calculate the axial arc value of each section, and the arc value of the first end section is about 1.36, the arc value of the central section is about 0.95, and the arc value of the second end section is about 1.42 (unit 10^-2 mm^-1). The frequency domain analysis is performed on the circumferential torque measurement data, the torque fluctuation characteristic value and the unevenness index are calculated, and the typical value is 7.5%. The time sequence alignment is performed on the temperature data by cubic spline interpolation, and the complete circumferential temperature value time sequence data is created. The arc difference value and the temperature gradient distribution of the central region and the two end regions are calculated, and the weighted fusion arc deviation comprehensive evaluation model is constructed. The sliding window technology is used to analyze the change curve of the arc difference value with time, the arc change rate is calculated, and the mutation point position is marked. At the same time, the comprehensive load index 0.92 is calculated by the main motor load rate (92%), the main roller torque value (87% of the rated torque) and the hydraulic system pressure value (96% of the rated pressure), and it is determined that the main roller is in full load state. The segmented polynomial fitting method is used to analyze the local arc data of 21 axial positions, the principal component analysis method is used to process the arc field distribution characteristics, and finally the arc distribution graph intuitively showing the arc distribution state of the main roller is generated.

[0032] Step S3: dividing the main roller arc distribution graph into regions by the main roller arc change rate, and marking the arc stress points; introducing the main roller spatial parameters and the arc stress points into the mechanics analysis software to construct the initial state model of the main roller of the roller press; obtaining the roller material data; performing material-main roller stress parameterization simulation according to the roller material data and the initial state model of the main roller of the roller press, and obtaining the simulation main roller stress state data;

[0033] In the embodiment of the application, the main roller curvature distribution map is divided into about 8 curvature gradient regions according to a curvature deviation range of 0.02 mm. The curvature difference and distance ratio of adjacent points in each region are measured, and the curvature-distance difference ratios at axial positions 583 mm and 1417 mm are identified as 0.012 and 0.014, respectively, which exceed the threshold judgment standard of 0.01, and the two positions are marked as curvature stress point starting regions. Since the deviation section positions are close to the ends of the roller press, the stress transmission direction is determined to be from the two ends to the middle, and the stress size is marked. The main roller space parameters and curvature stress point data are imported into a mechanics analysis system to create an accurate main roller three-dimensional geometric model, and the model is meshed to generate about 180,000 hexahedral elements. Boundary conditions are set in the model: all degrees of freedom are fixed at the driving end, axial displacement is allowed at the non-driving end, and elastic constraint conditions are set at the support positions. The curvature stress points are set as load application points, and the load size and direction are calculated according to the curvature gradient value of the region where the stress point is located. At the same time, a cement raw material sample is obtained from the production line, and its hardness value (5-level Mohs hardness), density value (2.8 grams per cubic centimeter), moisture content (6.5%), and particle size distribution (10-30 mm accounts for 26%, 5-10 mm accounts for 32%, and 0-5 mm accounts for 42%) are measured. Based on the material characteristic parameters, the particle size distribution, cohesion coefficient, friction coefficient, and elastic modulus of the discrete element model are set, and a material-roller surface interaction simulation model of the rolling process is constructed. Dynamic analysis is performed in the joint simulation system for 60 seconds, and the main roller bending deformation data is recorded every 0.5 seconds. The maximum deformation, average deformation, and deformation change rate in the stable operation stage are extracted to generate complete simulation main roller stress state data.

[0034] Step S4: Intelligent main roller bending compensation based on simulation main roller stress state data to obtain main roller bending compensation parameters.

[0035] In the embodiment of the present application, the hardware units of the main roller of the roller press are encoded and marked, and key parameters such as the working surface width (1400 mm) of the main roller body, the rated pressure range (500-2000 kN / m) and the radial deformation allowable value (1.8 mm) are analyzed. The position distribution data of the hydraulic supporting units are extracted, and the six supporting units are located at the axial positions of 417 mm, 650 mm, 883 mm, 1117 mm, 1350 mm and 1583 mm, and the piston stroke amount of each supporting unit is 20 mm. The accurate correspondence between the supporting position and the stroke amount is established through the position stroke capacity mapping, the pressure adaptation degree of all supporting units is calculated, and the average adaptation degree reaches 98.4%. Eleven high-precision pressure sensors are arranged to monitor the extrusion pressure values of the middle and both ends of the main roller in real time, and the average extrusion pressure of the middle region is calculated to be 1782 kN / m, the average extrusion pressure of both ends is 1645 kN / m, the difference is 137 kN / m, and the normalized difference is 8.02%. The extrusion pressure distribution data are processed by multi-threshold segmentation, and two extrusion pressure peak points located at the axial positions of 600 mm and 1400 mm are marked, and the peak pressures are 1894 kN / m and 1862 kN / m respectively. The stress diffusion around these peak points is monitored at intervals of 5 cm, and a diffusion gradient distribution diagram is generated. The density clustering algorithm is used for clustering analysis of abnormal stress points, and three potential deformation regions are identified, and the deformation degree is evaluated by a multi-index weighted scoring method, wherein the 450-720 mm and 1320-1620 mm regions are divided into severe deformation regions, and the 830-980 mm region is divided into a slight deformation region. The supporting units 2 and 6 corresponding to the severe deformation region are increased by 42 kN and 37 kN of compensation force respectively, and the supporting unit 4 of the slight deformation region is increased by 11 kN of compensation force. The compensation force is applied through the piecewise linear control model, the arc recovery is continuously monitored, and finally a complete main roller bending compensation parameter file is generated.

[0036] Preferably, step S1 comprises the following steps:

[0037] Step S11: performing surface space scanning on the main roller of the roller press by a laser ranging device to obtain the coordinates of the main roller surface in three-dimensional space, the scanning accuracy is ±0.05 mm, the scanning interval is 10 mm in the axial direction and 5° in the radial direction, and the main roller surface coordinate data are recorded;

[0038] Step S12: determining the axial position and the radial dimension of the main roller according to the main roller surface coordinate data;

[0039] Step S13: performing geometric information encoding on the main roller of the roller press through the axial position and the radial dimension to obtain the main roller space parameters;

[0040] Step S14: based on the 7 measurement point positions of the main roller of the roller press arranged uniformly along the main roller axial direction, 4 measuring points are arranged along the circumference of the main roller surface at each measurement point position, and main roller measurement point data is obtained;

[0041] Step S15: according to the main roller measurement point data, temperature sensors, eddy current displacement sensors and torque strain gauges are arranged to construct a main roller measurement network;

[0042] Step S16: the main roller working condition is measured multiple times by using the main roller measurement network to obtain original main roller measurement data; wherein the working condition multiple measurement includes three times of collection, the first collection is performed at the 10th second after the roller press is started under no load, and the collection time is 5 seconds; the second collection is performed after the material enters the roll gap for 10 seconds after loading, and the collection time is 5 seconds; the third collection is performed after stable operation for 5 minutes, and the collection time is 10 seconds; the original main roller measurement data includes the main roller speed value, the main roller torque value and the main roller temperature value;

[0043] Step S17: the original main roller measurement data is subjected to digital filtering processing to generate main roller running state data.

[0044] In the embodiment of the application, the main roller is fixed on a special rotating tool, so that the main roller can rotate at a constant angular velocity. The laser ranging device is erected in the parallel direction of the main roller axis, 500 mm away from the surface of the main roller, and is installed on a precision guide rail to realize axial movement. When the ranging device is started to scan, the main roller is first rotated to the initial position (marked as 0°), and the laser ranging device starts to collect a measuring point every 10 mm along the axial direction from one end of the main roller. After completing an axial scan, the main roller is rotated by 5° to continue the next round of axial scan, and the cycle is repeated until the 360° full scan of the surface of the main roller is completed. The data obtained at each measuring point includes the x, y and z coordinate values of the point in the three-dimensional coordinate system, and the measurement accuracy is controlled within ±0.05 mm. The surface coordinate data of the main roller is imported into a numerical calculation program, and the data points of each axial position are circularly fitted according to the axial position grouping. The circular fitting adopts the Gauss-Newton iteration method, sets the center coordinates of each axial position as (a, b), and the radius as r, and the objective function is F = Σ[(xi-a) 2 +(yi-b) 2 -r 2 ] 2, and the convergence threshold is set to 10-6. After the fitting is completed, a series of center coordinates and radius values along the axial direction are obtained. The axial position of the main roller is determined by the connecting line of each center point obtained by fitting, that is, the center axis of the main roller; the radial size is determined by the radius value at each axial position, including the maximum radius, the minimum radius and the average radius. For the main roller with uneven wear, the roundness error at each axial position also needs to be calculated, that is, the difference between the maximum radius and the minimum radius. A cylindrical coordinate system is established with the center axis of the main roller as the z-axis, and the position of each point on the surface of the main roller is expressed as (r, θ, z), where r is the radial distance, θ is the circumferential angle, and z is the axial position. Then the radial distance r is fitted using Fourier series expansion: r(θ, z) = r0(z) + Σ[a n (z)cos(nθ) + b n (z)sin(nθ)], where n takes 1 to 10. For r0(z), a n (z) and b n (z) at different axial positions z, cubic spline interpolation is used for fitting to obtain continuous functions. Finally, the spatial parameters of the main roller consist of a group of Fourier coefficients and spline coefficients, a total of 21 axial position points, and each position point has 21 Fourier coefficients (r0 and a n , b n, n = 1-10), totaling 441 parameters. These parameters completely describe the geometric characteristics of the main roller surface, including the basic cylindrical shape, axial taper, local concave-convex, and wear condition. The total length L of the main roller is measured, and the effective working length L' is obtained after removing the non-working section length at both ends. The effective working length L' is evenly divided into 6 equal parts, and the axial positions of the 7 measurement points are determined at 0, L' / 6, 2L' / 6, 3L' / 6, 4L' / 6, 5L' / 6, L' respectively. For a standard 2000mm effective length main roller, the 7 measurement points are located at 0, 333.33, 666.67, 1000, 1333.33, 1666.67 and 2000mm. At each axial measurement point position, 4 measurement points are evenly arranged along the circumferential direction of the main roller surface, with circumferential angles of 0°, 90°, 180°, 270°, forming a uniformly distributed measurement point matrix. The measurement point positions are permanently marked with metal marker sheets, with a diameter of 10mm and a thickness of 0.5mm, fixed on the main roller surface by epoxy resin adhesive. Each measurement point marker sheet is engraved with a unique number in the format "A-B", where A represents the axial position serial number (1-7) and B represents the circumferential position serial number (1-4), forming a total of 28 measurement point data acquisition networks. The temperature sensor uses a PT100 platinum resistance sensor with a temperature measurement range of -50℃ to 500℃ and an accuracy of ±0.1℃. It is installed at the 0° measurement point of the 7 axial positions, with the sensor probe inserted 3mm below the main roller surface, through a 4mm diameter and 3mm deep installation hole and filled with heat-conducting silicone grease. The eddy current displacement sensor uses ECP-3502 model, with a measurement range of 0-5mm and a resolution of 0.001mm. It is installed at the 90° and 270° measurement point corresponding positions of the 7 axial positions, with an initial gap of 2mm between the sensor probe and the main roller surface, and is fixed on the roller press frame by a special bracket. The torque strain gauge uses BF350-3AA model with a sensitivity coefficient of 2.1, and is pasted at the 180° measurement point of the 7 axial positions. The strain gauge is installed at a 45° angle along the main roller surface to maximize the detection of shear strain caused by torque. All sensor signals are connected to the data acquisition system through shielded cables, with a sampling frequency of 1000Hz. The data acquisition system uses a 24-bit resolution A / D converter to convert analog signals to digital signals and transmit them to the data processing unit, forming a complete main roller measurement network. The main roller measurement network is used for multiple prediction measurements under working conditions. The first acquisition starts after the roller press motor starts and the main roller reaches the rated speed (usually 30rpm) and stabilizes for 10 seconds, and lasts for 5 seconds. During the acquisition process, the temperature values of each measurement point are recorded, and the initial temperature is usually the ambient temperature (about 25℃); at the same time, the radial displacement value measured by the eddy current sensor is recorded, which represents the runout of the main roller under no load; and the strain value measured by the torque strain gauge is recorded, which is converted into torque value, and the no load torque value mainly reflects the system mechanical resistance.The second acquisition is performed 10 seconds after the material (such as cement raw material) enters the roll gap through the feeding device, at which time the roller press begins to bear the pressure of the material, and the acquisition lasts for 5 seconds. The acquisition content includes the temperature rise value (usually rises by 2-5°C) of each measuring point, the radial displacement change (reflecting the force deformation of the roller system), and the torque value (significantly higher than the no-load value). The third acquisition is performed after the roller press has been running stably for 5 minutes, at which time the system reaches a state of thermal equilibrium, and the acquisition lasts for 10 seconds. The recorded data includes the temperature distribution under the stable working state (usually reaches 60-80°C), the radial position of the main roller (reflecting the stability of the system after a long time of running), and the torque value (reflecting the energy consumption of the material processing). All the acquisition data is marked with a time stamp and saved as the original main roller measurement data. All the acquisition signals are pre-processed, including removing abnormal values and baseline drift. The abnormal value determination standard is the data points exceeding the range of 3 times the standard deviation, and the linear interpolation method is used to replace the abnormal values. Then, a low-pass Butterworth filter is applied to the temperature data for processing, with the cutoff frequency set to 2 Hz, the filter order set to 4, and the filter transfer function set to H(s) = 1 / [1+(s / 2π·2)]. 4 ], to eliminate high-frequency noise while retaining the temperature change trend. A band-pass filter is applied to the torque data for processing, with the passband range set to 1-50 Hz, to retain the periodic torque fluctuation information related to the main roller speed, and the average torque value and torque fluctuation amplitude are calculated after filtering. The eddy current displacement sensor data is processed using wavelet denoising technology, with the db5 wavelet basis function, 5 layers of decomposition, the soft threshold method for threshold value, and the Donoho formula th = σ√(2lnN) for threshold value calculation, where σ is the noise standard deviation and N is the number of data points.

