Automatic control method for mobile crusher

Abstract

The invention provides an automatic control method for a mobile crusher, which comprises the following steps: acquiring the large particle size ratio, the medium particle size ratio, the small particle size ratio, the current roller spacing and the current feeding flow of a to-be-crushed material of the mobile crusher in real time, and identifying the surface adhesion coefficient of the material according to the water content of the material, extracting a distribution span value reflecting the difference between the agglomerating degree and the discrete particle span; according to the comparison result of the distribution span value and the crushing range threshold value of the mobile crusher, the risk level of mutual extrusion and blockage of the materials in the crushing channel is evaluated, and the high-risk clamping stagnation state when the distribution span value exceeds the crushing range threshold value is identified; the feeding flow is reduced according to the high-risk clamping stagnation state and the water content of the materials, the large-particle-size proportion, the medium-particle-size proportion and the small-particle-size proportion after the feeding flow is adjusted are collected, and the change track of the agglomeration degree and the change direction of the distribution span value are recognized.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of information technology, and particularly relates to a mobile crusher automatic control method. BACKGROUND

[0002] In the field of industrial production, the automation control technology of mobile crushers is particularly crucial, as it directly affects the efficiency of material processing and the quality of the products, and is an indispensable part of modern manufacturing. Mobile crushers are commonly used in on-site resource recycling of construction waste, temporary mining, disaster emergency rescue, and aggregate production in engineering sites. With the increasing demand of industry, the control accuracy and adaptability of crushers have become important standards for measuring the performance of the equipment. The composition, moisture content, and particle size distribution of the material in the field vary significantly, and often contain impurities. Open-air operations are greatly affected by the weather, and the moisture and adhesion of the material change in real time with rainfall and temperature, which puts higher demands on the real-time response capability of the control system. Due to the limitations of space and weight on the vehicle, the capacity of the crushing chamber is limited, and the power fluctuation of the power system (such as diesel engine) is relatively large, so high-efficiency and stable crushing must be achieved under limited resources. In the resource recycling and building material processing industries, the crushing effect directly affects the smooth progress of the subsequent process. However, many current control methods often struggle to cope with dynamic changes in the environment when faced with complex material characteristics, especially when the particle size distribution of the material varies greatly. Traditional control strategies are prone to difficulties. These methods usually cannot be flexibly adjusted according to the real-time state of the material, resulting in unstable crushing effect and even a decrease in the efficiency of the equipment. Especially when dealing with mixed materials, the control system lacks the ability to deeply perceive and respond to the characteristics of the material, making the equipment seem inadequate when faced with diverse materials. The roller spacing, as a key control factor, directly affects the degree of material crushing and the uniformity of the final product particles, but when the proportion of large, medium, and small particles in the material varies greatly, simply reducing the spacing often has the opposite effect. The reason is that when the spacing is too small, particles can easily squeeze each other in the narrow space, causing congestion and hindering the normal crushing of larger particles, resulting in poor overall crushing effect and even the risk of equipment jamming. Specifically, this problem is particularly evident in actual production. For example, when processing construction waste, the material often contains wet and sticky materials of different sizes and fine sand. If the roller spacing is set too small, the medium-sized particles will accumulate in the channel, not only preventing effective crushing, but also blocking the entry of larger particles, ultimately reducing the proportion of small particles produced, and seriously affecting the quality consistency of the product. This contradiction caused by the mismatch between material distribution characteristics and equipment control parameters has become a difficult problem that needs to be solved in automation control. Therefore, how to dynamically adjust the matching relationship between roller spacing and feed speed according to the real-time changes in the particle size distribution of the material during the crushing process to avoid congestion and ensure the uniformity of the crushed particles has become a key problem in current applications. SUMMARY

[0003] The application is particularly suitable for mobile crushers, as they often face variable working conditions, complex material sources, and large fluctuations in water content, etc. The static control strategy of traditional fixed crushers is difficult to adapt to. The application significantly improves the adaptability and stability of mobile crushers in field operations, construction waste site crushing, etc. through real-time sensing and dynamic adjustment. The application provides a mobile crusher automatic control method, mainly including: Obtaining particle size distribution data and operating parameter data of the material to be crushed, and determining the distribution span value of the material; According to the comparison result of the distribution span value and the preset threshold value, the risk level of the material in the crushing channel is evaluated, and the high-risk jamming state is identified; According to the high-risk jamming state, adjust the feed flow, monitor the change trajectory of the particle size distribution of the material after adjustment and the change direction of the distribution span value; According to the change direction, determine the matching combination scheme of the roller spacing target value and the feed flow target value; Apply the set value in the matching combination scheme to modify the running state, and evaluate the uniformity of the particle size distribution of the crushed product; According to the uniformity evaluation result, adjust or maintain the set value, and output the particle size consistency control result of the crushed product.

[0004] Further, the obtaining particle size distribution data and operating parameter data of the material to be crushed, and determining the distribution span value of the material, includes: Scan the material at the inlet of the mobile crusher, obtain the real-time data of the large particle size ratio, the medium particle size ratio and the small particle size ratio, and read the current roller spacing value and the feed flow value from the device sensor; Detect the water content value of the material, calculate the surface adhesion coefficient according to the water content value, judge the adhesion and aggregation state of gravel and fine sand according to the surface adhesion coefficient, and identify the volume ratio of the wet adhesion formed by the lump; From the lump volume ratio data and the real-time data of the large particle size ratio, the medium particle size ratio and the small particle size ratio, calculate the discrete particle span difference value according to the difference between the large particle size ratio and the small particle size ratio, and obtain the distribution span value by weighted sum of the discrete particle span difference value and the lump volume ratio according to the preset weight coefficient.

[0005] Further, the according to the comparison result of the distribution span value and the preset threshold value, the risk level of the material in the crushing channel is evaluated, and the high-risk jamming state is identified, including: Obtain the distribution span value and the preset crushing range threshold value, divide the threshold value interval to form a comparison standard, and obtain the material extrusion risk level; extracting a particle gap rate from the material particle size proportion data, and calculating an extrusion degree index according to a product of the particle gap rate and the distribution span value; determining a jam probability value according to a ratio of the extrusion degree index to a standard width of a crushing channel, and identifying the high-risk jamming state if the distribution span value exceeds a crushing range threshold and the jam probability value is greater than a preset jam threshold.

[0006] Further, the adjusting the feed flow according to the high-risk jamming state, monitoring a change trajectory of the adjusted material particle size distribution and a change direction of the distribution span value, comprises: querying a preset corresponding relationship table according to the high-risk jamming state and a material moisture content value to obtain a current adhesion coefficient, and calculating a roll surface adhesion force value through the adhesion coefficient; determining a roll surface adhesion risk according to a comparison result of the roll surface adhesion force value and a preset threshold value; calculating a blocking trend value according to a relationship between the adhesion coefficient and a material flow resistance, determining a feed flow reduction coefficient through the blocking trend value, and adjusting a feed flow value; collecting adjusted material particle size proportion data, constructing a lumping degree change trajectory curve, calculating a change slope according to a difference value of adjacent time distribution span values and a time interval, and determining a change direction of the distribution span value.

