EP4149686A2 - Procédé pour régler un concasseur - Google Patents

Procédé pour régler un concasseur

Info

Publication number
EP4149686A2
EP4149686A2 EP21728414.0A EP21728414A EP4149686A2 EP 4149686 A2 EP4149686 A2 EP 4149686A2 EP 21728414 A EP21728414 A EP 21728414A EP 4149686 A2 EP4149686 A2 EP 4149686A2
Authority
EP
European Patent Office
Prior art keywords
bulk material
effective diameter
drive
sensor
crusher
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
EP21728414.0A
Other languages
German (de)
English (en)
Inventor
Christian HINTERDORFER
Christian Hinterreiter
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Rubble Master HMH GmbH
Original Assignee
Rubble Master HMH GmbH
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Priority claimed from ATA50422/2020A external-priority patent/AT523754A2/de
Priority claimed from ATA50423/2020A external-priority patent/AT523755A2/de
Priority claimed from ATA50420/2020A external-priority patent/AT523812B1/de
Application filed by Rubble Master HMH GmbH filed Critical Rubble Master HMH GmbH
Publication of EP4149686A2 publication Critical patent/EP4149686A2/fr
Pending legal-status Critical Current

Links

Classifications

    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B02—CRUSHING, PULVERISING, OR DISINTEGRATING; PREPARATORY TREATMENT OF GRAIN FOR MILLING
    • B02C—CRUSHING, PULVERISING, OR DISINTEGRATING IN GENERAL; MILLING GRAIN
    • B02C18/00—Disintegrating by knives or other cutting or tearing members which chop material into fragments
    • B02C18/06—Disintegrating by knives or other cutting or tearing members which chop material into fragments with rotating knives
    • B02C18/16—Details
    • B02C18/22—Feed or discharge means
    • B02C18/2225—Feed means
    • B02C18/2291—Feed chute arrangements
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B02—CRUSHING, PULVERISING, OR DISINTEGRATING; PREPARATORY TREATMENT OF GRAIN FOR MILLING
    • B02C—CRUSHING, PULVERISING, OR DISINTEGRATING IN GENERAL; MILLING GRAIN
    • B02C18/00—Disintegrating by knives or other cutting or tearing members which chop material into fragments
    • B02C18/06—Disintegrating by knives or other cutting or tearing members which chop material into fragments with rotating knives
    • B02C18/16—Details
    • B02C18/22—Feed or discharge means
    • B02C18/2225—Feed means
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B02—CRUSHING, PULVERISING, OR DISINTEGRATING; PREPARATORY TREATMENT OF GRAIN FOR MILLING
    • B02C—CRUSHING, PULVERISING, OR DISINTEGRATING IN GENERAL; MILLING GRAIN
    • B02C23/00—Auxiliary methods or auxiliary devices or accessories specially adapted for crushing or disintegrating not provided for in preceding groups or not specially adapted to apparatus covered by a single preceding group
    • B02C23/02—Feeding devices
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B02—CRUSHING, PULVERISING, OR DISINTEGRATING; PREPARATORY TREATMENT OF GRAIN FOR MILLING
    • B02C—CRUSHING, PULVERISING, OR DISINTEGRATING IN GENERAL; MILLING GRAIN
    • B02C25/00—Control arrangements specially adapted for crushing or disintegrating
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
    • G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
    • G05B13/0265—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion
    • G05B13/027—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion using neural networks only
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/50—Depth or shape recovery
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/60—Analysis of geometric attributes
    • G06T7/62—Analysis of geometric attributes of area, perimeter, diameter or volume
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B02—CRUSHING, PULVERISING, OR DISINTEGRATING; PREPARATORY TREATMENT OF GRAIN FOR MILLING
    • B02C—CRUSHING, PULVERISING, OR DISINTEGRATING IN GENERAL; MILLING GRAIN
    • B02C13/00—Disintegrating by mills having rotary beater elements ; Hammer mills
    • B02C13/26—Details
    • B02C13/286—Feeding or discharge
    • B02C2013/28618—Feeding means
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B02—CRUSHING, PULVERISING, OR DISINTEGRATING; PREPARATORY TREATMENT OF GRAIN FOR MILLING
    • B02C—CRUSHING, PULVERISING, OR DISINTEGRATING IN GENERAL; MILLING GRAIN
    • B02C13/00—Disintegrating by mills having rotary beater elements ; Hammer mills
    • B02C13/26—Details
    • B02C13/286—Feeding or discharge
    • B02C2013/28618—Feeding means
    • B02C2013/28672—Feed chute arrangements
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10028—Range image; Depth image; 3D point clouds
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20081—Training; Learning
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20084—Artificial neural networks [ANN]
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02B—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. HOUSING, HOUSE APPLIANCES OR RELATED END-USER APPLICATIONS
    • Y02B20/00—Energy efficient lighting technologies, e.g. halogen lamps or gas discharge lamps
    • Y02B20/40—Control techniques providing energy savings, e.g. smart controller or presence detection

