Method, device and readable medium for intelligently adjusting the discharge opening size of a cone crusher

Abstract

The invention discloses a method, a device and a readable medium for intelligently adjusting the size of a cone crusher discharge port. The method comprises the following steps: acquiring a first aggregate image of finished aggregate outside a cone crusher discharge port in real time, segmenting the first aggregate image using an instance segmentation model, and obtaining a first segmentation result; obtaining a first aggregate parameter according to the first segmentation result, the first aggregate parameter including one or more of a first average aggregate particle size, a first aggregate grading curve area difference, a first average aggregate needle shape and a first average aggregate number; constructing a cone crusher discharge port size prediction model, inputting the first aggregate parameter into the cone crusher discharge port size prediction model, and outputting a cone crusher discharge port size prediction value; comparing the cone crusher discharge port size prediction value with a cone crusher discharge port size threshold, adjusting the cone crusher discharge port size according to the comparison result, and changing the aggregate particle size through the adjusted cone crusher discharge port size, which is efficient, accurate and convenient.

Description

Technical Field

[0001] The invention relates to the field of cone crusher discharge port adjustment, and in particular to a cone crusher discharge port size intelligent adjustment method, device and readable medium. Background Art

[0002] With the rapid development of the construction industry, the types of stones of various specifications have increased. Cone crusher, as one of the most important equipment, is also a popular crushing equipment. It is often used in the crushing and processing of various ores. Due to the outstanding advantages of high output, stable performance, and long service life, cone crushers are widely used in the market. In daily operation, one of the factors affecting the output of cone crusher is the adjustment of the discharge port. Operators must be proficient in the adjustment size of the discharge port to ensure smooth production and ensure that the particle size of the finished aggregate produced by the discharge port meets the requirements. Different cone crusher discharge port sizes produce different particle sizes of finished aggregate.

[0003] The traditional method of adjusting the discharge port is for an observer to observe the size of the aggregate particle size near the discharge port, determine the size of the discharge port opening, and then manually adjust the size of the discharge port. In this case, when the wear-resistant liner is more worn, the change in the size of the aggregate particle size is visible to the naked eye. At this time, the observer observes the change in the aggregate particle size, and there is already a large lag. Another method is based on the detection method of coarse aggregate particle size. This method requires sampling to the coarse aggregate detector for aggregate particle size detection. At this time, the offline detection speed of sampling and then testing is slow, and the size of the discharge port cannot be adjusted in time. In addition, both of the above methods require manual participation and are inefficient. Summary of the invention

[0004] In view of the problems existing in the above-mentioned background technology, the purpose of the embodiments of the present application is to propose a method, device and readable medium for intelligently adjusting the size of the discharge port of a cone crusher to solve the technical problems mentioned in the above background technology section.

[0005] In a first aspect, an embodiment of the present application provides a method for intelligently adjusting the size of a cone crusher discharge port, comprising the following steps:

[0006] S1, acquiring a first aggregate image of finished aggregate outside a discharge port of a cone crusher in real time, and segmenting the first aggregate image using an instance segmentation model to obtain a first segmentation result;

[0007] S2, obtaining first aggregate parameters according to the first segmentation result, where the first aggregate parameters include one or more of a first average aggregate particle size, a first aggregate gradation curve area difference, a first average aggregate needle shape, and a first average aggregate number;

[0008] S3, constructing a cone crusher discharge opening size prediction model, inputting the first aggregate parameter into the cone crusher discharge opening size prediction model, and outputting a cone crusher discharge opening size prediction value;

[0009] S4, comparing the predicted value of the discharge opening size of the cone crusher with the discharge opening size threshold of the cone crusher, obtaining an adjustment instruction according to the comparison result and sending it to the fixed cone assembly control system, and the fixed cone assembly control system adjusts the discharge opening size of the cone crusher, and changes the particle size of the aggregate through the adjusted discharge opening size of the cone crusher;

[0010] S5, repeating steps S1-S4 until the predicted value of the cone crusher discharge opening size is within the cone crusher discharge opening size threshold range.