[0007] Further, the determining a matching combination scheme of the roll spacing target value and the feed flow target value according to the change direction, comprises: obtaining a change direction identifier of the distribution span value, if the identifier is an upward direction, obtaining a change slope by dividing a difference value of adjacent time lumping degrees by a time interval, and calculating a congestion aggravation coefficient according to the change slope; determining an adaptive roll spacing target value that needs to be expanded based on the congestion aggravation coefficient, determining a number of flow reduction steps and a reduction amplitude of each step, and constructing a first matching combination scheme of the roll spacing target value and the flow target value; if the change direction identifier is a downward direction, calculating a difference value sequence of the distribution span value at continuous sampling time, and judging a convergence degree of material particle size tending to be uniform through the difference value sequence; determining the roll spacing target value that needs to be reduced and gradually restoring the flow target value according to the convergence degree, and constructing a second matching combination scheme.

[0008] Further, after determining the change direction of the distribution span value, comprising: according to the change direction identifier of the distribution span value, searching for a corresponding parameter set from a matching combination scheme library to obtain the roll spacing set value and the feed flow set value; The roller spacing setting value is correlated with the surface adhesion coefficient. An additional expansion requirement value is calculated based on the value of the surface adhesion coefficient. The roller spacing setting value is then added to the additional expansion requirement value to obtain the adjusted roller spacing setting value. The matching relationship between the adjusted roller spacing setting value and the feed flow rate setting value and the current large particle size ratio, medium particle size ratio and small particle size ratio is calculated. The degree of fit index is determined based on the absolute value of the difference between the change range of the setting value and the change range of the particle size ratio. Based on the aforementioned adaptation index, time series data of fine particle output are monitored. When the difference in fine particle output within a continuous sampling period is less than a preset stable threshold, the control timing for achieving stable output is determined.

[0009] Furthermore, the step of modifying the operating state according to the set values ​​in the matching combination scheme and evaluating the uniformity of the particle size distribution of the crushed product includes: Based on the direction of change of the distribution span value, select the corresponding roller spacing setting value and feed flow rate setting value from the preset scheme library; The additional expansion requirement value is determined by calculating the correlation between the roller spacing setting value and the surface adhesion coefficient, and the roller spacing setting value is adjusted accordingly. The fit index is determined based on the absolute value of the difference between the adjusted roller gap setting and the current particle size percentage change. By monitoring the time series data of fine particle output, the timing of stable output control is determined. The operating status is modified by applying the roller gap setting value and the feed flow rate setting value. Samples of crushed products are collected to evaluate the uniformity of particle size distribution.

[0010] Furthermore, based on the uniformity assessment results, the set value is adjusted or maintained, and the particle size consistency control results of the crushed products are output, including: The operating status is modified by applying the roller spacing setting value and feed flow rate setting value at the control timing, and the actual roller spacing value and feed flow rate value after adjustment are collected. Based on the actual values, crushed product samples were collected, fine particle size distribution data were measured, and the sum of squares of the differences between the particle size values ​​and the average particle size was divided by the number of samples to obtain the standard deviation value. If the standard deviation value is less than the preset consistency threshold, it is determined to be a uniform compliance state, and the current set value is maintained; If the target is not met, adjust the roller gap setting to increase the preset expansion amount and decrease the feed flow setting, and output the crushed product particle size consistency control result containing the setting value and standard deviation value.

[0011] The technical solutions provided by the embodiments of the present invention may include the following beneficial effects: This invention discloses an automated control method for a mobile crusher, aiming to solve the challenges of material adhesion and aggregation, crushing chamber blockage, and uneven product particle size caused by high moisture content. This invention collects real-time data on material particle size distribution, roller spacing, and feed flow rate. Combined with moisture content assessment of surface adhesion coefficient, it identifies the degree of agglomeration and distribution span, thereby determining blockage risk and predicting roller adhesion trends. It dynamically adjusts roller spacing and feed flow rate to reduce jamming risk, and adaptively optimizes control parameters based on the direction of distribution span changes, ultimately achieving stable fine particle output and uniform particle size distribution. The core innovation of this invention lies in the multi-dimensional analysis and real-time control based on distribution span and material characteristics. This effectively avoids equipment congestion caused by wet and sticky materials, improves crushing efficiency and product consistency, and significantly enhances the operational stability and output quality of the mobile crusher. During mobile crusher operation, the equipment is frequently relocated to different sites, and the materials come from diverse sources with unstable properties (e.g., high humidity during the rainy season and strong material adhesion). This invention, through real-time detection and adaptive control, effectively prevents blockage and maintains output uniformity. In actual operation, mobile crushers may tilt due to vibration, exceeding the safe operating level. A tilt sensor can be installed; when the tilt exceeds a set threshold, the system automatically alarms and suspends operation until the equipment returns to a safe operating level, thus protecting the structural safety of the equipment. Attached Figure Description

[0012] Fig. 1 This is a flowchart of an automated control method for a mobile crusher according to the present invention.

[0013] Fig. 2 This is a schematic diagram of an automated control method for a mobile crusher according to the present invention.

[0014] Fig. 3 This is another schematic diagram of an automated control method for a mobile crusher according to the present invention. Detailed Implementation

[0015] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this specification, and not all embodiments. Based on the embodiments in this specification, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of this specification.

[0016] Compared with the steady-state or semi-steady-state control strategies typically used in stationary crushers, the control method of this invention, specifically designed for mobile crushers, differs fundamentally in its objectives, mechanisms, and architecture: stationary crushers aim to achieve maximum processing capacity under stable feed conditions; while mobile crushers focus on rapidly adapting to material changes, preventing sudden blockages, and ensuring particle size consistency, emphasizing the agility and reliability of control. Fixed equipment parameters often rely on manual setting or slow self-adaptation; this method achieves fully automatic, real-time, multi-parameter coordinated adjustment with a response speed of seconds, meeting the high dynamic requirements of mobile operations.

[0017] The automated control method for mobile crushers described in this invention is particularly suitable for on-site operations in solid waste resource recovery and infrastructure engineering. When the raw materials processed by the equipment are solid wastes such as reinforced concrete blocks generated after building demolition, industrial tailings, and waste ore (hereinafter referred to as "solid waste"), the system dynamically adjusts the roller spacing and feed flow rate by real-time sensing of the material particle size distribution, moisture content, and degree of agglomeration. This effectively prevents wet and sticky solid waste from clogging the crushing chamber and ensures the uniformity of recycled aggregate particle size, thereby achieving efficient and stable crushing of solid waste and forming a complete solid waste treatment process. Furthermore, the recycled stone obtained by this method, due to its controllable particle shape, reasonable gradation, and stable fine particle output, can be directly applied to key parts of water conservancy projects, such as dam filling, bridge foundation construction, embankment slope protection structures, and hydraulic concrete aggregate preparation. By realizing on-site crushing and utilization at the mobile operation site, this method not only improves the efficiency of solid waste resource recovery but also significantly supports the local supply needs of high-quality aggregates for water conservancy projects, achieving seamless integration from solid waste treatment to water conservancy project construction.