[0045] Preferably, in step S2, the main roller is divided into sections according to the main roller running state data, and then the main roller arc deviation analysis includes:

[0046] The main roller running state data is divided into main roller axial and main roller surface circumferential measurement data, respectively obtaining the main roller axial measurement data and the main roller surface circumferential measurement data.

[0047] Based on the main roller spatial parameters, the main roller measurement points are divided, and the main roller is divided into 3 sections according to the axial position, namely two end sections and a central section, obtaining the main roller sections; wherein the length of the two end sections is 35% of the full length of the main roller, and the length of the central section is 30% of the full length of the main roller.

[0048] The main roller axial arc value of each section of the main roller is calculated according to the main roller axial measurement data.

[0049] The circumferential torque value of each section of the main roller is analyzed according to the main roller surface circumferential measurement data.

[0050] The temperature value time sequence data is obtained by aligning the main roller surface circumferential measurement data in time sequence.

[0051] The central-end arc difference value is calculated based on the axial arc value of each section of the main roller.

[0052] The temperature-torque gradient data of the main roller is obtained by comparing the numerical values of the circumferential temperature value time series data and the circumferential torque value of each section of the main roller, and arranging the numerical gradient.

[0053] The main roller arc deviation data is obtained by integrating the central-end arc difference value and the main roller temperature-torque gradient data.

[0054] In the embodiment of the application, the data is separated according to the spatial attributes by using the data classification matrix method. In the process of extracting the axial measurement data of the main roller, the sensor data of 7 axial positions is reorganized according to the time synchronization principle to form an n×7 data matrix M_axial, where n is the number of sampling time points. Each row in the matrix represents the measurement value at 7 different axial positions at the same time, and each column represents the data sequence of the same axial position changing with time. When extracting the circumferential measurement data of the surface of the main roller, the data of 4 circumferential points at each axial position is arranged according to the circumferential angle (0°, 90°, 180°, 270°) to form an n×28 data matrix M_circ. The matrix is organized according to the double index structure of "axial position-circumferential position", i.e. the first 4 columns are the data of the 4 circumferential points of the first axial position, and so on. For a main roller with a standard length of 2000 mm, the two end sections each account for 35% of the total length, i.e. 700 mm at each end; the central section accounts for 30% of the total length, i.e. 600 mm. When dividing, first determine the axial coordinate origin position of the main roller (usually the end face of the main roller), then calculate the boundary positions of each section: the first end section ranges from 0 to 700 mm, the central section ranges from 700 to 1300 mm, and the second end section ranges from 1300 to 2000 mm. Corresponding to the 7 measurement points set, the 1st, 2nd and 3rd measurement points are located in the first end section, the 4th and 5th measurement points are located in the central section, and the 6th and 7th measurement points are located in the second end section. The section division follows the stress and wear law of the main roller, and the two end sections usually bear greater stress concentration and non-uniform wear, while the central section maintains a relatively stable working state. The axial position points and their corresponding radial displacement values in each section are taken to construct a coordinate set. For example, the 1st, 2nd and 3rd measurement points in the first end section and the 4th and 5th measurement points in the central section are taken to construct a coordinate set, and the 6th and 7th measurement points in the second end section are taken to construct a coordinate set. The least square method is applied to fit the circular arc equation (z-a) 2 +(r-b) 2 =R 2where (a, b) is the center coordinate and R is the radius of the circle. The fitting process uses the Levenberg-Marquardt algorithm to solve the nonlinear least squares problem, and the iteration termination condition is that the parameter variation is less than 10^-8 or the iteration number exceeds 500. After the fitting is completed, the curvature of the arc is calculated as K = 1 / R, and the axial radian value is defined as 100-K, with the unit of 10^-2 mm^-1. For the main roller of the calender under standard working conditions, the radian value of the first end section is usually 1.25-1.45, the radian value of the central section is 0.85-1.05, and the radian value of the second end section is 1.30-1.50. The torque data in each section is statistically analyzed in the time domain to calculate the average, standard deviation, maximum, and minimum values. Taking the first end section as an example, the average torque values of the 4 circumferential measuring points at the positions of the 1st, 2nd, and 3rd measuring points are calculated, denoted as T_avg_1, and the standard deviation is denoted as T_std_1; similarly, the T_avg_2, T_std_2, T_avg_3, and T_std_3 of the central section and the second end section are calculated. Then, frequency domain analysis is performed, and the fast Fourier transform (FFT) is performed on the torque time series of each section, with the Hanning window as the sampling window and the FFT point number as 4096, to calculate the frequency spectrum distribution of the torque fluctuation. The amplitude of the main frequency component (usually related to the main roller speed, and for a main roller with a speed of 30 rpm, the main frequency is 0.5 Hz) is extracted, denoted as the circumferential torque fluctuation characteristic value T_amp_1, T_amp_2, and T_amp_3 of each section. The torque non-uniformity index of each section is calculated and defined as the ratio of the standard deviation to the average value: T_nonunif_i = T_std_i / T_avg_i x 100%, which should be less than 8% when the main roller is working normally. The starting time of the first measurement (10 seconds after the calender is started under no load) is taken as the time zero t0. Due to the slight time difference in the sampling of the temperature sensors of each measuring point, data interpolation alignment is required. The temperature time series of each measuring point is resampled on a unified time scale using the cubic spline interpolation method, with a sampling interval of 1 ms. The aligned temperature data is organized into a three-dimensional matrix T(i, j, k), where i represents the time index (1 to the total number of samples), j represents the axial position index (1 to 7), and k represents the circumferential position index (1 to 4). The time axis of the temperature data is standardized, with the 5-second data of the first measurement mapped to 0-5000 ms, the 5-second data of the second measurement mapped to 10000-15000 ms, and the 10-second data of the third measurement mapped to 60000-70000 ms. The temperature change in the middle period is estimated using linear interpolation to form complete circumferential temperature value time series data. The average radian value of the two end sections is calculated as K_ends = (K_1 + K_3) / 2, where K_1 and K_3 are the radian values of the first end section and the second end section, respectively. Then, the central-two-end radian difference value is calculated as AK = K_2-K_ends, where K_2 is the radian value of the central section.The positive and negative of the difference in curvature indicates the curvature distribution characteristics: when Δκ > 0, the central segment has a larger curvature than the two ends, and the main roller is "convex"; when Δκ < 0, the central segment has a smaller curvature than the two ends, and the main roller is "concave". Under normal working conditions, the main roller should be slightly concave, and the typical difference in curvature Δκ should be between -0.2 and -0.5. The absolute value |Δκ| of the difference in curvature is calculated as an index of the uniformity of the curvature of the main roller. The smaller the value, the more uniform the axial curvature distribution of the main roller. At the same time, the difference in curvature between the two end segments Δκ_ends = |κ_1-κ_3| is calculated, which reflects the symmetry of the two ends of the main roller and should generally be controlled within 0.15. The average temperature of each segment under stable working conditions (third measurement) is calculated and denoted as T_avg_1, T_avg_2 and T_avg_3. The temperature gradients ΔT_1-2 = T_avg_1-T_avg_2 and ΔT_3-2 = T_avg_3-T_avg_2 of each segment are calculated, indicating the temperature difference between the ends and the center. Similarly, the torque gradients ΔM_1-2 = T_avg_1-T_avg_2 and ΔM_3-2 = T_avg_3-T_avg_2 are calculated. Then a three-dimensional data point (κ_i, T_avg_i, M_avg_i) is constructed, i = 1, 2, 3, representing the curvature value, temperature value and torque value of each segment. Polynomial interpolation is performed on the three data points to obtain the curvature-temperature-torque relationship function κ = f(T, M). The partial derivatives of temperature and torque with respect to curvature are calculated. and Constructing the sensitivity matrix The segments are arranged according to the numerical gradients of temperature and torque to form a gradient sequence table. Under normal circumstances, the temperature gradient is sorted from high to low as the first end segment > the second end segment > the central segment, and the torque gradient is sorted according to the material distribution. The main roller curvature deviation index DI = w1·|Δκ| + w2·max(|ΔT_1-2|, |ΔT_3-2|) / T_ref + w3·max(|ΔM_1-2|, |ΔM_3-2|) / M_ref is defined, where w1, w2, w3 are weight coefficients, taking values of 0.5, 0.3 and 0.2 respectively, T_ref is the reference temperature value (taking 50°C), and M_ref is the reference torque value (taking 80% of the rated torque). According to the DI value, the main roller curvature state is classified: DI < 0.3 is excellent, 0.3 ≤ DI < 0.6 is normal, 0.6 ≤ DI < 0.9 is pre-warning, and DI ≥ 0.9 is dangerous. At the same time, the main roller curvature deviation spatial distribution diagram is constructed, with the horizontal axis as the axial position z and the vertical axis as the deviation value δ(z), and the curve is obtained by polynomial fitting: δ(z) = a0 + a1z + a2z 2 + a3z 3 where the coefficients a0, a1, a2, a3 are determined by least squares fitting of the actual deviation values of the measurement points.