[0011] In a specific embodiment, building a cone crusher discharge port size prediction model in step S3 specifically includes:

[0012] Acquire a second aggregate image of a finished aggregate of a cone crusher at different discharge opening sizes;

[0013] Using the instance segmentation model to segment the second aggregate image to obtain a second segmentation result;

[0014] Acquire second aggregate parameters according to the second segmentation result, where the second aggregate parameters include one or more of a second average aggregate particle size, a second aggregate gradation curve area difference, a second average aggregate needle shape, and a second average aggregate number;

[0015] The second aggregate parameter is used as the characteristic variable and a multivariate nonlinear regression model is established: i =f(x i ,β)+ε i ;

[0016] Among them, the vector y i For different discharge opening sizes, i = 1, 2, ..., n, the vector x i =(x i1 ,x i2 ,x i3 ,x i4 ) is the characteristic variable under the influence of different discharge opening sizes, x i1 is the second average aggregate size, x i2 is the area difference of the second aggregate gradation curve, x i3 is the second average aggregate acicularity, x i4 is the second average number of aggregates, β is the parameter vector, ε i is the random error term;

[0017] The multivariate nonlinear regression model was transformed into a multivariate linear model, and the least square method was used to combine the second aggregate parameters to calculate the regression coefficients in the multivariate linear model to obtain the cone crusher discharge port size prediction model.

[0018] In a specific embodiment, the first average aggregate particle size and the second average aggregate particle size are average values ​​of aggregate particle sizes over a period of time.

[0019] In a specific embodiment, the area difference of the first aggregate grading curve and the area difference of the second aggregate grading curve are the area difference between the current aggregate grading curve and the standard aggregate grading curve, wherein the standard aggregate grading curve is an average aggregate grading curve within a period of time calculated based on the aggregate image of the finished aggregate produced by the cone crusher after the discharge port has just been adjusted, and the current aggregate grading curve is an average aggregate grading curve calculated based on the aggregate image within a period of time before the current time.

[0020] In a specific embodiment, the first average needle-likeness and the second average needle-likeness are the average values ​​of the needle-likeness of all aggregates within a period of time, the needle-likeness of the aggregate is the ratio of the major axis to the minor axis of each aggregate particle size, and the first average number of aggregates and the second average number of aggregates are the average values ​​of the number of aggregates contained in each aggregate image within a period of time.

[0021] In a specific embodiment, the fixed cone assembly control system includes a PLC, a hydraulic motor and a fixed cone assembly, and the hydraulic motor is controlled by the PLC to drive the fixed cone assembly to move so as to change the size of the discharge port of the cone crusher.

[0022] In a specific embodiment, the instance segmentation model is a trained Mask-RCNN network model.

[0023] In a second aspect, an embodiment of the present application provides a device for intelligently adjusting the size of a cone crusher discharge port, comprising:

[0024] An instance segmentation module is configured to acquire a first aggregate image of finished aggregate outside a discharge port of a cone crusher in real time, and segment the first aggregate image using an instance segmentation model to obtain a first segmentation result;

[0025] a parameter calculation module configured to obtain first aggregate parameters according to the first segmentation result, wherein the first aggregate parameters include one or more of a first average aggregate particle size, a first aggregate gradation curve area difference, a first average aggregate needle shape, and a first average number of aggregates;

[0026] A size prediction module is configured to construct a cone crusher discharge opening size prediction model, input the first aggregate parameter into the cone crusher discharge opening size prediction model, and output a cone crusher discharge opening size prediction value;

[0027] The size adjustment module is configured to compare the predicted value of the discharge opening size of the cone crusher with the discharge opening size threshold of the cone crusher, obtain an adjustment instruction according to the comparison result and send it to the fixed cone assembly control system, and the fixed cone assembly control system adjusts the discharge opening size of the cone crusher, and changes the particle size of the aggregate through the adjusted discharge opening size of the cone crusher;

[0028] The loop module is configured to repeatedly execute the instance segmentation module to the size adjustment module until the predicted value of the cone crusher discharge opening size is within the cone crusher discharge opening size threshold range.

[0029] In a third aspect, an embodiment of the present application provides an electronic device comprising one or more processors; a storage device for storing one or more programs, wherein when the one or more programs are executed by one or more processors, the one or more processors implement a method as described in any implementation manner in the first aspect.

[0030] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method described in any implementation manner in the first aspect.

[0031] Compared with the prior art, the present invention has the following beneficial effects:

[0032] (1) The present invention can obtain the first aggregate image outside the discharge port of the cone crusher in real time, and use the instance segmentation model to segment the first aggregate image, calculate the first aggregate parameter according to the segmentation result, and predict the size of the discharge port of the cone crusher by combining the first aggregate parameter with the cone crusher discharge port size prediction model, and further adjust the discharge port size of the cone crusher according to the predicted value, which has stronger real-time performance, higher adjustment efficiency, and does not require human intervention.