[0018] like Figs. 1-3 This embodiment of an automated control method for a mobile crusher may specifically include: Step S101: Real-time acquisition of the proportion of large, medium, and small particles of the material to be crushed by the mobile crusher, as well as the current roller gap and feed flow rate. Based on the moisture content of the material, the surface adhesion coefficient of the material is identified, and the distribution span value reflecting the difference between the degree of agglomeration and the span of discrete particles is extracted.

[0019] A laser particle size analyzer scans the material at the feed inlet of the mobile crusher to obtain real-time data on the proportions of large, medium, and small particles. Simultaneously, the current roller gap and feed flow rate are read from the equipment's sensors. The initial material throughput state is determined based on the proportional relationship between the three particle size proportions and the roller gap. A capacitive humidity sensor detects the material's moisture content, and an X-ray densitometer measures the material density. The surface adhesion coefficient is calculated based on the correspondence between the moisture content and the material density. If the moisture content exceeds a preset threshold, the adhesion coefficient is determined according to the formula: the adhesion coefficient equals a first preset coefficient multiplied by the moisture content plus a second preset coefficient. The adhesion coefficient is used to determine the adhesion and aggregation state of the crushed stone and fine sand. A convolutional neural network analyzes the surface texture feature image of the material, inputting the texture feature image and outputting agglomeration recognition results. The volume percentage of agglomerates formed due to wet adhesion is identified, obtaining agglomeration volume percentage data. From the agglomerate volume ratio data and the three particle size ratio data, the discrete particle span difference value D (D = large particle size ratio - small particle size ratio) is calculated according to the difference between the large particle size ratio and the small particle size ratio. The discrete particle span difference value D and the agglomerate volume ratio are weighted and summed according to a preset weight coefficient to obtain the distribution span value reflecting the overall non-uniformity of the material. The feed flow rate is then adjusted in combination with the initial state of material throughput.

[0020] Specifically, in one embodiment, the laser particle size analyzer uses dual-beam scanning technology to detect the material at the feed inlet in real time. The beam scans the material surface at a frequency of 50 times per second, and the proportions of large, medium, and small particles are calculated through diffraction intensity distribution inversion. Large particles are defined as those with a diameter exceeding 50 mm, medium particles are those between 20 and 50 mm, and small particles are those below 20 mm. The equipment sensors include a displacement sensor and a flow sensor. The displacement sensor is mounted on the roller support frame to measure the roller gap value in real time, and the flow sensor is installed below the feed belt to obtain the feed flow rate value by weighing. The initial state of material throughput is determined by calculating the ratio of the maximum particle size proportion to the current roller gap value. When this ratio exceeds 0.8, it is considered a difficult throughput state; a ratio between 0.5 and 0.8 indicates a normal throughput state; and a ratio below 0.5 indicates a smooth throughput state.

[0021] Specifically, the capacitive humidity sensor detects the moisture content of materials based on the principle of dielectric constant change. The sensor probe is inserted 10 cm into the material, and the moisture content is inferred by measuring the change in capacitance. The X-ray densitometer measures the density of materials by utilizing the attenuation of rays as they penetrate. The X-ray source and receiver are installed on opposite sides of the feed channel. The surface adhesion coefficient is calculated based on the empirical relationship between material moisture content and density. In practical applications involving crushed construction waste, when the moisture content is below a preset threshold of 8%, the adhesion coefficient is calculated linearly using the formula A = k * M, where A is the adhesion coefficient, k is the linear coefficient with a value of 0.05, and M is the moisture content. When the moisture content exceeds the preset threshold, the adhesion coefficient exhibits non-linear growth using the formula A = a * M + b * D, where a is the first preset coefficient with a value ranging from 0.1 to 0.2, b is the second preset coefficient with a value ranging from 0.2 to 0.4, and D is the density, the specific value of which is adjusted according to the material density.

[0022] It should be noted that when analyzing the surface texture features of materials, the convolutional neural network first acquires images of the material surface at a rate of 30 frames per second using an industrial camera, with an image resolution of 1920×1080 pixels. The network structure consists of 5 convolutional layers and 3 fully connected layers. The convolutional layers use 3×3 convolutional kernels to extract texture features, including particle edge sharpness, surface roughness, and color depth variations. The input layer receives the preprocessed texture feature image, and the image preprocessing includes grayscale conversion, histogram equalization, and noise filtering. The network is trained on a large number of labeled wet clump samples to learn to recognize particle adhesion features caused by moisture. The output layer produces clump recognition results, including clump location, size, and adhesion strength level. The clump volume percentage is obtained by dividing the sum of the volumes of all identified clumps by the total volume of the material in the scanned area.

[0023] In one possible implementation, the calculation of the discrete particle span difference value involves a comprehensive analysis of multiple parameters. First, the difference between the proportion of large and small particles is extracted as the basic span index, reflecting the dispersion of the material's particle size distribution. When the proportion of large particles is 60% and the proportion of small particles is 10%, the basic span index is 0.5, indicating that the material's particle size distribution is extremely uneven. Then, the moderating effect of the proportion of medium-sized particles on the span is considered. A higher proportion of medium-sized particles indicates a more concentrated particle size distribution. At this point, the basic span index is corrected by a correction coefficient of 1 minus half the proportion of medium-sized particles. The basic span index is then multiplied by this correction coefficient to obtain the final discrete particle span difference value.

[0024] Preferably, in the weighted summation of the distribution span values, the weighting coefficients are determined based on statistical analysis of historical crushing data. The weighting coefficient for the discrete particle span difference value is set to 0.6, reflecting the main influence of particle size distribution non-uniformity on the crushing effect; the weighting coefficient for the agglomerate volume ratio is set to 0.4, reflecting the restrictive effect of wet, sticky agglomerates on material flowability. The weighted summation formula is: distribution span value equals 0.6 multiplied by discrete particle span difference value plus 0.4 multiplied by agglomerate volume ratio.

[0025] For example, in a scenario involving the processing of a large amount of wet, sticky construction waste, the material contains concrete blocks, brick fragments, and moist fine sand of varying sizes. A laser particle size analyzer detected that large-diameter concrete blocks accounted for 45%, medium-sized brick fragments for 30%, and fine sand and small particles for 25%. Simultaneously, the moisture content was detected to be 15%, exceeding the preset threshold of 12%. At this point, the adhesion coefficient was determined according to an enhanced calculation model, and the fine sand formed clumps due to moisture, with clumps accounting for 20% of the total volume. Determining the initial state of material throughput provides a basis for subsequent adjustments to crushing parameters. When a difficult-to-pass state is identified, the system prioritizes widening the roller spacing to avoid large particles clogging the material; in a smooth-passing state, the roller spacing can be appropriately narrowed to improve crushing accuracy. This state determination mechanism based on real-time detection data allows the crusher to adjust its operating parameters promptly according to the dynamic changes in material characteristics.