[0055] Preferably, the main roller section division according to the main roller operation state data in step S2 is followed by the main roller arc deviation analysis, which comprises:

[0056] The central-to-end arc difference in the main roller arc deviation data is subjected to time series analysis, and the arc difference change rate of the main roller at different time points is calculated to obtain the main roller arc change rate;

[0057] According to the main roller spatial parameters, it is judged whether the main roller has an axis deviation, and the section with a deviation exceeding 0.05 mm is marked to obtain main roller deviation section data;

[0058] The actual working state of the main roller is determined, and the working state is divided into full load, half load and light load;

[0059] Based on the main roller deviation section data and the main roller temperature-torque gradient data in the main roller arc deviation data, the main roller in the three working states is subjected to arc deformation measurement to obtain main roller arc deformation data;

[0060] The main roller local arc data is determined according to the main roller arc deformation data;

[0061] The main roller local arc data is subjected to arc field statistics, and arc field distribution data is detected;

[0062] The arc field distribution data is imported into a drawing tool, and the data points are mapped to a two-dimensional coordinate system, wherein the coordinate axes respectively represent the physical position and the arc value of the main roller, to obtain a main roller arc distribution map.

[0063] In the embodiment of the application, a sampling time point sequence T = {t1, t2,..., t n} is established, t1 corresponds to the first collection start time, t n corresponds to the third collection end time. For each time point t i , the central-to-end arc difference Δκ(t i ) at that time is calculated. The time series data is processed by using a sliding window technique, the window length is 1 second (1000 data points), and the window overlap rate is 50%. The mean, variance and linear trend slope of the data in each window are calculated. The arc difference change rate is calculated by using the central difference method: R(t i ) = [Δκ(t i+1 )-Δκ(t i-1 )] / (t i+1 -t i-1 ), the unit is 10 -2 mm -1 / s. For each working stage (empty start, load running, stable running), calculate the average change rate R_avg_1, R_avg_2 and R_avg_3 respectively. Set the change rate threshold R_th = 1.5 x 10 -3 mm -1 / s, when |R(t i )| > R_th, mark the time as the inflection point. According to the time series analysis results, draw the curve of the arc difference value changing with time, and mark the inflection point on the curve. Typically, the central-to-end arc difference value change rate first increases and then decreases during the process of the roll press from empty to full load, and finally tends to be stable. The change rate in the stable stage should be less than 5 x 10 -4 mm -1 / s. In a three-dimensional rectangular coordinate system, it is represented as L(t) = P0 + t · v, where P0 is the starting coordinate of the axis, and v is the axis direction vector, which should be parallel to the Z axis in the ideal case. Use the axial position data of the main roll to extract the center coordinates at each axial position, forming the point set {(x1, y1, z1), (x2, y2, z2),..., (x n , y n , z n )}. Use the three-dimensional least squares method to fit the actual axis equation L'(t) = P0' + t · v'. Calculate the angle θ = arccos((v · v') / (|v| · |v'|)) between the theoretical axis and the actual axis, and the minimum distance d = |(P0 - P0') × v| / |v| between the two axes. When θ > 0.1° or d > 0.05 mm, it is determined that the main roll has axis deviation. Calculate a cross section every 50 mm along the axial direction of the main roll, and calculate the deviation distance δ i = |P' i -P i | of the actual axis and the ideal axis for each cross section, where P' i is the point on the actual axis, and P i is the corresponding point on the ideal axis. Mark all δ iThe section of >0.05 mm is a deviation section, and the starting position, the ending position and the maximum deviation value thereof are recorded to form a main roller deviation section data table. A working state judgment index system is established, including three core indexes of a main motor load rate, a main roller torque value and a hydraulic system pressure value. The main motor load rate M_r is calculated from the ratio of the actual output power of the motor to the rated power, M_r = P_actual / P_rated x 100%; the main roller torque value T is calculated from the torque strain gauge measurement data, and the average value under the stable working condition is taken; and the hydraulic system pressure value P is directly measured by the hydraulic sensor. The three indexes are standardized: M_r_norm = M_r / 100%, T_norm = T / T_rated, and P_norm = P / P_rated, wherein T_rated is the rated torque of the main roller, and P_rated is the rated pressure of the hydraulic system. The comprehensive load index LI = 0.4 x M_r_norm + 0.4 x T_norm + 0.2 x P_norm is calculated. According to the LI value, the working state is divided into: full load state (0.8≤LI≤1.0), corresponding to that the material fills the roll gap and reaches the design capacity; half load state (0.4≤LI<0.8), corresponding to that the material partially fills the roll gap and the production load is medium; light load state (0.1≤LI<0.4), corresponding to that the material enters the roll gap in a small amount and the production load is low. For the case of LI>1.0, it is recorded as an overload state, and the process parameters need to be adjusted immediately or the material input needs to be reduced. For each working state, the “main roller temperature-torque gradient data” collected under the state is selected. For each deviation section marked in the “main roller deviation section data”, the corresponding temperature-torque gradient value is found from the “main roller temperature-torque gradient data” according to the axial position thereof. According to the temperature-torque gradient value, the length and position of the deviation section, and the working state (full load, half load, light load), the main roller rigid material deformation amount of the deviation section under the working state is estimated by using an empirical formula. The main roller axis is divided into 21 evaluation points, and the distance between adjacent evaluation points is 1 / 20 of the effective length of the main roller. For each evaluation point, the radial displacement data thereof is extracted, and the local arc is calculated in combination with the original geometric data. The local arc κ_local is calculated as follows: ① the positions and radial coordinates of 5 points including the evaluation point and the 2 points before and after the evaluation point are taken; ② a second-order polynomial y = ax 2 +bx+c is used to fit the 5 points; and ③ the curvature κ_local of the fitted curve at the evaluation point is calculated, κ_local = |2a| / [1+(b) 2 ]^(3 / 2). The least square method is used to solve the fitting parameters a, b and c, and the objective function F = Σ[(ax_i 2 +bx_i+c-y_i) 2], i = 1 to 5, the normal equations are obtained by taking the derivative to be zero, and the fitting coefficients are solved. For the edge points (the first 2 points and the last 2 points), an asymmetric point selection strategy is adopted to ensure the accuracy of the fitting. The local camber values of the 21 evaluation points are organized into a sequence {κ_local_1, κ_local_2,..., κ_local_21} to form the local camber data of the main roll. At the same time, the deviation rate of the local camber from the global average camber is calculated: δ_i = (κ_local_i - κ_avg) / κ_avg × 100%, which should be less than 12% in normal cases. The local camber data is regarded as a 21-dimensional vector κ_local = [κ_local_1, κ_local_2,..., κ_local_21]. First, the data is standardized so that the mean of each dimension is 0 and the standard deviation is 1, obtaining the standardized camber vector κ_norm. The covariance matrix C = κ_norm^T·κ_norm / 20 is calculated, with a matrix size of 21 × 21. The eigenvalues λ_1 ≥ λ_2 ≥... ≥ λ_21 and the corresponding eigenvectors v_1, v_2,..., v_21 are obtained by eigenvalue decomposition of the covariance matrix C. The eigenvalues represent the variance contribution of each principal component, and the eigenvectors represent the direction of the principal component. The principal component scores are calculated: s_i = κ_norm·v_i, i = 1 to 21. The first three principal components are taken to construct the camber field feature space. The cumulative variance contribution rate is calculated: CVR_k = (λ_1 + λ_2 +... + λ_k) / (λ_1 + λ_2 +... + λ_21), k = 1 to 3. Usually CVR_3 > 0.85, indicating that the first three principal components can explain more than 85% of the camber field variation. The spatial distribution characteristics of the camber field are determined by the scores of the first three principal components, forming the camber field distribution data.

[0064] Preferably, the step S3 of dividing the camber distribution map of the main roll into regions by the camber change rate of the main roll and marking the camber stress points comprises:

[0065] The camber distribution map of the main roll is divided into regions according to the camber deviation range by the camber change rate of the main roll, and the camber gradient region is obtained; wherein the camber deviation range of each region is set to 0.02 mm;

[0066] The camber difference between adjacent measurement points is measured in each camber gradient region, and the ratio of the camber difference to the distance between adjacent measurement points is calculated to obtain the camber-distance difference ratio data;

[0067] If the camber-distance difference ratio data is greater than 0.01 mm / cm, it is preliminarily determined that the region corresponding to the camber-distance difference ratio data is the camber stress point starting region;

[0068] The stress transmission direction is determined based on the starting area of the arc stress point; if the main roller deviation section is close to the middle of the roller press, the stress transmission direction is from the middle to both ends; if the main roller deviation section is close to one end of the roller press, the stress transmission direction is from the end to the other end;

[0069] The stress size is determined by comparing the arc difference between the arc gradient areas;

[0070] The stress transmission direction and the stress size are marked on the main roller arc distribution map, and the marked stress transmission path is superimposed on the main roller arc distribution map to generate the arc stress point.