[0033] (2) The present invention uses the average aggregate particle size, the area difference of the aggregate grading curve, the average aggregate needle shape and the average number of aggregates as characteristic parameters to construct a cone crusher discharge port size prediction model, so the obtained prediction value is more accurate and more comprehensive.

[0034] (3) The method for intelligently adjusting the discharge port size of a cone crusher of the present invention is more intelligent than the traditional method for adjusting the discharge port size of a cone crusher. It adopts an online detection method for aggregate particle size combined with an intelligent adjustment device for the discharge port of a cone crusher, making the adjustment of the discharge port of a cone crusher more efficient, more accurate and more convenient. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0036] Figure 1 is an exemplary device architecture diagram to which an embodiment of the present application may be applied;

[0037] Figure 2 A schematic diagram of a flow chart of a method for intelligently adjusting the size of a discharge opening of a cone crusher according to an embodiment of the present invention;

[0038] Figure 3 It is a result diagram before and after segmentation of an example segmentation model of the method for intelligently adjusting the discharge port size of a cone crusher according to an embodiment of the present invention;

[0039] Figure 4 It is a schematic diagram of a fixed cone assembly control system of a method for intelligently adjusting the discharge port size of a cone crusher according to an embodiment of the present invention;

[0040] Figure 5 It is a schematic diagram of a device for intelligently adjusting the discharge opening size of a cone crusher according to an embodiment of the present invention;

[0041] Figure 6 It is a schematic diagram of the structure of a computer device suitable for implementing the electronic device of the embodiment of the present application. DETAILED DESCRIPTION

[0042] In order to make the purpose, technical scheme and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0043] Figure 1 An exemplary device architecture 100 is shown to which a method for intelligently adjusting the discharge opening size of a cone crusher or a device for intelligently adjusting the discharge opening size of a cone crusher according to an embodiment of the present application can be applied.

[0044] like Figure 1 As shown, the device architecture 100 may include terminal devices 101, 102, 103, a network 104 and a server 105. The network 104 is used to provide a medium for communication links between the terminal devices 101, 102, 103 and the server 105. The network 104 may include various connection types, such as wired, wireless communication links or optical fiber cables, etc.

[0045] Users can use terminal devices 101, 102, 103 to interact with server 105 through network 104 to receive or send messages, etc. Various applications, such as data processing applications, file processing applications, etc., can be installed on terminal devices 101, 102, 103.

[0046] Terminal devices 101, 102, 103 can be hardware or software. When terminal devices 101, 102, 103 are hardware, they can be various electronic devices, including but not limited to smart phones, tablet computers, laptop computers, desktop computers, etc. When terminal devices 101, 102, 103 are software, they can be installed in the electronic devices listed above. They can be implemented as multiple software or software modules (for example, software or software modules used to provide distributed services), or they can be implemented as a single software or software module. No specific limitation is made here.

[0047] The server 105 may be a server that provides various services, such as a background data processing server that processes files or data uploaded by the terminal devices 101, 102, and 103. The background data processing server may process the acquired files or data and generate processing results.

[0048] It should be noted that the method for intelligently adjusting the discharge port size of a cone crusher provided in the embodiment of the present application can be executed by the server 105, or by the terminal devices 101, 102, and 103. Accordingly, the device for intelligently adjusting the discharge port size of a cone crusher can be arranged in the server 105, or in the terminal devices 101, 102, and 103.

[0049] It should be understood that Figure 1 The number of terminal devices, networks and servers in the embodiment is only for illustration. Any number of terminal devices, networks and servers may be provided as required. In the case where the processed data does not need to be obtained remotely, the above device architecture may not include a network, but only a server or a terminal device.

[0050] Figure 2 A method for intelligently adjusting the size of a cone crusher discharge port provided in an embodiment of the present application is shown, comprising the following steps:

[0051] S1, acquiring a first aggregate image of finished aggregate outside a discharge port of a cone crusher in real time, and segmenting the first aggregate image using an instance segmentation model to obtain a first segmentation result.

[0052] In a specific embodiment, the instance segmentation model is a trained Mask-RCNN network model. The pre-trained weights on the COCO training set are downloaded and the Mask-RCNN network model is trained in combination with the training set collected and annotated under the actual working conditions on site to obtain the trained Mask-RCNN network model. The prediction results are as follows: Figure 3 As shown, Figure 3 (a) is the first aggregate image, Figure 3 (b) is the first segmentation result. Specifically, the Mask-RCNN network model is inception_v2, but is not limited thereto.