[0026] For example, during rainy season construction, the moisture content of construction waste increases significantly, causing loose sand and stone powder to bind together into hard clumps. Convolutional neural networks identify the surface texture features of these clumps to determine their bonding strength and the difficulty of breaking them. Clumps with high bonding strength are prone to clogging when passing through rollers, so this clogging risk needs to be quantified by calculating the distribution span value.

[0027] Step S102: Based on the comparison between the distribution span value and the crushing range threshold of the mobile crusher, assess the risk level of mutual compression and blockage of materials in the crushing channel, and identify the high-risk jamming state when the distribution span value exceeds the crushing range threshold.

[0028] The distribution span value is obtained and compared with a preset crushing range threshold. A three-tiered comparison standard is formed based on the threshold interval. If the distribution span value is less than the first threshold, it is classified as a low-risk layer; if it is between the first and second thresholds, it is classified as a medium-risk layer; and if it exceeds the second threshold, it is classified as a high-risk layer, thus obtaining the material compression risk level. For each risk level, the particle gap ratio is extracted from the material particle size distribution data. The compression degree index is calculated by multiplying the particle gap ratio by the distribution span value. The blockage probability value is determined by the ratio of the compression degree index to the standard width of the crushing channel. The risk level of the material in the crushing channel is assessed based on the joint judgment result of the blockage probability value and the risk level. If the distribution span value exceeds the crushing range threshold and the blockage probability value is greater than the preset blockage threshold, it is identified as a high-risk jamming state.

[0029] Specifically, in one implementation, the hierarchical comparison of the distribution span value and the crushing range threshold is achieved through a three-level threshold system. The first threshold is set to 0.3, corresponding to a relatively uniform particle size distribution; the second threshold is set to 0.6, indicating that the particle size difference begins to be significant; and the third threshold is 0.8, representing an extremely uneven distribution. When the mobile crusher processes mixed construction waste, the real-time acquired distribution span value is compared with these three thresholds to form a precise division of four risk zones.

[0030] Specifically, the particle gap ratio is extracted based on the spatial packing theory of three particle size proportions. The gaps between large particles are filled by small and medium-sized particles. The gap ratio is calculated using the formula: gap ratio equals 1 minus the sum of the products of each particle size proportion and its packing density. Packing density refers to the volume density of particles in their natural packing state, which can be obtained through standard experiments such as the funnel method. In actual crushing operations, when the proportion of large-diameter concrete blocks is high, the particle gap ratio increases, and the material's flowability within the channel decreases. The compression degree index is obtained by multiplying the gap ratio by the distribution span value; this index directly reflects the intensity of material interaction within a confined space. The standard width of the crushing channel refers to the effective crushing space formed by the roller spacing and the feed inlet width under normal operating conditions. The calculation of the blockage probability value involves comparing the compression degree index with the standard width. When the compression degree index approaches the standard width, the possibility of material flow obstruction increases sharply. The preset clogging threshold is dynamically adjusted according to the characteristics of different materials. The adjustment process is based on the material's moisture content, viscosity, and experimental data. First, the material flow resistance and historical clogging rate are calculated, and then the threshold is determined through ratio calculation. For example, the threshold is set to 0.65 when processing dry crushed stone, and the threshold is reduced to 0.55 when processing wet and sticky construction waste.

[0031] Preferably, the risk level assessment employs a dual-judgment mechanism. First, it determines whether the distribution span value exceeds the 0.6 breakage range threshold; then, it checks whether the blockage probability value exceeds a preset blockage threshold. Only when both conditions are met simultaneously is the condition identified as high-risk congestion. This joint judgment avoids misjudgments that may arise from a single indicator, improving the accuracy of risk identification.

[0032] For example, when handling damp construction waste after rain, even if the distribution span value is only 0.5, the system will still issue a warning signal when the probability of clogging reaches 0.7 due to the increased viscosity of the material, prompting operators to pay attention to the potential risk of jamming.

[0033] Step S103: Reduce the feed flow rate based on the high-risk jamming state and the moisture content of the material, collect the proportion of large-diameter particles, medium-diameter particles, and small-diameter particles after adjusting the feed flow rate, and identify the trajectory of change in the degree of agglomeration and the direction of change in the distribution span value.

[0034] Based on the high-risk jamming state and material moisture content, a preset table of correspondence between moisture content and adhesion coefficient is consulted to obtain the current adhesion coefficient. The adhesion force is calculated by multiplying the adhesion coefficient by the roller surface contact area. If the adhesion force exceeds a preset adhesion threshold, a risk of roller surface adhesion is identified. Based on the roller surface adhesion risk assessment result, a stagnation trend value is calculated according to the relationship between the adhesion coefficient and material flow resistance. The feed flow reduction coefficient is determined according to the position of the stagnation trend value within a preset range. The adjusted feed flow value is obtained by multiplying the original feed flow rate by the reduction coefficient. Feeding is controlled according to the adjusted feed flow value. Data on the proportion of large, medium, and small particles are collected at preset time intervals. A trajectory curve of the change in the degree of agglomeration is constructed based on the correspondence between the collection time and the agglomeration degree value. Simultaneously, a trajectory curve of the change in the distribution span value is constructed based on the correspondence between the collection time and the distribution span value. The slope of the change in the degree of agglomeration is obtained by calculating the ratio of the difference in the degree of agglomeration between adjacent times to the time interval from the trajectory curve of the change in the degree of agglomeration. The slope of the distribution span value change is obtained by calculating the ratio of the difference between the distribution span values ​​at adjacent times to the time interval from the distribution span value change trajectory curve. If the slope of the distribution span value change is positive, the direction of change of the distribution span value is identified as upward; if the slope is negative, the direction of change is identified as downward. This yields the trajectory of the change in the degree of clumping and the direction of change of the distribution span value.

[0035] Specifically, in one implementation, the relationship between moisture content and adhesion coefficient is obtained through experimental calibration. During the experiment, construction waste samples were collected under different moisture contents, increasing from 5% to 25%, with 10 samples collected for each gradient. The adhesion force of each sample on the roller surface was measured using a shear force tester, and the adhesion coefficient was calculated based on the ratio of adhesion force to contact area. Experimental data showed that when the moisture content was below 8%, the adhesion coefficient remained between 0.1 and 0.2; when the moisture content was between 8% and 15%, the adhesion coefficient increased linearly to 0.5; and when the moisture content exceeded 15%, the adhesion coefficient increased sharply, reaching 0.85 at a moisture content of 25%. This experimental data was stored as a lookup table, and during actual operation, linear interpolation was performed based on the currently detected moisture content value to obtain the accurate adhesion coefficient.