[0071] In the embodiment of the application, the main roller arc rate of change data set calculated in the extraction step S2 is extracted, and the data set contains the arc rate of change values of 21 axial positions and 16 circumferential positions of the main roller surface. Based on these data, the main roller surface is partitioned using the contour line segmentation method, and the arc deviation range is set to 0.02 mm, that is, the arc difference value between adjacent areas is fixed to 0.02 mm. In the specific operation, the maximum and minimum values of the arc of the main roller surface are first determined. For a standard main roller with a diameter of 1200 mm, the arc change range is usually 0.85-1.45×10 -2 mm -1 , according to which the entire range is divided into about 8 areas, and the arc deviation in each area does not exceed 0.02 mm. In the division process, the bilinear interpolation algorithm is used to calculate the arc values between the grid points to ensure the continuity of the area boundary. When drawing the contour line map, different areas are marked with different colors, for example, the 0.85-0.87 area is blue, the 0.87-0.89 area is cyan, and so on, until the 1.43-1.45 area is red. Finally, a layered main roller arc distribution map clearly showing the boundaries of each arc gradient area is generated, which contains 8 areas marked as D1 to D8. In each area, a grid of measurement points is determined. For a main roller with a diameter of 1200 mm and a length of 2000 mm, one measurement point is set every 100 mm along the axial direction and every 45 degrees along the circumferential direction, forming a 21×8 measurement point matrix. The arc difference value Δκ ij = |κ i+1j -κ ij | is calculated for each pair of adjacent measurement points (i, j) and (i+1, j), where κ ij represents the arc value at point (i, j). At the same time, the actual physical distance d ij between adjacent measurement points is measured. For axially adjacent points, the distance is 100 mm; for circumferentially adjacent points, the distance is πD / 8≈471 mm, where D is the diameter of the main roller. The arc-distance difference ratio β ij = Δκ ij / d ij, in mm / cm. For example, at two measurement points at axial positions 600 mm and 700 mm within region D3, the radian values are 0.92 x 10 -2 mm -1 and 1.05 x 10 -2 mm -1 , respectively, with a radian difference of 0.13 x 10 -2 mm -1 and a distance of 100 mm, the radian-distance difference ratio is calculated to be 0.013 mm / cm. The same calculation is performed for all pairs of measurement points to generate a complete radian-distance difference ratio data matrix. Each radian-distance difference ratio value β ij is judged: when β ij > 0.01 mm / cm, the corresponding pair of measurement points is marked as a potential stress point starting position. In the actual case of the main roll testing, it is found that the radian-distance difference ratio reaches 0.013 mm / cm in the region of axial positions 500-600 mm, and reaches 0.014 mm / cm in the region of axial positions 1400-1500 mm, both of which exceed the judgment threshold of 0.01 mm / cm. At the same time, region connectivity analysis is performed to connect adjacent high radian-distance difference measurement points into continuous regions, and finally two radian stress point starting regions are determined: the first region ranges from axial 450-650 mm, circumferential 315-45 degrees, with a center position of about (550 mm, 0 degrees); the second region ranges from axial 1350-1550 mm, circumferential 135-225 degrees, with a center position of about (1450 mm, 180 degrees). The determined stress point starting regions are highlighted and marked, and the region range, center position, and maximum radian-distance difference ratio value are recorded in the data table. In this embodiment, the two determined stress point starting regions are located at axial positions 550 mm and 1450 mm, which are located in the first end section (0-700 mm) and the second end section (1300-2000 mm) of the main roll respectively, i.e. the deviation section is close to the ends of the roll press. According to the stress transfer rule, when the deviation section of the main roll is close to one end of the roll press, the stress transfer direction is from that end to the other end. Therefore, the stress transfer direction of the first region (550 mm) is determined to be from the first end to the middle, and the stress transfer direction of the second region (1450 mm) is determined to be from the second end to the middle. Using vector representation, the stress transfer vector of the first region is (0, 0, 1), indicating along the positive direction of the Z axis (from the left end to the right); the stress transfer vector of the second region is (0, 0, -1), indicating along the negative direction of the Z axis (from the right end to the left). The above stress transfer direction conclusion is verified by stress distribution gradient, and it is found that the stress gradient from both ends to the middle is significantly higher than other directions. The average radian value κ av g =∑κ ij / n, where n is the number of measuring points in the region. For adjacent radian gradient regions D m and D n , the inter-regional radian difference Δκ mn = |κ av g, m -κ av g, n | is calculated. The inter-regional radian difference is divided by the inter-regional average distance d mn to obtain the radian gradient value G mn = Δκ mn / d mn . The radian gradient value is directly related to the stress size, and a linear mapping function S = k · G is used to convert the gradient value into a normalized stress size, where k is a proportionality coefficient, which is calibrated by experiment to be k = 5 × 10 4 N·cm. In this embodiment, the radian difference of the first stress point starting region (the junction of D3 and D4) is 0.10 × 10 -2 mm -1 , the corresponding inter-regional average distance is 90 mm, and the calculated radian gradient value is 0.011 × 10 -2 mm -1 / cm, which is converted to a stress size of about 550 N; the radian difference of the second stress point starting region (the junction of D5 and D6) is 0.12 × 10 -2 mm -1 , the corresponding inter-regional average distance is 85 mm, and the calculated radian gradient value is 0.014 × 10 -2 mm -1 / cm, which is converted to a stress size of about 700 N. These values indicate that the stress borne by the second stress point is about 27% greater than that borne by the first stress point, which is consistent with the actual measured pressure distribution. On the base radian distribution map, different color arrows are used to mark the stress transmission direction, with red arrows representing the left-to-right transmission direction of the first stress point (550 mm), and blue arrows representing the right-to-left transmission direction of the second stress point (1450 mm). The arrow lengths are set in proportion to the stress sizes, with the first stress point arrow length set to 55 mm and the second stress point arrow length set to 70 mm. Circular markers are drawn at the starting points of the arrows, with the diameters representing the influence ranges of the stress points, with the first stress point marker diameter set to 120 mm and the second stress point marker diameter set to 140 mm. A ray tracing algorithm is used to generate stress transmission paths, which project the stress from the stress points along the transmission direction, with the path width drawn according to the stress size decay law: W(d) = W0·exp(-αd), where W0 is the initial width, d is the distance from the stress point, and a is the decay coefficient (value 0.005 mm -1 ). The generated stress transmission paths are superimposed on the original radian distribution map in a layer, with the transparency set to 40% to ensure that the underlying radian distribution is clearly visible.

[0072] Preferably, the step S3 of introducing the main roller space parameters and the radian force points into the mechanical analysis software to construct the initial state model of the main roller of the roller press comprises:

[0073] introducing the main roller space parameters and the radian force points into the mechanical analysis software, and constructing a three-dimensional geometric model of the main roller according to the main roller space parameters;

[0074] setting the surface of the main roller as a force grid node according to the three-dimensional geometric model of the main roller, and determining the fixed end, the free end and the support structure of the main roller, and marking the position and size of each key section of the main roller;

[0075] setting the radian force points as load application points, and setting stress transmission parameters between the nodes;

[0076] correlating the radian force points on the surface of the main roller; if the main roller is in a full load state, the force distribution is uniformly connected; if the main roller is in a half load or light load state, the force distribution is unevenly distributed, so as to construct the initial state model of the main roller of the roller press.

[0077] In the embodiment of the application, the main roller space parameters and the radian stress points of the mark are taken as input data, and are introduced into the ANSYS mechanical analysis system to construct a three-dimensional geometric model of the main roller. In the introduction process, the main roller space parameters are first converted into a geometric data file in IGES format to ensure the geometric accuracy in the data transmission process. During the conversion, 441 geometric control points are used, and the control point distribution density is 21 points in the axial direction x 21 points in the circumferential direction, so as to form a complete NURBS curved surface description. After being introduced into the ANSYS working environment, the DesignModeler module is used to create a solid main roller model, and the specific operation includes the following steps. First, a cylindrical coordinate system is established with the main roller axis as the reference, and the axis direction is defined as the Z axis. Then, the main roller surface is constructed according to the introduced NURBS curved surface data. Next, the main roller solid model is generated through a scanning operation (Sweep), and a journal structure is added at both ends, with a diameter of 40% of the main roller diameter and a length of 30% of the diameter. Finally, internal structural features are added, including a cooling channel (an axial channel with a diameter of 25 mm, 30 mm away from the surface) and a keyway (with a width of 8% of the main roller diameter and a depth of 4% of the diameter). The completed three-dimensional geometric model accurately reflects the external dimensions and internal structure of the main roller, retains the slight changes in the surface radian, and the model accuracy is controlled within ±0.02 mm. Based on the constructed three-dimensional geometric model of the main roller, the ANSYS Mechanical module is used to set the stress grid nodes. The hexahedron-dominated hybrid grid method is used for grid division, and the following detailed parameters are used during the grid generation process. The surface grid size of the main roller is set to 10 mm, the radian stress point area grid is encrypted to 5 mm, and the internal grid size gradually increases to 30 mm. The surface grid generation adopts the curved surface fitting technology to ensure that the curvature capture accuracy is more than 95% of the original curvature. The grid quality control parameters include a skewness <0.85 and an orthogonal quality >0.65. After the grid generation, the number of typical elements of the main roller model reaches 180,000, and the number of nodes is about 250,000. When the boundary conditions are set, the fixed end, the free end and the support structure of the main roller are first determined. The driving end (usually the left end) is set as the fixed end, which is realized by fixing all the degrees of freedom of the journal end face (UX=UY=UZ=ROTX=ROTY=ROTZ=0). The non-driving end is set as the free end, which allows axial displacement but limits radial displacement (UX=UY=ROTX=ROTY=0). The support structure adopts bearing constraints, and an elastic support is applied to the outer surface of the journal neck, with an elastic coefficient of 5×109N / m. When the key sections of the main roller are labeled, the actual size is determined. For a main roller with a total length of 2000 mm, the two end sections are 700 mm (0-700 mm and 1300-2000 mm), and the central section is 600 mm (700-1300 mm). An independent cross-sectional number is set for each section. In the implementation, the load application node group is created at the corresponding position in the ANSYS working environment.The load is applied using multi-point constraint (MPC) technology. First, a virtual node is created at the location of the arc force point, and then the virtual node is connected to the surrounding solid mesh nodes through a rigid beam unit (BEAM188). For each arc force point on the main roller surface, three key parameters are set: load size F_i (unit: N), load direction vector n_i (unit vector), and load influence area radius R_i (unit: mm). The load size is determined according to the arc gradient area where the force point is located. The calculation formula is F_i = k·ΔG_i·A_i, where k is the proportional coefficient (typical value is 1.2×107N / mm), ΔG_i is the arc gradient value (unit: mm-1), and A_i is the area affected by the force point (unit: mm). 2 ). The load direction vector is determined based on the stress transfer direction. When the stress is transferred from the middle to the two ends, is the angle with the axial direction, usually 15°-30°. When stress is transferred from one end to the other, n_i = [0, 0, ±1]. The radius of the load-affected area is determined based on the radian-distance difference ratio data, R_i = 50 + 200·|δ_i|, where δ_i is the radian-distance difference ratio (unit: mm / cm). The stress transfer parameter settings between nodes include: contact stiffness coefficient K_c = 2×108N / m 3, friction coefficient μ = 0.15, stress attenuation coefficient α = 0.85, stress transmission distance threshold d th = 3 · R i. According to the force correlation of the surface arc stress point of the main roller working state, a complete initial state mechanical model is constructed. Under full load state, the stress distribution realizes uniform connection, the operation method is: first, divide the main roller surface into 64 circumferential units × 21 axial units of regular grid; Then assign a uniform pressure P uniform = F total / A total to each grid unit, where F total is the total load (usually 6 × 105 N), A total is the total area of the main roller surface; Then the load correction is carried out for the area near the stress point, and the correction coefficient is c i = 1 + 0.4 · sin (πd i / R i), d i is the distance from the grid unit to the nearest stress point; Finally, through the pressure continuity smoothing algorithm, the pressure change rate between adjacent units is ensured to be less than 15%. Under half load state, the stress is unevenly distributed, and a segmented pressure mode is adopted: the main roller is divided into stress area and non-stress area, the stress area covers 180° range, and the pressure value is 1.6 times of the full load state; The pressure value of the non-stress area is 0.4 times of the full load state; A 30° transition zone is set between the two areas, and a cosine transition function P trans = P loaded · (1 + cos (θ)) / 2 + P unloaded · (1 - cos (θ)) / 2, θ ∈ [0, π] is used. Under light load state, the stress is concentrated in local area, and Gaussian distribution model is used to describe the pressure distribution: P (x, y) = P max · exp (- (x 2 / 2σ x 2 +y 2 / 2σ y 2 )), where P max is the maximum pressure value (usually 2.5 times of the full load pressure), σ x and σ y are the distribution parameters in axial and circumferential directions, respectively, and the values are 1 / 3 of the load acting area. After the model is constructed, initial equilibrium calculation is performed to ensure that the stress system meets the static equilibrium condition, and the local load is adjusted until the difference between the force and the counterforce is less than 0.5% of the total load.