[0053] S2, obtaining first aggregate parameters according to the first segmentation result, where the first aggregate parameters include one or more of a first average aggregate particle size, a first aggregate gradation curve area difference, a first average aggregate needle shape, and a first average aggregate number.

[0054] In a specific embodiment, the first aggregate image is an aggregate image of finished aggregate outside the discharge port of the cone crusher collected in real time, and instance segmentation is performed through an instance segmentation model to obtain a mask of each aggregate on the first aggregate image, and aggregate parameters such as aggregate particle size, average acicularity of aggregate, and aggregate grading curve are calculated according to the mask of each aggregate. The first average aggregate particle size is the average value of the aggregate particle size over a period of time. The area difference of the first aggregate grading curve is the area difference between the current aggregate grading curve and the standard aggregate grading curve, wherein the standard aggregate grading curve is the average aggregate grading curve over a period of time calculated according to the aggregate image of the finished aggregate produced by the cone crusher after the discharge port is just adjusted, and the current aggregate grading curve is the average aggregate grading curve calculated according to the aggregate image over a period of time before the current time. The first average acicularity is the average value of the acicularity of all aggregates over a period of time, and the acicularity of the aggregate is the ratio of the major diameter to the minor diameter of each aggregate particle size. The first average number of aggregates is the average number of aggregates contained in each aggregate image within a period of time. When the first aggregate image of the finished aggregate outside the discharge port of the cone crusher is obtained in real time, the size of the discharge port of the cone crusher is unknown and needs to be further predicted.

[0055] S3, constructing a cone crusher discharge port size prediction model, inputting the first average aggregate particle size, the first aggregate grading curve area difference, the first average aggregate needle shape and the first average aggregate number into the cone crusher discharge port size prediction model, and outputting the cone crusher discharge port size prediction value.

[0056] In a specific embodiment, building a cone crusher discharge port size prediction model in step S3 specifically includes:

[0057] Acquire a second aggregate image of a finished aggregate of a cone crusher at different discharge opening sizes;

[0058] Using the instance segmentation model to segment the second aggregate image to obtain a second segmentation result;

[0059] Acquire second aggregate parameters according to the second segmentation result, the second aggregate parameters including one or more of a second average aggregate particle size, a second aggregate gradation curve area difference, a second average aggregate needle shape, and a second average aggregate number;

[0060] The second aggregate parameter is used as the characteristic variable and a multivariate nonlinear regression model is established: i =f(x i ,β)+ε i ;

[0061] Among them, the vector y i For different discharge opening sizes, i = 1, 2, ..., n, the vector x i =(x i1 ,x i2 ,x i3 ,x i4 ) is the characteristic variable under the influence of different discharge opening sizes, x i1 is the second average aggregate particle size, x i2 is the area difference of the second aggregate gradation curve, x i3 is the second average aggregate acicularity, x i4 is the second average number of aggregates, β is the parameter vector, ε i is the random error term;

[0062] The multivariate nonlinear regression model was transformed into a multivariate linear model, and the least square method was used to combine the second aggregate parameters to calculate the regression coefficients in the multivariate linear model to obtain the cone crusher discharge port size prediction model.

[0063] In a specific embodiment, the second average aggregate particle size is the average value of the aggregate particle size over a period of time. The second aggregate gradation curve area difference is the area difference between the current aggregate gradation curve and the standard aggregate gradation curve, wherein the standard aggregate gradation curve is the average aggregate gradation curve over a period of time calculated based on the aggregate image of the finished aggregate produced by the cone crusher after the discharge port has just been adjusted, and the current aggregate gradation curve is the average aggregate gradation curve calculated based on the aggregate image over a period of time before the current time. The second average acicularity is the average value of the acicularity of all aggregates over a period of time, and the acicularity of the aggregate is the ratio of the major diameter to the minor diameter of each aggregate particle size. The second average number of aggregates is the average value of the number of aggregates contained in each aggregate image over a period of time. At this time, when the different discharge port sizes of the cone crusher are known, the second aggregate image of the finished aggregate at different discharge port sizes is calculated, the second aggregate parameters are obtained, and a multivariate nonlinear regression model between the second aggregate parameters and different discharge port sizes is established, which is further converted into a multivariate linear regression model. Finally, the various regression parameters in the multivariate linear regression model are calculated by the least squares method, and finally the cone crusher discharge port size prediction model is obtained.