[0036] Specifically, the calculation of roller surface adhesion needs to consider the actual contact between the material and the roller surface. The roller surface contact area is not simply the geometric area, but rather the effective contact area dynamically changing based on the material particle size distribution and roller rotation speed. During crusher operation, large-diameter materials form point contact with the roller surface, medium-diameter materials form line contact, and small-diameter materials and sticky clumps form surface contact. Image recognition technology is used to calculate the total area of ​​each contact type in real time, multiplied by the corresponding adhesion coefficient to obtain the comprehensive adhesion value. The preset adhesion threshold is determined based on the roller material and surface treatment process; the threshold is set to 500 Newtons for smooth steel rollers and 800 Newtons for toothed rollers. The prediction of the stagnation trend value is based on fluid mechanics principles and the flow characteristics of particulate materials. When wet, sticky materials flow within the crushing channel, the increased adhesion coefficient leads to increased inter-particle friction, forming flow characteristics similar to high-viscosity fluids. The stagnation tendency value is calculated as follows: First, the equivalent viscosity V of the material is determined based on the adhesion coefficient A. Then, the Reynolds number Re is calculated in conjunction with the channel geometry parameters, Re = ρvd / μ, where ρ is density, v is velocity, d is characteristic length, and μ is viscosity. When the Reynolds number is below the critical value of 2000, the material exhibits a laminar flow state, and the stagnation tendency value Z is equal to the product of the adhesion coefficient A and the channel aspect ratio, i.e., the ratio of channel length L to width W, i.e., L / W, i.e., Z = A * (L / W). When the Reynolds number is above 2000, the material is in a turbulent flow state, and the stagnation tendency value Z = A * (L / W) * (1 + 0.1 * Re / 1000). The model inputs are A, V, and channel parameters, and the output is Z. The feed flow rate reduction coefficient is determined based on the position of the stagnation trend value in the range of 0 to 1. When the stagnation trend value is 0.2, the reduction coefficient is 0.9; when the stagnation trend value is 0.5, the reduction coefficient is 0.7; and when the stagnation trend value exceeds 0.8, the reduction coefficient drops to 0.4, thereby achieving adaptive adjustment of the feed flow rate.

[0037] For example, when processing construction waste collected during the rainy season, the material moisture content reaches 18%, and the adhesion coefficient is found to be 0.62 according to a table. At this time, a large amount of moist fine sand is adsorbed on the surface of large concrete fragments, forming a sticky coating layer. The adhesion force on the roller surface is detected to reach 650 Newtons, exceeding the preset threshold, and it is determined that there is an adhesion risk.

[0038] Preferably, the trajectory of agglomeration degree is constructed through continuous sampling. Particle size distribution data is collected every 30 seconds, and the timestamp and corresponding proportions of large, medium, and small particle sizes are recorded. The degree of agglomeration is determined by calculating the volume percentage of binding particles in the total particles, and image analysis algorithms are used to identify the degree of blurring at particle boundaries to determine whether agglomeration occurs.

[0039] In one possible implementation, the trajectory curve is smoothed using cubic spline interpolation to eliminate the influence of sampling noise. The local slope of the curve is calculated using the five-point difference method, that is, taking two sampling points before and after the current time and fitting the slope of the straight line using the least squares method.

[0040] Understandably, the direction of change in the distribution span value directly reflects the dynamic evolution trend of the crushing process. When the slope is positive and the value is large, it indicates that the material non-uniformity is aggravated and the crushing effect is deteriorated; when the slope is negative, it indicates that the adjustment measures are effective and the material tends to be more uniform.

[0041] For example, in a certain processing step, the initial distribution span was 0.7. After adjusting the feed flow rate, the distribution span values ​​for five consecutive sampling periods were 0.68, 0.65, 0.61, 0.58, and 0.54, respectively. The calculated average slope was -0.032, and the system identified the change direction as decreasing, indicating that the crushing state was improving. Furthermore, the comprehensive analysis of the agglomeration degree change trajectory and the distribution span value change direction provided a basis for subsequent control decisions. When both show a decreasing trend, it indicates that the current adjustment strategy is effective; if the agglomeration degree decreases but the distribution span value increases, it indicates that although the agglomeration phenomenon is reduced, the particle size distribution is more uneven, requiring further optimization of control parameters.

[0042] Step S104: Obtain the direction of change of the distribution span value. When the distribution span value continues to increase, evaluate the degree of congestion in the crushing chamber by combining the change trajectory of the agglomeration degree. Extract the matching combination scheme of the adaptive roller spacing target value to be expanded and the flow rate target value to be reduced. When the distribution span value continues to shrink, evaluate the degree of convergence of the material particle size tending to be uniform. Extract the matching combination scheme of the roller spacing target value to be reduced and the flow rate target value to be restored.

[0043] The direction of change of the distribution span value is identified. If the indicator is upward, the slope is obtained by dividing the difference in agglomeration degree between adjacent moments by the time interval. A congestion aggravation coefficient is calculated based on the ratio of the slope to the growth rate of the distribution span value. When the congestion aggravation coefficient exceeds a preset congestion threshold, the congestion aggravation level is determined. Based on the congestion aggravation level, the preset correspondence between the level and the roll gap expansion amount is queried to determine the adaptive roll gap target value to be expanded. Simultaneously, the number of flow reduction steps and the reduction magnitude of each step are determined based on the congestion aggravation coefficient to obtain a step-wise flow reduction target value. A first matching combination scheme of roll gap target value and flow target value is constructed. If the direction of change is downward, the difference sequence of the distribution span value at consecutive sampling moments is calculated. The degree of convergence of the material particle size tending towards uniformity is determined by the ratio of the mean of the difference sequence to the initial distribution span value. Based on the convergence degree, a preset convergence level parameter table is queried, and the corresponding roller spacing reduction amount and flow recovery step size values ​​are extracted from the parameter table. The target value of the roller spacing to be reduced and the target value of the flow rate to be gradually restored are determined, and a second matching combination scheme is constructed.

[0044] Specifically, the direction of change in the distribution span value is determined through continuous monitoring. The distribution span value is recorded every 30 seconds, and after collecting 5 data points, a trend line is fitted using the least squares method. The direction of change is determined based on the sign of the trend line's slope. A slope greater than 0.01 indicates an upward trend, a slope less than -0.01 indicates a downward trend, and a slope between -0.01 and 0.01 indicates a stable state.

[0045] Specifically, the calculation of the congestion aggravation coefficient involves the correlation analysis of two key parameters. The slope of the change in agglomeration degree reflects the evolution rate of the material's agglomeration state, and is obtained by dividing the difference in agglomeration degree values ​​between adjacent sampling times by a 30-second time interval. The growth rate of the distribution span value is calculated using the five-point difference method, taking the distribution span values ​​at the current moment and two moments before and after, fitting a straight line to obtain the instantaneous growth rate. The congestion aggravation coefficient is equal to the ratio of the slope of the change in agglomeration degree to the growth rate of the distribution span value. This coefficient reflects the coupling degree between the two factors of material agglomeration and uneven particle size distribution. When the coefficient exceeds 1.5, it indicates that the agglomeration rate is faster than the uneven distribution rate, and the material forms viscous blockages in the crushing chamber; a coefficient between 0.8 and 1.5 indicates moderate congestion; and a coefficient below 0.8 indicates mild congestion.

[0046] It should be noted that there is a non-linear relationship between the degree of congestion and the amount of roller spacing expansion. For mild congestion, the roller spacing expands by 5% to 10% of the current spacing; for moderate congestion, it expands by 15% to 20%; and for severe congestion, it expands by 25% to 35%. This non-linear design considers the balance between the mechanical constraints of the crusher and its crushing efficiency. While excessive roller spacing expansion can alleviate congestion, it can cause large particles to pass through without being fully crushed, affecting product quality. Three preset expansion parameter tables are provided, corresponding to dry materials, wet materials, and highly viscous materials, respectively. The appropriate parameter table is automatically selected based on the real-time detected moisture content.