[0078] Preferably, the material-main roller stress parameterization simulation in step S3 according to the roller material data and the initial state model of the main roller of the roller press comprises:

[0079] According to the roller material data, the hardness value, density value, moisture content and particle size distribution of the roller material are obtained as material characteristic parameter data;

[0080] According to the roller material data, the target particle size distribution, maximum particle size limit and minimum particle size limit are extracted as grinding task specification data;

[0081] According to the grinding task specification data, the roller press operation parameters are matched;

[0082] The material characteristic parameter data is used to set the material discrete element parameters of the initial state model of the roller press main roller, including the material particle size distribution, cohesion coefficient, friction coefficient, and elastic modulus. A material-roller surface interaction simulation model of the roller pressing process is constructed. The material particle size distribution follows the Rosing-Rammler distribution, and the friction coefficient includes the particle-particle friction coefficient and the particle-roller surface friction coefficient.

[0083] Set the initial curvature of the main roller and set the initial curvature to the standard straight line state, that is, the curvature deviation is 0mm; set the main roller working mode to continuous pressing, and set the pressing pressure to 500-2000kN / m and the main roller linear speed to 0.5-1.5m / s;

[0084] Start the calculation function of the mechanical analysis software, input the roller press operating parameters into the material-roller surface interaction simulation model to perform material-main roller stress parametric simulation, and obtain the simulated main roller stress state data.

[0085] In the embodiment of the present invention, a standard sampling technique is used to obtain real-time material samples, and the sample size is one thousandth of the hourly processing volume of the production line, and is at least not less than 10 kg. The obtained samples are subjected to a standard test process: the hardness value is measured using a Mohs hardness tester, and the material is measured at random points 10 times, and the arithmetic mean is taken, with a measurement accuracy of ±0.2 level; the density value is determined by the volume displacement method, and a material sample of known mass is placed in a measuring cylinder, and a known volume of water is added and the total volume change is recorded. The calculation formula is ρ = m / (V2-V1), where m is the sample mass, V1 is the initial water volume, and V2 is the total volume after the sample is added, accurate to ±0.01 g / cm 3 ; The moisture content is determined by the drying method. Take 100g of the material sample and dry it to constant weight at 105±2℃. The calculation formula is w=(m1-m2) / m1×100%, where m1 is the mass before drying and m2 is the mass after drying, accurate to ±0.1%; the particle size distribution is determined by the standard screening method, using a 10-layer vibrating screen (the sieve hole sizes are 50mm, 30mm, 20mm, 10mm, 5mm, 3mm, 1mm, 0.5mm, 0.25mm, 0.125mm), a vibration time of 15 minutes, an amplitude of 2mm, a vibration frequency of 50Hz, record each sieve weight and calculate the cumulative undersize percentage, and draw a particle size distribution curve. For standard cement raw materials, the typical hardness value is 4-6 and the density value is 2.6-3.0g / cm 3 , moisture content is 3-8%, d 50 The particle size is 5-15mm. For cement raw material grinding, the target particle size distribution parameters are extracted from the enterprise production process specifications: the target particle size distribution is usually described by the Rosing-Rammler equation, which is in the form of R(d)=100·exp[-(d / d') n], wherein R(d) is the cumulative percentage of undersize material with particle size greater than d, d' is the characteristic particle size, n is the distribution uniformity index, the parameters d' and n are determined by fitting the actual sieve data by the least square method, for standard cement raw meal processing, the target d' value is 0.08-0.12 mm, and the n value is 0.8-1.2; the maximum particle size limit is taken as the particle size corresponding to R(d) = 5% in the sieve curve, which is usually controlled at 30-40 mm; the minimum particle size limit is taken as the particle size corresponding to R(d) = 95%, which is usually controlled at 0.002-0.005 mm. For ore grinding, the target particle size also needs to consider the feeding requirements of the subsequent sorting equipment, and the typical target is 70-80% passing through a 0.074 mm sieve hole. In actual operation, the particle size value corresponding to a specific sieve percentage is obtained in discrete sieve data by using a piecewise linear interpolation method. A mapping relationship matrix of material properties and roller pressure parameters is established. For the extracted target particle size distribution, the index matching coefficient λ = d 50 / (d_max-d_min) is calculated, wherein d 50 _max and d_min are the maximum and minimum particle size limits, respectively. Based on the λ value, the roller pressure P_line is determined, and the calculation formula is P_line = P_base·(1+2.5·λ), wherein P_base is the base line pressure, and for cement raw meal, the value is 950 kN / m. The linear speed v of the main roller is determined according to the material hardness index H_i, and the calculation formula is v = v_ref·(H_ref / H_i)^0.4, wherein v_ref is the reference speed 1.2 m / s, and H_ref is the reference hardness 5. The roller gap s is calculated according to the target maximum particle size d_max, s = 0.6·d_max, and for a maximum particle size limit of 30 mm, the roller gap is set to 18 mm. The feeding rate Q is calculated according to the roller press geometric parameters and operating parameters, Q = 60·π·D·L·s·ρ·v·η_fill, and the unit is t / h, wherein D is the main roller diameter (m), L is the main roller length (m), s is the roller gap (m), ρ is the material density (t / m 3), v is linear velocity (m / s), η fill is roll gap filling coefficient, taking value 0.4-0.6, specific value is determined by empirical formula η fill = 0.38+0.05·w+0.01·H i, w is material moisture content (%). Roll press-material interaction model is created in EDEM discrete element simulation environment. Material particle size distribution is set to follow Rosin-Rammler distribution, mathematical expression is f(d)=100·n·(d^(n-1) / d'^n)·exp[-(d / d')^n], wherein d' and n value are obtained from measured particle size distribution. When creating material particle model, set particle shape to polyhedral combination, single particle is combined by 3-7 basic spheres, combination mode is center packing type, overlap rate is 25-35%, to simulate irregular shape of material. Material cohesion coefficient is calculated according to moisture content w, formula is c=c0·(1+5·w), wherein c0 is reference cohesion, taking value 1.2×10 4 N / m 2 . Particle-particle friction coefficient μ pp is set to , which is material internal friction angle, determined by direct shear test, for standard cement raw meal, μ pp =0.45-0.60; Particle-roll surface friction coefficient μ pw is set according to roll surface material and roughness, for standard hard alloy roll surface, μ pw =0.35-0.45. Material elastic modulus E m is determined by compression test, calculation formula is E m =σ / ε, wherein σ is applied stress, ε is relative deformation, for cement raw meal, typical value is 2.0-4.5 GPa. Material Poisson's ratio is set to 0.25-0.30, recovery coefficient is set to 0.4-0.65, specific value is determined by rebound test. Through geometric transformation, main roll surface is reset to standard straight line state, i.e. radian deviation is 0 mm. During resetting, radial adjustment is made to each point P(r, θ, z) on main roll surface, so that , wherein corresponding to the average radius value at axial position z. In particular, all node coordinates of the main roller surface mesh are extracted, a mapping table of radial position and axial position is established, a least square method is used to fit the ideal cylindrical surface equation, the radial deviation of each node is calculated, and then it is adjusted to the ideal cylindrical surface through node displacement application. The working mode of the main roller is set to continuous pressing mode, and the specific working parameters are set according to the actual working condition: the pressing pressure is determined by the ratio of the hydraulic cylinder force F and the effective length L of the main roller, P_line = F / L, and the setting range is 500-2000 kN / m, and the specific value is set in stages, the starting stage is set to 600 kN / m, the transition stage is set to 1200 kN / m, and the stable running stage is set to 1600 kN / m; the main roller linear speed v is calculated according to the main roller rotating speed n and the main roller diameter D, v = π·D·n / 60, and the setting range is 0.5-1.5 m / s, corresponding to the rotating speed 8-23 rpm of the main roller with a typical diameter of 1.2 m. After all the working parameters are set, the model is preloaded and balanced to ensure that the system is in a static force balance state, and the preloaded balance convergence standard is that the residual force is less than 0.1% of the total load. The roller press operation parameters are input into the material-roller surface interaction simulation model, and the material-main roller stress parameterization simulation is performed. The simulation process sets multi-physics field coupling calculation: the discrete element module is responsible for simulating the interaction between material particles and the contact action between the material particles and the roller surface, and the time step is set to 2×10 -5 seconds to ensure calculation stability; the finite element module is responsible for calculating the stress distribution and deformation of the main roller under the action of the load, and the explicit integration method is adopted, and the time step is 1×10 -4 seconds. The two modules are coupled through interface data exchange, and the exchange frequency is once every 100 discrete element time steps. The following key data are recorded during the calculation process: the stress distribution σ(θ, z, t) of the main roller surface, including the radial stress σ_r, the hoop stress σ_θ and the axial stress σ_z; the deformation displacement u(θ, z, t) of the main roller, including the radial component u_r, the hoop component u_θ and the axial component u_z; the load distribution F(θ, z, t) of the main roller, reflecting the contact force distribution between the material and the roller surface; the material flow velocity field v(x, y, z, t) describing the motion state of the material in the roller gap. The simulation calculation continues until the system reaches a steady state, and the judgment standard is that the change rate of the maximum deformation of the main roller in the last 10 calculation periods is less than 1%. The final obtained simulation main roller stress state data includes: the stress time history curve, the deformation time history curve, the stress distribution cloud picture and the deformation distribution cloud picture of each monitoring point of the main roller, and the data accuracy is controlled within ±0.5%.

[0086] Especially important is that the material-main roller stress parameterization simulation further includes:

[0087] According to the material characteristic parameter data, the material is divided into coarse particle zone, medium particle zone and fine particle zone, and the proportion of different particle size regions is calculated; wherein, the coarse particle zone is set to 10-30mm, the medium particle zone is set to 5-10mm, and the fine particle zone is set to 0-5mm;

[0088] According to the material characteristic parameter data, the material is divided into high moisture zone, medium moisture zone and low moisture zone, and the distribution of different moisture regions is analyzed, wherein, the high moisture zone is set to 15-20%, the medium moisture zone is set to 8-15%, and the low moisture zone is set to 0-8%;

[0089] According to the material characteristic parameter data, the material is divided into high hardness zone, medium hardness zone and low hardness zone, and the distribution of different moisture regions is analyzed, wherein, the high hardness zone is set to 7-10 levels, the medium hardness zone is set to 4-6 levels, and the low hardness zone is set to 1-3 levels;

[0090] A simulation working condition matrix is created, which contains at least 27 different working condition combinations, wherein, the simulation working condition matrix is composed of 3 kinds of material particle size × 3 kinds of moisture content × 3 kinds of hardness;

[0091] Dynamic simulation analysis is carried out for each working condition combination, and the simulation time is set to 60 seconds, and the main roller bending deformation data is recorded once every 0.5 seconds;

[0092] The maximum deformation, average deformation and deformation rate of each monitoring point of the main roller in the simulation process are extracted to obtain the stress state data of the simulation main roller.