[0064] Specifically, the second aggregate image is an aggregate image of finished aggregates collected under different discharge port sizes of the cone crusher. The particle sizes of the finished aggregates produced by the cone crushers with different discharge port sizes are different, so a linear relationship is established. First, a multivariate nonlinear regression model is established, and the multivariate nonlinear regression model is further converted into a multivariate linear regression model. The multivariate nonlinear regression model is established based on one or more of the second average aggregate particle size, the second aggregate grading curve area difference, the second average aggregate needle shape and the second average number of aggregates calculated by the second aggregate image, and then further converted into a multivariate linear model to obtain a cone crusher discharge port size prediction model. Finally, the first aggregate parameter corresponding to the first aggregate collected in real time is input into the cone crusher discharge port size prediction model, and the cone crusher discharge port size prediction value is output.

[0065] The specific conversion process from the multivariate nonlinear regression model to the multivariate linear model is as follows:

[0066] Assume that only two factors are considered among the second average aggregate particle size, the second aggregate gradation curve area difference, the second average aggregate needle shape and the second average aggregate number:

[0067] A two-variable quadratic model with interaction terms was used to describe the relationship between the discharge opening size and the two factors:

[0068]

[0069] Among them, y is the regression value, which represents the size of the discharge port, and x1 and x2 are two independent variables, which represent factor 1 and factor 2 respectively. The following experimental data are obtained from the experiment:

[0070] Table 1 Experimental data of factors 1 and 2 and discharge opening size

[0071] Data serial number x1 x2 y 1 -99.24 1.75 30 2 100.5 1.73 30 3 -319.22 1.74 30 4 -79.64 1.78 31 5 21249 1.77 31 6 199.24 1.75 31 7 123.5 1.77 32 8 309.22 1.78 32 9 279.64 1.77 32

[0072] The binary quadratic nonlinear regression model is transformed into a five-variable linear regression model, from two independent variables to five independent variables. The data format is shown in Table 2:

[0073] y=a+b1x1+b2x2+b3x3+b4x4+b5x5;

[0074] Table 2 Experimental data of five independent variables and discharge opening size

[0075]

[0076] Through the regression method in the Excel "Data Analysis" function, the five-variable linear regression model is obtained as follows:

[0077] y=-757.8-0.00006x1+869.5x2+0.001x3+0.000003x4-239.5x5;

[0078] Restoring it back, we get the binary quadratic regression model:

[0079]

[0080] S4, comparing the predicted value of the discharge opening size of the cone crusher with the discharge opening size threshold of the cone crusher, obtaining an adjustment instruction according to the comparison result and sending it to the fixed cone assembly control system, and the fixed cone assembly control system adjusts the discharge opening size of the cone crusher, and changes the particle size of the aggregate through the adjusted discharge opening size of the cone crusher;

[0081] Specifically, the predicted value of the discharge port size of the cone crusher is compared with the threshold value of the discharge port size of the cone crusher. If the predicted value of the discharge port size of the cone crusher is greater than the threshold value of the discharge port size of the cone crusher, the discharge port size of the cone crusher is reduced. If the predicted value of the discharge port size of the cone crusher is less than the threshold value of the discharge port size of the cone crusher, the discharge port size of the cone crusher is increased. The adjustment of the discharge port size of the cone crusher is to obtain the corresponding adjustment instruction according to the comparison result, and send the adjustment instruction to the fixed cone assembly control system. The fixed cone assembly control system includes a PLC, a hydraulic motor and a fixed cone assembly. The hydraulic motor is controlled by the PLC to drive the fixed cone assembly to move to change the discharge port size of the cone crusher. The fixed cone assembly can be lowered or raised.

[0082] refer to Figure 4 A camera 1 is installed on the conveyor belt 2 outside the discharge port of the cone crusher. The camera 1 is used to collect aggregate images of finished aggregates 3 on the conveyor belt 2, and transmit the collected aggregate images to the industrial computer 4. The industrial computer 4 performs instance segmentation on the collected aggregate images, predicts the mask of each aggregate, calculates the equivalent particle size of the aggregate, the average acicularity of the aggregate, the aggregate grading curve and other characteristics, and predicts the discharge port size through the cone crusher discharge port size prediction model. If the cone crusher discharge port size prediction value is greater than the cone crusher discharge port size threshold, the discharge port adjustment information is sent to the PLC 5, and the PLC 5 controls the hydraulic motor 6 to lower the fixed cone assembly 7 to reduce the discharge port size, and vice versa, and finally realizes the online automatic adjustment of the discharge port of the cone crusher.