Definitions

  • the invention relates to a method for regulating a crusher with a crushing tool and a vibratory conveyor having a drive, bulk material lying in a detection area being detected with a sensor.
  • the invention is therefore based on the object of improving a method of the type described at the outset in such a way that, in the case of grains with inhomogeneous Grain size distribution, even large grains can be crushed with the same crushing result without the risk of damaging the crusher.
  • the invention solves the problem in that an effective diameter deff resulting from the largest diameter dmax and its direction is determined as a control variable transversely to the conveying direction of a grain of the bulk material in the detection area and that when the effective diameter deff is exceeded above a specified power threshold value, the power of the breaking tool is increased and / or the drive is switched off when the effective diameter deff is exceeded above a predetermined switch-off limit value.
  • the grains conveyed through the detection area are categorized in which the effective diameter deff of the crowns is compared with comparison values.
  • the power threshold value can be provided as a comparison value, and when the effective diameter deff of a grain is exceeded, the power of the breaking tool is increased.
  • the associated increase in impact energy can prevent the engine from being crushed, i.e. an undesirable lowering of the speed of the crusher rotor, so that even large grains, whose effective diameter is close to the dimensions of the crusher inlet, can be crushed.
  • the power threshold value can be a specified grain diameter that is below a shutdown limit value.
  • a cut-off limit value can form a comparison value, which, if exceeded, interrupts the conveyance of the bulk material before it reaches the crusher.
  • the crusher itself can also be switched off.
  • various image processing methods known from the prior art can be used to determine the effective diameter.
  • the grains can be detected in the detection area of a sensor and subjected to particle segmentation, for example a watershed transformation.
  • the sensor can comprise, for example, an optical or a depth sensor, which records the grains in the detection area and in one maps two-dimensional image.
  • conclusions can be drawn about the largest diameter dmax and the effective diameter dett, which results from its direction, transversely to the conveying direction. This means that the effective diameter deff is obtained by projecting the largest diameter dmax onto a straight line running transversely to the conveying direction.
  • 2D image processing methods can be used, 3D image processing methods with the aid of a volume sensor as a sensor deliver better results with regard to the determination of the diameter, since this also enables the depth information of the grains detected to be determined.
  • the volume of the bulk material arranged in a detection area of a volume sensor is determined as a control variable at regular intervals and compared with a default value, for example a nominal volume input flow of the crusher. If the volume detected by the volume sensor per interval is below the specified value, the drive can be activated to increase the oscillation amplitude and / or to increase the oscillation frequency until the specified value is reached. If the preset value is exceeded, the drive can reduce the oscillation amplitude and / or the oscillation frequency until it falls below the preset value can be controlled. In a preferred embodiment, the regulation can also take place in such a way that the recorded volume is in a predetermined range as a default value.
  • a stereo camera which can determine the volume with the aid of common image processing methods can be provided as the volume sensor. As a drive for the vibratory conveyor, unbalance motors are usually provided.
  • an effective diameter deff be determined transversely to the conveying direction of a grain of the bulk material and that at least two actuators of the drive be controlled so that the more effective Diameter deff is reduced transversely to the conveying direction.
  • the actuators for example unbalance motors, other vibration exciters or dampers to influence their vibration amplitude and vibration frequency, can be controlled independently of one another, so that an alignment of the bulk material grain, also called grain in the following, is made possible through an asymmetrical vibration input.
  • the reduction in the effective diameter deff which results, for example, on the basis of the largest diameter dmax and its direction, can take place by aligning the largest diameter dmax in the conveying direction.
  • the direction of the largest diameter does not have to coincide exactly with the conveying direction, but can, for example, be within a tolerance angle.
  • the effective diameter deff can, however, also correspond to the expansion of a casing around the respective grain transversely to the conveying direction. Alignment can take place by increasing the drive power if the cross section of the conveyor trough of the vibratory conveyor is designed in such a way that the bulk material grains are aligned with their largest diameter in the conveying direction at an energetic minimum. This is the case, for example, when the conveyor trough is V-shaped in cross section.
  • the actuators can be activated to align the grain closest to the crusher inlet.
  • the crusher inlet of the crusher must be aligned such that the crusher inlet longitudinal axis is arranged parallel to the conveying direction, so that an inventive alignment of the bulk material grain enables it to pass through the crusher inlet.
  • the at least two actuators of the drive to reduce the effective diameter deff of the grains can be activated when the effective diameter deff is exceeded transversely to the conveying direction of a grain in the detection area above a predetermined alignment limit value.