[0047] Preferably, the step-by-step reduction of traffic flow is achieved using a multi-level decreasing mechanism. The first step reduces the traffic flow to 80% of its original value for 60 seconds; if congestion is not relieved, the second step reduces it to 60% for 90 seconds; if necessary, the third step is activated to reduce it to 40%. The reduction magnitude of each step is dynamically adjusted according to the congestion aggravation coefficient; the larger the coefficient, the greater the reduction magnitude and the shorter the transition time between steps.

[0048] For example, when processing mixed construction waste with a moisture content of 18%, the distribution span value was detected to increase from 0.65 to 0.78, with the direction of change indicated as upward. The clumping degree increased from 15% to 23%, and the calculated congestion aggravation coefficient was 1.8, K=(Tnew / Told)*(Dnew / Dold), where K is the congestion aggravation coefficient, Tnew is the new clumping degree of 23%, Told is the old clumping degree of 15%, Dnew is the new distribution span value of 0.78, and Dold is the old distribution span value of 0.65, which was determined to be a severe congestion level.

[0049] In one possible implementation, a wet material parameter table is consulted. Based on the material moisture content H (in %) and the initial spacing D (in millimeters), the expansion ratio P = P(H) = 0.1H is defined. When H is greater than 20, the upper limit of P is 0.3. With inputs of H=100 and D=80, the output P=0.3, determining that the roller spacing needs to be expanded by 30%, from the initial 80 mm to 104 mm. Simultaneously, a three-stage flow control is initiated. The first stage reduces the flow rate from 5 tons per minute to 4 tons per minute. After observing that the congestion does not ease after 60 seconds, it automatically enters the second stage, reducing it to 3 tons per minute, and finally stabilizes at 2 tons per minute in the third stage, successfully avoiding complete blockage of the crushing chamber. Furthermore, when the distribution span value shows a decreasing trend, the degree of convergence is evaluated by calculating the difference sequence of five consecutive sampling points. The mean of the difference sequence reflects the convergence speed, and the ratio of the mean to the initial distribution span value is defined as the convergence rate. A convergence rate greater than 0.3 indicates rapid convergence, 0.1 to 0.3 indicates normal convergence, and less than 0.1 indicates slow convergence.

[0050] Understandably, the convergence level parameter table is established based on a large amount of historical data. During rapid convergence, the roller gap can be reduced by 15% to 20%, and the flow rate recovery adopts a rapid mode that increases by 20% every 30 seconds; during normal convergence, the roller gap is reduced by 8% to 12%, and the flow rate recovers by 15% every 60 seconds; during slow convergence, the roller gap is reduced by 3% to 5%, and the flow rate recovers by 10% every 90 seconds.

[0051] For example, after step-down flow reduction, the distribution span value gradually decreased from 0.78 to 0.52. The differences of five consecutive sampling points were -0.05, -0.06, -0.05, -0.05, and -0.05, respectively, with a mean of -0.052. The convergence rate was the ratio of the absolute value of the mean to the initial distribution span value, which was 0.067, indicating slow convergence. By reducing the roller gap from 104 mm to 99 mm and gradually restoring the flow rate from 2 tons, increasing it by 0.2 tons every 90 seconds, a stable operating state of 4.6 tons was finally achieved after 13 recovery cycles, realizing a dynamic balance between crushing efficiency and product uniformity.

[0052] Step S105: Based on the direction of change of the distribution span value, select the corresponding roller spacing setting value and feed flow rate setting value from the extracted matching combination scheme, identify the degree of adaptation of the corresponding roller spacing setting value and feed flow rate setting value to the current changes in the proportion of large particle size, medium particle size, and small particle size, and evaluate the control timing when the output of fine particles reaches a stable output.

[0053] Based on the direction of change of the distribution span value, the corresponding parameter set is searched from the preset matching combination scheme library. If the direction of change is upward, a combination scheme including roller spacing expansion parameter and flow rate reduction parameter is selected; if it is downward, a combination scheme including roller spacing reduction parameter and flow rate recovery parameter is selected. The roller spacing setting value and feed flow rate setting value are obtained. The surface adhesion coefficient a=k*m is calculated based on the moisture content, where a is the adhesion coefficient, k is a conversion constant set to 0.5, and m is the moisture content. The roller spacing setting value and the surface adhesion coefficient are correlated and calculated. When the adhesion coefficient exceeds the preset adhesion threshold, according to the rule that for every 0.1 increase in the adhesion coefficient, the roller spacing is expanded by a preset percentage, the additional expansion requirement value is calculated. The roller spacing setting value is added to the additional expansion requirement value to obtain the adjusted roller spacing setting value. Through the adjusted roller spacing setting value and feed flow rate setting value, the matching relationship between them and the current large particle size ratio, medium particle size ratio, and small particle size ratio is calculated. The fit degree index is determined based on the absolute value of the difference between the setting value change range and the particle size ratio change range. The smaller the fit degree index, the higher the fit degree. Based on the aforementioned adaptation index, time-series data of fine particle output is monitored, and the fine particle output value for each sampling period is recorded. When the difference between the maximum and minimum fine particle output values ​​within three consecutive sampling periods is less than a preset stable threshold, the control timing for achieving stable output is determined.

[0054] Specifically, in one implementation, the matching combination scheme library is established through statistical analysis of historical breakage data. The scheme library contains two main parameter sets: an expanded parameter set and a reduced parameter set. The expanded parameter set is suitable for operating conditions where the distribution span value increases, and includes three core parameters: the percentage increase in roller spacing, the flow rate reduction gradient, and the adjustment duration. The reduced parameter set is used for scenarios where the distribution span value decreases, and includes the percentage decrease in roller spacing, the flow rate recovery step size, and the recovery interval time. Each parameter set is further subdivided into three categories based on material type: dry materials, wet materials, and high-viscosity materials, forming six basic combination schemes.

[0055] Specifically, the identification of the direction of change in the distribution span value is based on real-time monitoring data. When an upward change is detected, it indicates that the material non-uniformity is intensifying, requiring an increase in the roller gap and a reduction in the feed flow rate. In this case, an expansion parameter set is extracted from the solution library, with the roller gap increase percentage ranging from 10% to 35%, and the flow rate reduction gradient divided into three levels, decreasing to 80%, 60%, and 40% of the original flow rate, respectively. When the change direction is downward, it indicates that the material is becoming more homogeneous, and the roller gap can be appropriately reduced while restoring the flow rate. In this case, a reduction parameter set is selected, with the roller gap reduction percentage ranging from 5% to 20%, and the flow rate restoration using a step-wise increase, each time increasing by 10% to 15% of the original flow rate. The influence of the surface adhesion coefficient on the roller gap setting is implemented through an additional expansion mechanism. The adhesion coefficient is positively correlated with the material moisture content; for every 5% increase in moisture content, the adhesion coefficient increases by 0.15 to 0.25. The preset adhesion threshold is dynamically adjusted according to the season and ambient humidity; the threshold is set to 0.4 in the dry season and decreases to 0.3 in the rainy season. When the adhesion coefficient exceeds the threshold, an additional expansion calculation is initiated: for every 0.1 increase in the adhesion coefficient, the roller gap is expanded by an additional 3% to 5%. This progressive expansion mechanism avoids sudden blockages caused by wet, sticky materials. The additional expansion requirement value is added to the basic roller gap setting value to form the final adjusted roller gap setting value, ensuring that wet, sticky materials can pass smoothly through the crushing channel.