[0083] S5, repeating steps S1-S4 until the predicted value of the cone crusher discharge opening size is within the cone crusher discharge opening size threshold range.

[0084] Specifically, after adjusting the discharge port size of the cone crusher each time, continue to obtain the first aggregate parameter, obtain the predicted value of the discharge port size of the cone crusher, and determine whether the predicted value of the discharge port size of the cone crusher is within the threshold range of the discharge port size of the cone crusher. If so, stop adjusting, otherwise continue to adjust the discharge port size of the cone crusher in a cycle until the discharge port size of the cone crusher is within the threshold range. By detecting the particle size of the aggregate in real time and combining the predicted value of the discharge port size of the cone crusher, the discharge port size of the cone crusher is adjusted in a cycle, which not only realizes the online detection of the aggregate particle size, but also can adjust the discharge port size of the cone crusher more efficiently, more accurately, and more conveniently, reducing labor costs and realizing automatic adjustment.

[0085] Further references Figure 5 As an implementation of the methods shown in the above figures, the present application provides an embodiment of a device for intelligently adjusting the size of a discharge opening of a cone crusher. Figure 2 Corresponding to the method embodiment shown, the device can be specifically applied to various electronic devices.

[0086] The embodiment of the present application provides a device for intelligently adjusting the size of a discharge opening of a cone crusher, comprising:

[0087] The instance segmentation module 1 is configured to obtain a first aggregate image of the finished aggregate outside the discharge port of the cone crusher in real time, and segment the first aggregate image using the instance segmentation model to obtain a first segmentation result;

[0088] A parameter calculation module 2 is configured to obtain first aggregate parameters according to the first segmentation result, wherein the first aggregate parameters include one or more of a first average aggregate particle size, a first aggregate gradation curve area difference, a first average aggregate needle shape, and a first average aggregate number;

[0089] The size prediction module 3 is configured to construct a cone crusher discharge opening size prediction model, input the first aggregate parameter into the cone crusher discharge opening size prediction model, and output a cone crusher discharge opening size prediction value;

[0090] The size adjustment module 4 is configured to compare the predicted value of the discharge opening size of the cone crusher with the discharge opening size threshold of the cone crusher, obtain an adjustment instruction according to the comparison result and send it to the fixed cone assembly control system, and the fixed cone assembly control system adjusts the discharge opening size of the cone crusher, and changes the particle size of the aggregate through the adjusted discharge opening size of the cone crusher;

[0091] The loop module 5 is configured to repeatedly execute the instance segmentation module 1 to the size adjustment module 4 until the predicted value of the cone crusher discharge opening size is within the cone crusher discharge opening size threshold range.

[0092] Reference below Figure 6 , which shows an electronic device (eg Figure 1 A schematic diagram of the structure of a computer device 600 (a server or terminal device as shown). Figure 6 The electronic device shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0093] like Figure 6 As shown, the computer device 600 includes a central processing unit (CPU) 601 and a graphics processing unit (GPU) 602, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 603 or a program loaded from a storage part 609 to a random access memory (RAM) 604. In the RAM 604, various programs and data required for the operation of the device 600 are also stored. The CPU 601, GPU 602, ROM 603 and RAM 604 are connected to each other through a bus 605. An input / output (I / O) interface 606 is also connected to the bus 605.

[0094] The following components are connected to the I / O interface 606: an input section 607 including a keyboard, a mouse, etc.; an output section 608 including a display such as a liquid crystal display (LCD), etc. and a speaker, etc.; a storage section 609 including a hard disk, etc.; and a communication section 610 including a network interface card such as a LAN card, a modem, etc. The communication section 610 performs communication processing via a network such as the Internet. A drive 611 may also be connected to the I / O interface 606 as needed. A removable medium 612, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 611 as needed so that a computer program read therefrom is installed into the storage section 609 as needed.

[0095] In particular, according to an embodiment of the present disclosure, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present disclosure includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication part 610, and / or installed from a removable medium 612. When the computer program is executed by a central processing unit (CPU) 601 and a graphics processing unit (GPU) 602, the above-mentioned functions defined in the method of the present application are executed.