  • This means that the alignment is only applied to those bulk material grains that can actually lead to a blockage of the crusher inlet.
  • This can be determined in that an effective diameter of the bulk material grain is compared with an alignment limit value, so that the alignment only takes place when this alignment limit value is exceeded.
  • the sensor can be a volume sensor which transmits an image of the bulk material arranged in its detection area to an evaluation unit, for example a screen.
  • the bulk material grains exceeding the alignment limit value, the switch-off limit value and the power threshold value can be marked in the image.
  • the senor comprises a depth sensor which generates a two-dimensional depth image of bulk material conveyed past the depth sensor and is fed to a previously trained, folding neural network, the at least three consecutive folding levels, so-called convolution layer and a downstream one Classifier, for example a so-called fully connected layer.
  • a volume classifier can be provided to determine the bulk material volume, the output value of which is defined as the im
  • the existing bulk material volume is output. Furthermore, a first diameter classifier can be provided for determining the largest diameter dmax, the output value of which is output as the largest diameter dmax of a grain lying in the detection range of the sensor. In addition, a second diameter classifier can be provided for determining the effective diameter detf, the output value of which is output as the largest effective diameter detf of a grain located in the detection range of the sensor. Finally, a power classifier or a shutdown classifier can be provided, the output value of which indicates that the largest effective diameter det f has exceeded a predetermined power threshold value or a predetermined shutdown limit value. As a result of these measures, the parameters of the bulk material can be determined even with varying lighting and conveying conditions.
  • the information required for determining parameters can be extracted from the depth information after a neural network used for this purpose has been trained with training depth images with known bulk material parameters.
  • the folding planes reduce the input depth images to a number of individual features, which in turn are evaluated by the downstream classifier so that the desired parameter of the bulk material depicted in the input depth image can be determined as a result.
  • the number of planned folding levels, each of which can be followed by a pooling level for information reduction can be at least three, preferably five, depending on the computing power available.
  • a plane for dimension reduction a so-called flattening layer, can be provided in a known manner.
  • the desired parameter therefore no longer has to be calculated for each individual grain. Since the distance between the depicted bulk material and the depth sensor is mapped with only one value for each pixel in the depth image, the amount of data to be processed can be reduced, the measurement process accelerated and the memory required for the neural network reduced, in contrast to the processing of color images. As a result, the neural network can be implemented on inexpensive Kl parallel computing units with GPU support and the method can be used regardless of the color of the bulk material.
  • the desired bulk material parameter can also be determined by accelerating the measurement process even at conveyor speeds of 3 m / s, preferably 4 m / s.
  • the aforementioned reduction in the amount of data in the depth image and thus in the neural network also reduces the susceptibility to errors for the correct determination of the bulk material parameter.
  • the use of depth images has the additional advantage that the measurement process is largely independent of changing exposure conditions.
  • a vgg16 network Simonyan / Zisserman, Very Deep Convolutional Networks for Large-Scale Image Recognition, 2015
  • the depth image can be captured with a 3D camera, for example, since it can be arranged above a vibratory conveyor even if there is little space available due to the smaller space requirement.
  • several successive output values of the classifier can also be averaged and the mean value as the desired parameter of the bulk material
  • Detection area can be output.
  • a described neural network can be used to control a crusher with a crushing tool and a vibratory conveyor having a drive, with Bulk material lying in a detection area is detected with a depth sensor, which generates a two-dimensional depth image of the bulk material conveyed past the depth sensor and is fed to a previously trained, folding neural network that has at least three consecutive folding levels and a downstream classifier, the output value of which is used as a parameter of the Bulk material present in the detection area is output.
  • the classifier can be a power classifier and / or a shutdown classifier, the power classifier indicating, as a positive output value, that the largest effective diameter deff is exceeded above a specified power threshold value and the shutdown classifier specifies, as a positive output value, that the largest effective diameter deff is exceeded above a specified shutdown limit value and at a positive output value of the power classifier increases the power of the breaking tool and / or with a positive output value of the shutdown classifier the drive of the vibratory conveyor is switched off.
  • the training of the neural network is made more difficult and the measurement accuracy decreases during operation if non-bulk material elements are in the detection range of the depth sensor. These include, for example, vibrating components of the bowl feeder itself, or other machine elements. To avoid the resulting disturbances, it is proposed that the values of those image points are removed from the depth image, the depth of which corresponds to a previously detected distance between the depth sensor and a background for this image point or exceeds this distance. In this way, disruptive image information, caused for example by vibrations of the unbalance motors, can be removed and both the depth images and the training depth images can be limited to the information relevant for the measurement.