[0056] Preferably, the fit index is obtained through multi-dimensional matching calculation. First, the change range of the setpoint is calculated, i.e., the percentage difference between the adjusted roller gap setpoint and the current actual roller gap, and the percentage difference between the feed flow rate setpoint and the current flow rate. Then, the real-time change range of the particle size distribution is obtained, including the change rates of the large particle size distribution, medium particle size distribution, and small particle size distribution over the most recent three sampling periods. The fit index is equal to the sum of the absolute values ​​of the differences between the setpoint change range and the particle size distribution change range. When the index value is less than 0.2, it indicates high fit; 0.2 to 0.5 indicates medium fit; and greater than 0.5 indicates low fit, requiring further parameter adjustment.

[0057] For example, in a certain crushing operation, the proportion of large-diameter particles changed from 45% to 38%, the proportion of medium-diameter particles changed from 30% to 35%, and the proportion of small-diameter particles changed from 25% to 27%. The overall change in particle size distribution was calculated to be 14%. At the same time, the roller spacing was adjusted from 80 mm to 92 mm, a change of 15%; and the flow rate decreased from 5 tons / minute to 3.5 tons / minute, a change of 30%.

[0058] In one possible implementation, the output of fine particles is monitored using a combination of a weighing sensor and a laser particle size analyzer. The weighing sensor is installed on the collection hopper below the discharge port and records the weight of the fine particles every 10 seconds. The laser particle size analyzer scans the discharge in real time and counts the percentage of particles smaller than 5 mm. Furthermore, the determination of the control timing is based on statistical principles. The output of fine particles is continuously recorded for each sampling period, and the maximum and minimum values ​​within three consecutive periods are calculated. A preset stability threshold is set according to product quality requirements: 5% of the average output for high-precision products and 10% for ordinary products. The system is considered to have reached a stable state when the difference is less than the threshold three times consecutively.

[0059] Understandably, accurate timing of control is crucial for the efficient operation of a crusher. Judging too early leads to unstable product quality, while judging too late reduces production efficiency.

[0060] For example, when processing concrete waste, the fine particle output in the first three sampling cycles was 120 kg, 118 kg, and 122 kg, respectively. The difference between the maximum and minimum values ​​was 4 kg, accounting for 3.3% of the average, which is less than the 5% stability threshold. The system determined that the control timing for stable output had been reached and continued to operate while maintaining the current roller spacing of 92 mm and the flow rate of 3.5 tons / minute.

[0061] Step S106: By controlling the timing, the operating status of the mobile crusher is modified in real time using the roller gap setting value and the feed flow rate setting value. A new round of roller gap and feed flow rate data is collected to evaluate the uniformity of the crushed product particle size distribution. If the standard is not met, the data collection step is repeated to continuously adjust the roller gap setting value and the feed flow rate setting value. If the standard is met, the current roller gap setting value and feed flow rate setting value are maintained, and the particle size uniformity control result of the crushed product is output.

[0062] The actuator is triggered at a controlled timing, and the operating state of the mobile crusher is modified by applying the roller gap setting value and the feed flow rate setting value. The actual roller gap value and the actual feed flow rate value after adjustment are collected, and the operating parameter group formed by the actual value and the setting value is recorded. Based on the operating parameter group, crushed product samples are collected, and the particle size distribution data of fine particles is measured. The sum of the squares of the differences between the particle size value and the average particle size is calculated and divided by the number of samples to obtain the standard deviation value. When the standard deviation value is less than the preset consistency threshold, it is determined to be in a uniform and compliant state. According to the uniform and compliant state, if it is not compliant, the roller gap setting value is adjusted to increase the preset expansion amount and the feed flow rate setting value is decreased. If it is compliant, the current roller gap setting value and feed flow rate setting value are maintained, and the crushed product particle size consistency control result containing the roller gap setting value, feed flow rate setting value, and standard deviation value is output.

[0063] Specifically, in one implementation, the triggering of the control timing is achieved through a real-time monitoring system. Once a stable output state is determined, a control signal is transmitted to the hydraulic actuator and the frequency converter. The hydraulic actuator adjusts the extension and retraction of the hydraulic cylinder according to the roller spacing setpoint, achieving precise adjustment of the roller spacing. The frequency converter receives the feed flow rate setpoint and controls the rate at which material enters the crushing chamber by changing the speed of the belt conveyor.

[0064] Specifically, the operating parameter set comprises both setpoints and actual values. The actual roller spacing is measured using a laser displacement sensor with an accuracy of 0.1 mm; the actual feed flow rate is calculated using load cells and speed sensors, with data updated every second. The operating parameter set is stored in a data cache, providing a benchmark reference for subsequent crushing performance evaluation.

[0065] It should be noted that the standard deviation is calculated using statistical methods to assess the dispersion of particle size distribution. A 100-gram sample is randomly collected from the crushed product, and the weight distribution of each particle size interval is obtained through vibrating sieving. The difference between the median of each particle size interval and the weighted average particle size of all intervals is calculated. The square of this difference is multiplied by the weight percentage of that interval, and the square root of the weighted sum of squares for all intervals is taken to obtain the standard deviation. A preset consistency threshold is set according to product specifications; the threshold is typically 2.5 mm for building aggregates and 1.5 mm for recycled fine aggregates.

[0066] Preferably, when a non-compliant state is detected, an adaptive adjustment mechanism is activated. The roller gap setting increases by 5% of the current value, while the feed flow rate setting decreases by 10% of the current value. This differentiated adjustment strategy is based on the physical characteristics of material crushing: appropriately increasing the roller gap reduces the risk of clogging, and reducing the flow rate prolongs the residence time of the material in the crushing chamber; the two work synergistically to improve crushing uniformity.

[0067] For example, when processing construction waste with a moisture content of 15%, the initial roller spacing is set to 85 mm and the feed flow rate is 4 tons / minute. The first test showed a standard deviation of 3.2 mm, exceeding the threshold of 2.5 mm. The system automatically adjusted the roller spacing to 89 mm and the flow rate to 3.6 tons / minute. After three adjustment cycles, the standard deviation decreased to 2.3 mm, achieving a uniform and compliant state. The system maintained the current parameters and output the control results: roller spacing set at 93 mm, feed flow rate set at 3.2 tons / minute, and standard deviation at 2.3 mm. It should be noted that the amplitude of the vibrating screen should remain constant during operation and should not be adjusted arbitrarily to avoid sand cross-contamination due to amplitude reduction. Therefore, the adjustments in this control method only target the roller spacing and feed flow rate, and do not involve changes to the vibrating screen amplitude. Furthermore, the under-screen conveyor is designed with sufficient width to effectively avoid material congestion or secondary dust generation. Therefore, this control method does not involve adjustments to the conveyor structure.