[0093] In the embodiment of the present application, the material is divided into three regions according to the particle size range: the material with a particle size range of 10-30 mm is divided into a coarse particle region, the material with a particle size range of 5-10 mm is divided into a medium particle region, and the material with a particle size range of 0-5 mm is divided into a fine particle region. The proportion of each particle size region is calculated, and the mass fraction is used to represent it, and the calculation formula is W_i=(m_i / m_total)×100%, wherein W_i is the mass percentage of the i-th particle size region, m_i is the mass of the i-th particle size region, and m_total is the total mass of the material. In the specific calculation, the cumulative percentage of sieve undersize corresponding to the key particle size is read from the particle size cumulative distribution curve using the sieve test data: P1 is the cumulative percentage of sieve undersize corresponding to 5 mm, P2 is the cumulative percentage of sieve undersize corresponding to 10 mm, and P3 is the cumulative percentage of sieve undersize corresponding to 30 mm. Then, the proportion of the fine particle region is P1%, the proportion of the medium particle region is (P2-P1)%, the proportion of the coarse particle region is (P3-P2)%, and the proportion of the super coarse particle greater than 30 mm is (100-P3)%. For standard cement raw materials, the typical distribution is that the fine particle region accounts for 35-45%, the medium particle region accounts for 25-35%, the coarse particle region accounts for 20-30%, and the super coarse particle accounts for 0-5%. In the discrete element model, the corresponding number of particles is created according to these proportions to ensure that the particle size distribution in the simulation is consistent with the actual material. The Karl Fischer moisture meter is used to accurately measure the moisture content of the material sample, and the measurement accuracy is ±0.1%. According to the moisture content range, the material is divided into three moisture regions: the material with a moisture content of 15-20% is divided into a high moisture region, the material with a moisture content of 8-15% is divided into a medium moisture region, and the material with a moisture content of 0-8% is divided into a low moisture region. The sample is analyzed by stratified sampling, and the spatial distribution of different moisture regions is determined. The columnar sampler is used to extract samples from different positions and depths of the material pile, and the three-dimensional coordinates (x, y, z) and the moisture content w(x, y, z) are recorded at each sampling point. Based on the measurement data, a three-dimensional moisture distribution model is constructed, and a trilinear interpolation algorithm is used to calculate the moisture content at any position: w(x, y, z)=Σ (i＝1) 8w_i·N_i(x, y, z), where w_i is the water content value of the adjacent 8 sampling points, N_i(x, y, z) is a shape function, and the calculation formula is N_i(x, y, z) = (1±ξ)(1±η)(1±ζ) / 8, ξ, η, ζ are normalized coordinates. In the cement raw meal treated under standard conditions, the typical distribution is that the high moisture zone accounts for 5-15%, the medium moisture zone accounts for 40-60%, and the low moisture zone accounts for 30-50%. The hardness of 100 randomly selected material particles was determined, the measurement load was 500 g, and the holding time was 15 seconds. The hardness value of each measurement point was recorded, and a hardness value frequency distribution histogram was established. According to the Mohs hardness grade, the material was divided into three hardness zones: the material with a hardness of 7-10 grade was divided into a high hardness zone, the material with a hardness of 4-6 grade was divided into a medium hardness zone, and the material with a hardness of 1-3 grade was divided into a low hardness zone. The proportion of each hardness zone was calculated, and the frequency statistical method was used, and the calculation formula was P_i = n_i / N×100%, where P_i was the percentage of the i-th hardness zone, n_i was the number of particles in the i-th hardness zone, and N was the total number of test particles. For multi-mineral combined materials, X-ray diffraction (XRD) analysis was used to determine the mineral composition, and then the weighted average hardness was calculated according to the standard hardness value of each mineral and the content proportion. The spatial distribution of different hardness regions was analyzed, the correlation with mineral components and particle size was studied, and a hardness distribution probability density function f(H) = dP / dH was established, where P was the cumulative percentage, and H was the hardness value. In the standard cement raw meal, the typical hardness distribution was that the high hardness zone accounted for 15-25%, the medium hardness zone accounted for 50-70%, and the low hardness zone accounted for 15-25%. The simulation working condition matrix dimension was 3×3×3, corresponding to different levels of material particle size (coarse particle zone dominant, medium particle zone dominant, fine particle zone dominant), water content (high moisture, medium moisture, low moisture), and hardness (high hardness, medium hardness, low hardness). The material particle size combination design was: coarse particle zone dominant type (coarse particle zone 60%, medium particle zone 25%, fine particle zone 15%), medium particle zone dominant type (coarse particle zone 25%, medium particle zone 50%, fine particle zone 25%), and fine particle zone dominant type (coarse particle zone 15%, medium particle zone 25%, fine particle zone 60%). The water content combination design was: high moisture type (average water content 17%, range 15-20%), medium moisture type (average water content 11%, range 8-15%), and low moisture type (average water content 4%, range 0-8%). The hardness combination design was: high hardness type (average hardness 8 grade, range 7-10 grade), medium hardness type (average hardness 5 grade, range 4-6 grade), and low hardness type (average hardness 2 grade, range 1-3 grade). Each working condition combination was marked as "(P_i)(W_j)(H_k)", for example, "(P1)(W2)(H3)" represented a combination of coarse particle zone dominant, medium moisture, and low hardness.Standard roller press operating parameters were set for all 27 operating conditions: the main roller speed was set to 15 rpm, corresponding to a linear velocity of approximately 0.95 m / s; the pressing pressure was set to 1200 kN / m; and the roller gap was set to 15 mm. A working condition-parameter mapping table was established. For each monitoring point, three key deformation indicators were extracted: maximum deformation u_max, defined as the absolute maximum value of the radial displacement at that point during the entire simulation process, calculated as u_max = max|u_r(t)|, t∈[0, 60 s]; and average deformation u_avg, defined as the time-averaged radial displacement at that point during the stable operation phase (30-60 s), calculated as u_avg = (1 / T)∫. 30 60 u_r(t)dt, where T = 30s; the rate of change of deformation R_u, defined as the ratio of the standard deviation of the deformation in the stable stage to the average value, is calculated as R_u = σ_u / |u_avg| × 100%, where σ_u = √[(1 / T)∫ 30 60 (u_r(t)-u_avg) 2 dt]. The deformation data from all monitoring points is spatially analyzed and a deformation cloud diagram is plotted. The horizontal axis represents axial position, the vertical axis represents circumferential position, and the color represents the degree of deformation. The overall bending degree of the main roll is calculated: the axial bending index B_axial, which is defined as the ratio of the difference between the average deformation at both ends and the deformation at the center to the main roll diameter. The calculation formula is B_axial = |(u_ends - u_center) / D| × 100%, where u_ends is the average deformation of the monitoring points at both ends, u_center is the deformation of the central monitoring point, and D is the main roll diameter.

[0094] As an example of the present invention, refer to Figure 2 As shown, Figure 1 Detailed implementation steps of step S4 are shown in the flowchart. In this example, step S4 includes:

[0095] Step S41: continuously monitoring the extrusion pressure values ​​at the middle and both ends of the main roller, and performing difference calculation between the extrusion pressure value at the middle and both ends of the main roller to obtain the extrusion pressure distribution data of the main roller;

[0096] In the embodiment of the present application, when continuously monitoring the extrusion force values of the middle part and both ends of the main roller, 11 high-precision pressure sensors are arranged on the working surface of the main roller along the axial direction, wherein 5 are arranged in the middle part region (within 30% of the middle part of the length of the main roller) and numbered C1 to C5; 3 are arranged in each of the two end part regions (within 35% of each end of the main roller) and numbered L1 to L3 on the left end and R1 to R3 on the right end. The sampling frequency of each sensor is set to 200 Hz, and the data acquisition system records the pressure time sequence P_i(t), i is the sensor number. The sensors are installed 3 mm below the surface of the main roller, with a measurement accuracy of ±0.5%. The average extrusion force value of the middle part region P_center is calculated as (P_C1+P_C2+P_C3+P_C4+P_C5) / 5, and the average extrusion force value of the two end part regions P_ends is calculated as (P_L1+P_L2+P_L3+P_R1+P_R2+P_R3) / 6. When performing difference calculation, the extrusion force difference ΔP between the middle part and the two end parts is used, and the extrusion force difference ΔP_LR between the left and right ends is used. The difference value is normalized, ΔP_norm=ΔP / P_avg×100%, wherein P_avg is the average extrusion force of the whole roller surface. The change curve of ΔP_norm with time is recorded in real time, and the mean value μ_ΔP and the standard deviation σ_ΔP under steady state working condition (running time greater than 30 minutes) are calculated.

[0097] Step S42: labeling the extrusion force concentration region of the main roller extrusion force distribution data, and dividing the extrusion force concentration region into extrusion force peak points;

[0098] In the embodiment of the present application, the extrusion force high value threshold T_high=P_avg+1.5σ_P and the extrusion force low value threshold T_low=P_avg-1.5σ_P are set, wherein P_avg is the average extrusion force and σ_P is the extrusion force standard deviation. The extrusion force value P(z_i) of each measurement position z_i is judged: when P(z_i) is greater than T_high, the position is marked as a high pressure region; when P(z_i) is less than T_low, the position is marked as a low pressure region; the rest of the positions are marked as normal pressure regions. The continuous high pressure region forms an extrusion force concentration region, and the range (start position z_start to end position z_end), length L_conc=z_end-z_start, average pressure P_conc_avg and maximum pressure P_conc_max of each concentration region are calculated, and the extrusion force concentration region is divided into extrusion force peak points.

[0099] Step S43: according to the extrusion force peak points, the stress parameters around the extrusion force peak points are monitored with a scanning interval of 5 cm, and the main roller stress diffusion data are obtained.

[0100] In the embodiment of the present application, for each identified extrusion force peak point position z_peak, measurement points are set at intervals of 5 cm to the left and right of the point: z_peak±5 cm, z_peak±10 cm, z_peak±15 cm, etc., until the extrusion force value drops to 20% of the peak value or reaches the end of the main roller. At each measurement point z_i, the following force parameters are recorded: extrusion force value P(z_i), radial deformation amount δ_r(z_i), circumferential strain ε_θ(z_i), and axial strain ε_z(z_i). The radial deformation amount is measured by a displacement sensor, and the strain values are measured by surface strain gauges.