  • the training of the neural network requires large amounts of training depth images that represent the bulk material to be recorded as exactly as possible. However, the amount of work required to measure the required amount of bulk material is extremely high.
  • sample depth images each of a sample grain with known individual parameters be recorded and stored together with the individual parameters, after which several sample depth images are randomly combined to form a training depth image, the one that is common Parameter for example the sum, the maximum value or the mean value of the individual parameters of the composite sample depth images is assigned, after which the training depth image on the input side and the assigned common parameter on the output side are fed to the neural network and the weights of the individual network nodes are adapted in a learning step.
  • the training method is therefore based on the consideration that by combining sample depth images of measured sample grains, diverse combinations of training depth images can be created. It is therefore sufficient to use example depth images with comparatively fewer example grains
  • the weights between the individual network nodes are adapted in a known manner in the individual training steps so that the actual output value corresponds as closely as possible to the predefined output value at the end of the neural network.
  • Different activation functions can be specified at the network nodes, which are decisive for whether a sum value present at the network node is passed on to the next level of the neural network.
  • the values of those image points are removed from the depth image, the depth of which corresponds to a previously detected distance between the depth sensor and the background, for example the conveyor trough of the vibratory conveyor, for this image point or exceeds this distance.
  • the training depth images and the depth images of the measured bulk material only have the information relevant for the measurement, as a result of which a more stable training behavior is achieved and the recognition rate is increased during use.
  • the neural network can be trained on any type of bulk material.
  • the example depth images are combined with a random alignment to form a training depth image.
  • the number of grains per example depth image the number of possible arrangements of the grains is significantly increased without more sample depth images having to be generated and an over-adaptation of the neural network is avoided.
  • Separation of the grains of the bulk material can be omitted and larger bulk material volumes can be determined with constant conveying speed of the conveyor belt if the sample depth images with partial overlaps are combined to form a training depth image, the depth value of the training depth image in the overlap area corresponding to the shallowest depth of both sample depth images.
  • the neural network can be trained to recognize such overlaps and still be able to determine the parameters of the sample grains.
  • FIG. 1 shows a schematic side view of a vibratory conveyor for carrying out the method according to the invention
  • FIG. 2 shows a plan view of such a vibratory conveyor on a larger scale.
  • a method according to the invention can be used for regulating a vibratory conveyor 1 shown in FIG. 1.
  • Vibratory conveyors 1 are used, for example, to feed crushers with bulk material 2.
  • an effective diameter deff resulting from the largest diameter dmax and its direction 9 is transverse to the conveying direction 8 a Grain of the bulk material 2 used as a control variable (Fig. 2).
  • the effective diameter deff is exceeded above a predetermined power threshold value, the power of a breaking tool of a crusher, not shown, is increased.
  • a sensor 3 is provided which records the bulk material 2 lying in its detection area 4 and transfers the recorded data to a control unit 6.
  • the control unit 6 can determine the diameter by means of common image processing methods or with the help of a previously trained neural network and control the drive 5 as well as a drive of the crusher (not shown) as a function of the predetermined limit or threshold values.
  • the drive 5 can furthermore be regulated in such a way that the volume of the bulk material 2 lying in the detection area 4 detected by the sensor 3 at predetermined intervals corresponds to a specified value as a control variable.
  • the drive 5 is controlled by adapting the oscillation amplitude and / or the oscillation frequency in such a way that the controlled variable corresponds to a preset value.
  • a default value can be, for example, a range of a nominal volume input flow for which a crusher to be charged is designed.
  • the grains of the bulk material 2 can be aligned by targeted control of the drive 5.
  • the drive 5 can comprise several unbalance motors 7 as drives which, via actuators, are independent of one another with regard to their
  • Vibration amplitude and frequency can be controlled.
  • an asymmetrical input of vibrations can be generated, whereby, for example, particularly long bulk material grains can be aligned in such a way that their largest diameter dmax is displaced in conveying direction 8 and thus their effective diameter is reduced to that resulting from the largest diameter dmax and its direction 9. Blocking of the crusher by particularly long bulk material 2 can thereby be prevented.
  • the effective diameter deff resulting from the largest diameter dmax and its direction 9 can be compared with an alignment limit value. Only when the
  • control device 6 switch off the drive 5 via a switch-off limit value when the effective diameter deff resulting from the largest diameter dmax and its direction 9 is exceeded.
  • the breaking tool can be activated by the control device 6 when the effective diameter deff exceeds a power threshold value.