[0068] The above description is only a preferred embodiment of the present invention. It should be noted that those skilled in the art can make several improvements and additions without departing from the principle of the present invention, and these improvements and additions should also be considered within the scope of protection of the present invention.

Claims

1. A mobile crusher automation control method, characterized by, The method comprises the following steps: acquiring particle size distribution data and operating parameter data of the material to be crushed, and determining a distribution span value of the material; evaluating the risk level of the material in the crushing channel according to the comparison result of the distribution span value and a preset threshold value, and identifying a high-risk jamming state; adjusting the feed flow rate according to the high-risk jamming state, monitoring the change trajectory of the particle size distribution of the material after adjustment and the change direction of the distribution span value; determining a matching combination scheme of the target value of the roller spacing and the target value of the feed flow rate according to the change direction; modifying the operating state by applying the set value in the matching combination scheme, and evaluating the uniformity of the particle size distribution of the crushing product; adjusting or maintaining the set value according to the uniformity evaluation result, and outputting a crushing product particle size consistency control result.

2. The mobile crusher automation control method according to claim 1, characterized by, The method comprises the following steps: scanning the material at the feed inlet of the mobile crusher to acquire real-time data of the large particle size proportion, the medium particle size proportion and the small particle size proportion, and simultaneously reading the current roller spacing value and the feed flow rate value from the device sensor; detecting the moisture content value of the material, calculating the surface adhesion coefficient from the moisture content value, judging the adhesion and aggregation state of gravel and fine sand according to the surface adhesion coefficient, and identifying the volume proportion of the lump formed by wet adhesion; from the volume proportion data of the lump and the real-time data of the large particle size proportion, the medium particle size proportion and the small particle size proportion, calculating a discrete particle span difference value according to the difference between the large particle size proportion and the small particle size proportion, and obtaining the distribution span value by weighted summation of the discrete particle span difference value and the volume proportion of the lump according to a preset weight coefficient.

3. The mobile crusher automation control method according to claim 1, characterized by, The method comprises the following steps: acquiring the distribution span value and a preset crushing range threshold value, forming a comparison standard according to the threshold interval division, and obtaining the material extrusion risk level; extracting the inter-particle gap rate from the particle size proportion data, and calculating an extrusion degree index according to the product of the inter-particle gap rate and the distribution span value; determining a jamming probability value according to the ratio of the extrusion degree index to the standard width of the crushing channel, and identifying the high-risk jamming state if the distribution span value exceeds the crushing range threshold value and the jamming probability value is greater than a preset jamming threshold value.

4. The mobile crusher automation control method according to claim 1, characterized by, The method comprises the following steps: querying a preset corresponding relationship table according to the high-risk jamming state and the moisture content value of the material to acquire the current adhesion coefficient, calculating the roller surface adhesion force value from the adhesion coefficient; determining the roller surface adhesion risk according to the comparison result of the roller surface adhesion force value and a preset threshold value; calculating a blocking trend value according to the relationship between the adhesion coefficient and the material flow resistance, determining a feed flow rate reduction coefficient from the blocking trend value, and adjusting the feed flow rate value; Collect the adjusted material particle size proportion data, construct the agglomeration degree change trajectory curve, calculate the change slope according to the difference value of the distribution span value of adjacent time intervals, and determine the change direction of the distribution span value.

5. The mobile crusher automation control method according to claim 1, characterized by, The matching combination scheme of the target value of the roller spacing and the target value of the feeding flow is determined according to the change direction, and the matching combination scheme includes: Obtain the change direction identifier of the distribution span value, if the identifier is in the upward direction, obtain the change slope by dividing the difference value of the agglomeration degree of adjacent time intervals by the time interval, and calculate the congestion aggravation coefficient according to the change slope; Based on the congestion aggravation coefficient, determine the adaptive roller spacing target value that needs to be expanded, determine the number of flow reduction steps and the reduction amplitude of each step, and construct the first matching combination scheme of the roller spacing target value and the flow target value; If the change direction identifier is in the downward direction, calculate the difference value sequence of the distribution span value at consecutive sampling time intervals, and judge the convergence degree of the material particle size tending to be uniform through the difference value sequence; According to the convergence degree, determine the roller spacing target value that needs to be reduced and gradually restore the flow target value, and construct the second matching combination scheme.

6. The mobile crusher automation control method according to claim 1, characterized by, After determining the change direction of the distribution span value, it includes: According to the change direction identifier of the distribution span value, find the corresponding parameter set from the matching combination scheme library, and obtain the roller spacing set value and the feeding flow set value; The roller spacing set value is associated with the surface adhesion coefficient for calculation, the additional expansion requirement value is calculated according to the numerical value of the surface adhesion coefficient, and the adjusted roller spacing set value is obtained by adding the additional expansion requirement value to the roller spacing set value; Through the adjusted roller spacing set value and the feeding flow set value, the matching relationship between them and the current large particle size proportion, the medium particle size proportion and the small particle size proportion is calculated, and the adaptation degree index is determined according to the difference absolute value between the set value change amplitude and the particle size proportion change amplitude. Based on the adaptation degree index, monitor the time series data of fine particle output, and when the difference value of fine particle output in the consecutive sampling period is less than the preset stable threshold, determine the control opportunity of stable output.

7. The mobile crusher automation control method according to claim 1, characterized by, According to the application of the matching combination scheme in the set value modification running state, the uniformity of the broken product particle size distribution is evaluated, which includes: According to the change direction of the distribution span value, select the corresponding roller spacing set value and feeding flow set value from the preset scheme library; Through the associated calculation of the roller spacing set value and the surface adhesion coefficient, determine the additional expansion requirement value, and adjust the roller spacing set value; According to the difference absolute value between the adjusted roller spacing set value and the current particle size proportion change amplitude, determine the adaptation degree index; Monitor the time series data of fine particle output, judge the stable output control opportunity, apply the roller spacing set value and the feeding flow set value to modify the running state, and collect the broken product sample to evaluate the particle size distribution uniformity.

8. The mobile crusher automation control method according to claim 1, characterized by, According to the uniformity evaluation result, adjust or maintain the set value, and output the broken product particle size consistency control result, which includes: The roller gap setting value and the feed flow setting value are applied to modify the running state by controlling the timing, and the adjusted actual roller gap value and the feed flow value are collected; A crushed product sample is collected based on the actual value, fine particle size distribution data is measured, a difference between the particle size value and the average particle size is squared, and a standard deviation value is obtained by dividing the sample number; If the standard deviation value is less than a preset consistency threshold value, it is determined that the uniformity meets the standard, and the current setting value is maintained; If it does not meet the standard, the roller gap setting value is adjusted to increase by a preset expansion amount and the feed flow setting value is reduced, and a crushed product particle size consistency control result containing the setting value and the standard deviation value is output.

Images