[0101] Step S44: Perform diffusion gradient identification on the main roller force diffusion data, and perform force distribution evaluation on the diffusion gradient to obtain main roller force distribution data;

[0102] In the embodiment of the present application, a force diffusion space distribution map is constructed, with the horizontal axis being the axial position z of the main roller and the vertical axis being the diffusion parameters (extrusion force P, radial deformation δ_r, circumferential strain ε_θ, and axial strain ε_z), and the height of the curved surface representing the parameter value. Cluster analysis is performed on the gradient field, and a K-means clustering algorithm is used to divide the surface of the main roller into k gradient regions (usually k = 3-5), and the gradient characteristics in each region are similar. The diffusion gradient is evaluated for force distribution, and the following indexes are calculated: gradient uniformity index wherein and are the standard deviation and mean value of the gradient size, respectively; direction consistency index with a value range of [0, 1], and the larger the value, the more consistent the gradient direction; diffusion symmetry index wherein α_left and α_right are the diffusion coefficients on the left and right sides of the peak point, respectively.

[0103] Step S45: Cluster the main roller arc abnormal stress points according to the simulated main roller stress state data to obtain potential main roller arc deformation regions;

[0104] In the embodiment of the present application, the following key parameters are extracted from the stress state data: von Mises stress σ_vm, principal stresses σ_1, σ_2, σ_3, stress ratio σ_r = σ_1 / σ_3, and stress gradient The abnormal stress determination criteria are set as follows: σ_vm is greater than 0.6σ_yield (wherein σ_yield is the material yield strength) or σ_r is greater than 5 or greater than 10 MPa / mm. Mark the points that meet any of the conditions as abnormal stress points, forming an initial abnormal point set P = {p_1, p_2,..., p_n}. Perform abnormal point clustering using a density-based spatial clustering algorithm, and each cluster represents a potential arc deformation region. Calculate the feature indicators for each cluster: area A_i (the surface area covered by the cluster), average stress σ_avg_i (the average von Mises stress of the points in the cluster), maximum stress σ_max_i (the maximum von Mises stress in the cluster), stress concentration coefficient K_t.

[0105] Step S46: Perform deformation degree evaluation on the potential main roller arc deformation region based on the main roller extrusion force distribution data, and divide it into a severe deformation region and a slight deformation region;

[0106] In the embodiment of the present application, the area average stress σ_avg (weight w_σ = 0.3), the maximum deformation δ_max (weight w_δ = 0.25), the stress concentration coefficient K_t (weight w_K = 0.2), the deformation duration t_def (weight w_t = 0.15) and the area A (weight w_A = 0.1). Each indicator is scored according to the 0-10 point system, and the deformation region is divided into two categories according to the comprehensive score: severe deformation region (S >= 6.5) and slight deformation region (3.5 <= S < 6.5), and the region with a score lower than 3.5 does not need special treatment. Generate a detailed evaluation report for each region, including region boundary coordinates, center position, coverage range, indicator score and comprehensive score. A red marker line is applied to the boundary of the severe deformation region, a yellow marker line is applied to the slight deformation region, and the high-light region is displayed on the three-dimensional model of the main roller.

[0107] Step S47: According to the severe deformation region and the slight deformation region, the main roller bending compensation is performed to obtain the main roller bending compensation parameters; wherein the hydraulic support device of the severe deformation region is increased by 20-50kN compensation force, and the hydraulic support device of the slight deformation region is increased by 5-20kN compensation force.

[0108] In the embodiment of the present application, the number of support devices in the influence range of each deformation region is determined. For a severely deformed region, the center position z_center of the region is identified, the two closest hydraulic support devices i and i+1 are found, and the distance ratio λ=(z_center-z_i) / (z_{i+1}-z_i) is calculated, where z_i and z_{i+1} are the axial positions of the two support devices. The compensation force distribution coefficients k_i=1-λ and k_{i+1}=λ are determined. The total compensation force F_comp_severe required by the severely deformed region is calculated as F_comp_severe=E·I·κ_max·L / (δ_max·c), where E is the elastic modulus, I is the cross-sectional moment of inertia, κ_max is the maximum curvature, L is the length of the deformation region, δ_max is the maximum deformation, and c is the compensation coefficient (taking a value of 1.2-1.5). The compensation force is determined according to the deformation amount by the formula ΔF(kN)=20+30×(δ_max / δ_allow). For a slightly deformed region, a similar method is used, but the compensation force is reduced, and an additional compensation force of 5-20 kN is added, with the calculation formula being ΔF(kN)=5+15×(δ_max / δ_allow). The compensation force is applied through a hydraulic control system.

[0109] Especially important is that the main roller bending compensation further comprises, before the main roller bending compensation:

[0110] Marking the hardware unit of the main roller of the roller press, wherein the hardware unit comprises a main roller body module, a hydraulic support unit module, and a pressure sensor module;

[0111] Analyzing the main roller body working face width, the rated pressure range, and the radial deformation allowance value according to the main roller body module;

[0112] Extracting the support position distribution data and the support piston stroke amount according to the hydraulic support unit module;

[0113] Mapping the support piston stroke amount to the position stroke capacity through the support position distribution data to obtain a position stroke mapping value;

[0114] Detecting the pressure level adaptation degree of the rated pressure range based on the position stroke mapping value, and then parameterizing the adaptation relationship of the hydraulic support unit module through the pressure level adaptation degree to generate hydraulic support adaptation data;

[0115] Analyzing the pressure detection precision of the pressure sensor module and mapping the constraint relationship to generate pressure sensor constraint data;

[0116] Determining the rated arc limit based on the main roller body working face width and the radial deformation allowance value to obtain main roller arc limit data;

[0117] The main roller bending compensation control mechanism is constructed based on the hydraulic support adaptation data, the pressure sensor constraint data and the main roller arc limit data.

[0118] In the embodiment of the present application, the main roller hardware unit of the roller press is accurately marked, the hardware unit is divided into a main roller body module, a hydraulic support unit module and a pressure sensor module, and each module is assigned a unique identification code. Subsequently, the main roller body module is analyzed in shape by a precision measuring device, the effective width of the main roller working surface is determined, the rated pressure bearing range of the main roller is calculated according to the material strength characteristics, and the allowable limit value of the radial deformation of the main roller under working conditions is calculated by the elastic deformation theory. Then, for the hydraulic support unit module, the accurate distribution position data of each support unit in the axial direction of the main roller is extracted, the maximum stroke amount of each support piston is measured, and the rated support force and working pressure values of each support unit are recorded. The piecewise linear function method is used to establish a mapping relationship between the support position and the piston stroke amount, forming a continuous position-stroke mapping curve, ensuring that each position point on the axial direction of the main roller has a corresponding value of the support capacity. Based on the obtained position-stroke mapping value, dynamic pressure response testing is performed, the ratio of the actual support pressure to the theoretical required pressure is recorded under various working conditions, the pressure grade adaptation degree is detected, and the hydraulic support system is parameterized adjusted accordingly, generating a complete hydraulic support adaptation data table. At the same time, the pressure sensor module is subjected to standard pressure calibration, the linearity, repeatability and hysteresis error of each sensor are detected, the accurate mapping relationship between the sensor readings and the actual pressure is established, and a pressure sensor constraint data matrix is formed. According to the geometric characteristics and material properties of the main roller, the rated arc limit of the main roller under various working conditions is calculated and determined, and a main roller arc limit data table is generated. Finally, the hydraulic support adaptation data, the pressure sensor constraint data and the main roller arc limit data are integrated to construct a closed-loop control system with a triple protection mechanism, realizing real-time monitoring and accurate compensation of the arc state of the main roller, and ensuring that the main roller always maintains the best working arc during the rolling process.

[0119] Preferably, the present application also provides a parameterized simulation system for the arc of the main roller of the roller press, which executes the parameterized simulation method for the arc of the main roller of the roller press as described above, and the parameterized simulation system for the arc of the main roller of the roller press comprises:

[0120] The main roller arc scanning module is configured to perform surface space scanning on the main roller of the roller press to obtain main roller space parameters, and perform multiple pre-measurements on the main roller of the roller press under various working conditions to generate main roller running state data.

[0121] The arc analysis module is configured to divide the main roller into sections according to the main roller running state data, analyze the arc deviation of the main roller, obtain main roller arc deviation data, calculate the arc change rate of the main roller according to the main roller arc deviation data, determine the actual working state of the main roller, and evaluate the arc distribution of the main roller according to the main roller arc deviation data to obtain a main roller arc distribution map.

[0122] a stress simulation module, configured to divide the main roller arc distribution map into regions by a main roller arc rate of change, and mark out arc stress points; import the main roller spatial parameters and the arc stress points into a mechanics analysis software to construct a main roller initial state model of the roller press; acquire roller material data; perform material-main roller stress parameterization simulation according to the roller material data and the main roller initial state model of the roller press, and obtain simulation main roller stress state data;

[0123] a bending compensation module, configured to perform intelligent main roller bending compensation based on the simulation main roller stress state data, and obtain main roller bending compensation parameters.

[0124] Preferably, the application further provides a computer readable storage medium, which stores a computer program, the computer program is executed to realize the parameterization simulation method of the main roller arc of the roller press as any one of the above.

[0125] The application can realize active control of the main roller state of the roller press, and avoids secondary damage or performance decline caused by improper compensation. Actual application shows that after the method is adopted, the product qualified rate is increased by more than 15%, the energy consumption is reduced by about 12%, the service life of the main roller is prolonged by more than 30%, and the annual economic benefits are significantly improved. At the same time, the method reduces the dependence on manual experience, reduces the maintenance cost, improves the equipment reliability, provides technical support for intelligent and fine operation management of the roller press, and has a wide application prospect. Therefore, the parameterization simulation method of the main roller arc of the roller press can significantly improve the control level of the main roller arc deviation through the closed-loop logic of real-time monitoring, accurate analysis, simulation prediction and intelligent compensation, greatly improve the stability and consistency of the product quality, effectively reduce the energy consumption caused by uneven pressure, significantly prolong the service life of the main roller and related components by reducing local overload and optimizing stress, reduce the unplanned downtime and maintenance cost, and finally improve the efficiency and economic benefits of the entire roller pressing production process.

[0126] Therefore, from any point of view, the embodiments should be regarded as exemplary and non-limiting, the scope of the application is defined by the appended claims rather than the above description, and therefore all changes falling within the meaning and scope of the equivalent elements of the application file are intended to be included in the application.

[0127] The foregoing is considered as illustrative only of the principles of the application. Numerous modifications and changes will readily occur to those skilled in the art, and it is intended to embrace all such modifications and changes that fall within the scope of the application. Accordingly, the application is not to be restricted in scope to the specific embodiments disclosed herein but is to be accorded the full scope that the principles and novel features request appropriately granted.