Landscapes

  • Engineering & Computer Science (AREA)
  • Food Science & Technology (AREA)
  • Physics & Mathematics (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • General Physics & Mathematics (AREA)
  • Artificial Intelligence (AREA)
  • Theoretical Computer Science (AREA)
  • Evolutionary Computation (AREA)
  • Geometry (AREA)
  • Health & Medical Sciences (AREA)
  • Medical Informatics (AREA)
  • Software Systems (AREA)
  • Automation & Control Theory (AREA)
  • Disintegrating Or Milling (AREA)
  • Crushing And Grinding (AREA)

Abstract

L'invention concerne un procédé pour régler un concasseur comprenant un outil de concassage et un convoyeur vibrant (1) qui présente un entraînement (5), du produit en vrac (2) présent dans une zone de détection (4) étant détecté au moyen d'un capteur (3). L'invention a pour objet de permettre, dans le cas de fragments ayant une répartition de taille de fragments non homogène, le concassage des fragments de grande taille jusqu'à obtention d'un résultat de concassage identique, sans risque d'endommagement du concasseur. À cet effet, en tant que taille de réglage est déterminé un diamètre effectif deff résultant du plus grand diamètre dmax et de sa direction (9), transversalement à la direction d'acheminement (8) d'un fragment du produit en vrac (2) dans la zone de détection (4), et, lorsque le diamètre effectif deff dépasse une valeur seuil de puissance préétablie, la puissance de l'outil de concassage est augmentée, et/ou lorsque le diamètre effectif deff dépasse une valeur seuil d'arrêt préétablie, l'entraînement (5) est arrêté.
EP21728414.0A 2020-05-13 2021-05-12 Procédé pour régler un concasseur Pending EP4149686A2 (fr)

Applications Claiming Priority (4)

Application Number Priority Date Filing Date Title
ATA50422/2020A AT523754A2 (de) 2020-05-13 2020-05-13 Verfahren zum abschnittsweisen Bestimmen des Volumens eines auf ein Förderband aufgegebenen Schüttgutes
ATA50423/2020A AT523755A2 (de) 2020-05-13 2020-05-13 Verfahren zum abschnittsweisen Bestimmen der Korngrößenverteilung eines auf ein Förderband aufgegebenen Schüttgutes
ATA50420/2020A AT523812B1 (de) 2020-05-13 2020-05-13 Verfahren zur Regelung eines Schwingförderers
PCT/AT2021/060169 WO2021226651A2 (fr) 2020-05-13 2021-05-12 Procédé pour régler un concasseur

Publications (1)

Publication Number Publication Date
EP4149686A2 true EP4149686A2 (fr) 2023-03-22

Family

ID=76180819

Family Applications (1)

Application Number Title Priority Date Filing Date
EP21728414.0A Pending EP4149686A2 (fr) 2020-05-13 2021-05-12 Procédé pour régler un concasseur

Country Status (4)

Country Link
US (1) US12343732B2 (fr)
EP (1) EP4149686A2 (fr)
CN (1) CN115209999B (fr)
WO (1) WO2021226651A2 (fr)

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CN107755063A (zh) * 2016-08-18 2018-03-06 中国铁建重工集团有限公司 一种移动破碎站及其给料控制方法和系统
CN107899733B (zh) * 2017-12-18 2023-04-07 江苏徐工工程机械研究院有限公司 破碎机及其控制方法、装置和系统、计算机可读存储介质
CN108711149B (zh) * 2018-05-16 2022-01-28 郑州大学 基于图像处理的矿岩粒度检测方法
CN208928360U (zh) * 2018-08-17 2019-06-04 福建南方路面机械有限公司 一种可自动去除超粒径原料的制砂整形破碎机
CN110852395B (zh) * 2019-11-15 2023-11-14 鞍钢集团矿业有限公司 一种基于自主学习和深度学习的矿石粒度检测方法和系统

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CN115209999A (zh) 2022-10-18
US12343732B2 (en) 2025-07-01

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