Claims

1. A parametric simulation method for the radian of the main roller of a roller press, characterized in that: The following steps are involved: Step S1: Scanning the surface space of the main roller of the roller press to obtain the spatial parameters of the main roller; performing multiple predictions on the working conditions of the main roller of the roller press to generate the operating status data of the main roller; Step S2: dividing the main roll into sections according to the main roll operating status data, and then performing a main roll radian deviation analysis to obtain main roll radian deviation data; calculating the main roll radian change rate according to the main roll radian deviation data; determining the actual working status of the main roll, and performing a main roll radian distribution evaluation according to the main roll radian deviation data to obtain a main roll radian distribution diagram; wherein, dividing the main roll into sections according to the main roll operating status data and then performing a main roll radian deviation analysis in step S2 includes: The main roller operation status data is divided into main roller axial measurement data and main roller surface circumferential measurement data, and the main roller axial measurement data and main roller surface circumferential measurement data are obtained respectively; The main roll measurement points are divided based on the main roll spatial parameters. The main roll is divided into three sections according to the axial position: the end sections and the center section. The main roll sections are obtained. The end sections are 35% of the total length of the main roll, and the center section is 30% of the total length of the main roll. Calculate the main roller axial arc value of each section of the main roller based on the main roller axial measurement data; Analyze the circumferential torque value of each section of the main roller based on the circumferential measurement data of the main roller surface; Perform time series alignment of the temperature values ​​of the circumferential measurement data of the main roller surface to obtain the circumferential temperature value time series data; Based on the main roller axial curvature value of each section of the main roller, the curvature difference between the central area and the two end areas of the main roller is calculated to obtain the central-end curvature difference; According to the circumferential temperature value time series data and the circumferential torque value of each section of the main roller, the section value comparison is carried out and arranged according to the value gradient to obtain the main roller temperature-torque gradient data; The main roller radian deviation data is obtained by integrating the center-end radian difference with the main roller temperature-torque gradient data; Step S3: Divide the main roller radian distribution map into regions based on the main roller radian change rate and mark the radian stress points; import the main roller spatial parameters and radian stress points into mechanical analysis software to construct an initial state model of the roller press main roller; obtain roller press material data; perform material-main roller stress parameterization simulation based on the roller press material data and the roller press main roller initial state model to obtain simulated main roller stress state data; Step S4: performing intelligent main roller bending compensation based on the simulated main roller stress state data to obtain main roller bending compensation parameters.

2. The parametric simulation method for the radian of the main roller of the roller press according to claim 1, characterized in that: Step S1 includes the following steps: Step S11: Scan the surface of the main roller of the roller press with a laser ranging device to obtain the coordinates of the main roller surface in three-dimensional space. The scanning accuracy is ±0.05mm, the scanning interval is 10mm in the axial direction and 5° in the radial direction, and the coordinates are recorded as the main roller surface coordinate data. Step S12: determining the axial position and radial size of the main roller according to the surface coordinate data of the main roller; Step S13: encoding the geometric information of the main roller of the roller press through the axial position and radial size to obtain the spatial parameters of the main roller; Step S14: 7 measuring points are evenly arranged along the axial direction of the main roller of the roller press, and 4 measuring points are set along the circumference of the main roller surface at each measuring point to obtain the measuring point data of the main roller; Step S15: deploying temperature sensors, eddy current displacement sensors, and torque strain gauges based on the main roller measurement point data, thereby building a main roller measurement network; Step S16: Using the main roller measurement network, the working condition of the main roller of the roller press is predicted multiple times to obtain original main roller measurement data; wherein, the working condition multiple prediction measurement includes three acquisitions, the first acquisition is performed 10 seconds after the roller press is started without load, and the acquisition time is 5 seconds; the second acquisition is performed 10 seconds after the material enters the roller gap after loading, and the acquisition time is 5 seconds; the third acquisition is performed after 5 minutes of stable operation, and the acquisition time is 10 seconds; the original main roller measurement data includes the main roller speed value, the main roller torque value and the main roller temperature value; Step S17: performing digital filtering processing on the original main roller measurement data to generate main roller operation status data.

3. The parametric simulation method for the radian of the main roller of the roller press according to claim 1, characterized in that: In step S2, the main roller radian change rate is calculated based on the main roller radian deviation data; Determine the actual working status of the main roller, and evaluate the main roller radian distribution based on the main roller radian deviation data. The main roller radian distribution diagram includes: Perform time series analysis on the center-end radian difference in the main roller radian deviation data, and calculate the change rate of the main roller radian difference at different time points to obtain the main roller radian change rate; Determine whether the main roller has axis deviation based on the main roller spatial parameters, and mark the section where the deviation exceeds 0.05mm to obtain the main roller deviation section data; Determine the actual working state of the main roller and divide the working state into three states: full load, half load and light load; Based on the main roller deviation section data and the main roller temperature-torque gradient data in the main roller radian deviation data, the radian deformation of the main roller under three working conditions is measured to obtain the main roller radian deformation data; Determine the local curvature data of the main roller according to the curvature deformation data of the main roller; Conduct radian field statistics on the local radian data of the main roller and detect the radian field distribution data; Import the radian field distribution data into the drawing tool, and map the data points into a two-dimensional coordinate system, where the coordinate axes represent the physical position and radian value of the main roller, respectively, to obtain the main roller radian distribution diagram.

4. The parametric simulation method for the radian of the main roller of the roller press according to claim 1 is characterized in that: In step S3, the main roller radian distribution diagram is divided into regions according to the main roller radian change rate, and the radian force points are marked, including: The main roller radian distribution map is divided into regions according to the radian deviation range by the main roller radian change rate to obtain radian gradient regions; wherein the radian deviation range of each region is set to 0.02mm; The radian difference between adjacent measuring points is measured in each radian gradient region, and the ratio of the radian difference to the distance between adjacent measuring points is calculated to obtain radian-distance difference ratio data; If the radian-distance difference ratio data is greater than 0.01 mm / cm, it is preliminarily determined that the area corresponding to the radian-distance difference ratio data is the starting area of ​​the radian force point; The direction of arc stress transmission is determined based on the starting area of ​​the arc stress point; if the main roll deviation section is close to the middle of the roller press, the stress transmission direction is from the middle to both ends; if the main roll deviation section is close to one end of the roller press, the stress transmission direction is from that end to the other end; Compare the curvature differences between curvature gradient areas to determine the stress magnitude; The stress transfer direction and stress magnitude are marked on the main roller radian distribution diagram, and the marked stress transfer path is superimposed on the main roller radian distribution diagram to generate the radian stress point.

5. The parametric simulation method for the radian of the main roller of the roller press according to claim 1, characterized in that: In step S3, the main roller spatial parameters and arc force points are imported into the mechanical analysis software to construct the initial state model of the roller press main roller, including: Import the main roller spatial parameters and arc force points into the mechanical analysis software, and build the main roller 3D geometric model based on the main roller spatial parameters; According to the 3D geometric model of the main roller, the main roller surface is set as the force grid node, and the fixed end, free end and support structure of the main roller are determined, and the position and size of each key section of the main roller are marked; Set the arc stress point as the load application point and set the stress transfer parameters between nodes; The force-bearing points of the arc on the surface of the main roller are force-associated; if the main roller is in a fully loaded state, the force distribution is evenly connected; if the main roller is in a half-loaded or lightly loaded state, the force distribution is unevenly distributed, so as to construct the initial state model of the main roller of the roller press.

6. The parametric simulation method for the radian of the main roller of the roller press according to claim 1, characterized in that: In step S3, the material-main roller stress parameterized simulation is performed based on the roller pressing material data and the initial state model of the roller press main roller, including: According to the roller-pressed material data, the hardness value, density value, moisture content, and particle size distribution of the roller-pressed material are obtained as material characteristic parameter data; Extract target particle size distribution, maximum particle size limit, and minimum particle size limit based on roller-pressed material data as grinding task specification data; Match roller press operating parameters according to grinding task specification data; The material characteristic parameter data is used to set the material discrete element parameters of the initial state model of the roller press main roller, including the material particle size distribution, cohesion coefficient, friction coefficient, and elastic modulus. A material-roller surface interaction simulation model of the roller pressing process is constructed. The material particle size distribution follows the Rosing-Rammler distribution, and the friction coefficient includes the particle-particle friction coefficient and the particle-roller surface friction coefficient. Set the initial curvature of the main roller and set the initial curvature to the standard straight line state, that is, the curvature deviation is 0mm; set the main roller working mode to continuous pressing, and set the pressing pressure to 500-2000kN / m and the main roller linear speed to 0.5-1.5m / s; Start the calculation function of the mechanical analysis software, input the roller press operating parameters into the material-roller surface interaction simulation model to perform material-main roller stress parametric simulation, and obtain the simulated main roller stress state data.

7. The parametric simulation method for the radian of the main roller of a roller press according to claim 1, characterized in that: Step S4 includes the following steps: Step S41: continuously monitoring the extrusion pressure values ​​at the middle and both ends of the main roller, and performing difference calculation between the extrusion pressure value at the middle and both ends of the main roller to obtain the extrusion pressure distribution data of the main roller; Step S42: marking the squeeze force concentration area of ​​the main roller squeeze force distribution data, and dividing the squeeze force concentration area into squeeze force peak points; Step S43: monitoring the force parameters around the peak point of the extrusion force at a scanning interval of 5 cm based on the peak point of the extrusion force, and obtaining the force diffusion data of the main roller; Step S44: performing diffusion gradient identification on the main roller force diffusion data, and performing force distribution evaluation on the diffusion gradient to obtain the main roller force distribution data; Step S45: clustering abnormal stress points of the main roller radian according to the simulated main roller stress state data to obtain potential main roller radian deformation areas; Step S46: evaluating the deformation degree of the potential main roller radian deformation area based on the main roller extrusion force distribution data, and dividing it into a severe deformation area and a slight deformation area; Step S47: Perform main roller bending compensation according to the severely deformed area and the slightly deformed area to obtain main roller bending compensation parameters; wherein, the compensation force of the hydraulic support device in the severely deformed area is increased by 20-50kN, and the compensation force of the hydraulic support device in the slightly deformed area is increased by 5-20kN.

8. A parametric simulation system for the radian of the main roller of a roller press, characterized in that: The parametric simulation method for the main roller radian of a roller press according to claim 1 is used, and the parametric simulation system for the main roller radian of the roller press comprises: The main roller arc scanning module is used to perform surface space scanning on the main roller of the roller press to obtain the main roller space parameters; it performs multiple predictions on the working conditions of the main roller of the roller press to generate the main roller operating status data; The radian analysis module is used to divide the main roll into sections based on the main roll operating status data, and then perform radian deviation analysis on the main roll to obtain radian deviation data; calculate the radian change rate of the main roll based on the radian deviation data; determine the actual working status of the main roll, and evaluate the radian distribution of the main roll based on the radian deviation data to obtain a radian distribution diagram of the main roll; The stress simulation module is used to divide the main roller radian distribution map into regions based on the main roller radian change rate and mark the radian stress points; import the main roller spatial parameters and radian stress points into the mechanical analysis software to construct the initial state model of the roller press main roller; obtain the roller press material data; and perform a parametric simulation of the material-main roller stress based on the roller press material data and the roller press main roller initial state model to obtain the simulated main roller stress state data; The bending compensation module is used to perform intelligent main roller bending compensation based on the simulated main roller stress state data to obtain the main roller bending compensation parameters.

9. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed, the parameterized simulation method for the radian of the main roller of the roller press according to any one of claims 1 to 7 is implemented.

Images