[0096] It should be noted that the computer-readable medium described in the present application may be a computer-readable signal medium or a computer-readable medium or any combination of the above two. The computer-readable medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor device, device or component, or any combination of the above. More specific examples of computer-readable media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present application, a computer-readable medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution device, device or device. In the present application, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries a computer-readable program code. This propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The computer-readable signal medium may also be any computer-readable medium other than a computer-readable medium that can send, propagate or transmit a program for use by or in conjunction with an instruction execution device, apparatus or device. The program code contained on the computer-readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, optical cable, RF, etc., or any suitable combination of the above.

[0097] Computer program code for performing the operations of the present application may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0098] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the device, method and computer program product according to various embodiments of the present application. In this regard, each box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flow chart, and the combination of the boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based device that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0099] The modules involved in the embodiments described in this application may be implemented by software or hardware, and the modules described may also be set in a processor.

[0100] As another aspect, the present application also provides a computer-readable medium, which may be included in the electronic device described in the above embodiment; or it may exist independently without being assembled into the electronic device. The above computer-readable medium carries one or more programs, and when the above one or more programs are executed by the electronic device, the electronic device: acquires a first aggregate image of the finished aggregate outside the discharge port of the cone crusher in real time, and segments the first aggregate image using an instance segmentation model to obtain a first segmentation result; acquires a first aggregate parameter according to the first segmentation result, and the first aggregate parameter includes one or more of a first average aggregate particle size, a first aggregate grading curve area difference, a first average aggregate needle shape, and a first average number of aggregates; constructs a cone crusher discharge port size. The first aggregate parameter is input into the cone crusher discharge port size prediction model, and the cone crusher discharge port size prediction value is output; the cone crusher discharge port size prediction value is compared with the cone crusher discharge port size threshold, and an adjustment instruction is obtained according to the comparison result and sent to the fixed cone assembly control system, and the fixed cone assembly control system adjusts the cone crusher discharge port size, and the aggregate particle size is changed by the adjusted cone crusher discharge port size; the above steps are repeated until the cone crusher discharge port size prediction value is within the cone crusher discharge port size threshold range.

[0101] The above description is only a preferred embodiment of the present application and an explanation of the technical principles used. Those skilled in the art should understand that the scope of the invention involved in the present application is not limited to the technical solution formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above invention concept. For example, the above features are replaced with the technical features with similar functions disclosed in this application (but not limited to) by each other to form a technical solution.

Claims

1. A method for intelligently adjusting the size of a cone crusher discharge port, characterized in that: The following steps are involved: S1, acquiring a first aggregate image of finished aggregate outside a discharge port of a cone crusher in real time, and segmenting the first aggregate image using an instance segmentation model to obtain a first segmentation result; S2, acquiring first aggregate parameters according to the first segmentation result, where the first aggregate parameters include one or more of a first average aggregate particle size, a first aggregate gradation curve area difference, a first average aggregate needle shape, and a first average number of aggregates; S3, constructing a cone crusher discharge port size prediction model, inputting the first aggregate parameter into the cone crusher discharge port size prediction model, and outputting a cone crusher discharge port size prediction value. The construction of the cone crusher discharge port size prediction model in step S3 specifically includes: Acquire a second aggregate image of finished aggregate of the cone crusher at different discharge opening sizes; Segmenting the second aggregate image using an instance segmentation model to obtain a second segmentation result; Acquire second aggregate parameters according to the second segmentation result, where the second aggregate parameters include one or more of a second average aggregate particle size, a second aggregate gradation curve area difference, a second average aggregate needle shape, and a second average number of aggregates; The second aggregate parameter is used as the characteristic variable and a multivariate nonlinear regression model is established: i =f(x i ,β)+ε i ; Among them, the vector y i For different discharge opening sizes, i = 1, 2, ..., n, the vector x i =(x i1 ,x i2 ,x i3 ,x i4 ) is the characteristic variable under the influence of different discharge opening sizes, x i1 is the second average aggregate size, x i2 is the area difference of the second aggregate gradation curve, x i3 is the second average aggregate acicularity, x i4 is the second average number of aggregates, β is the parameter vector, ε i is the random error term; The multivariate nonlinear regression model is converted into a multivariate linear model, and the regression coefficients in the multivariate linear model are calculated by using the least square method in combination with the second aggregate parameter to obtain a cone crusher discharge port size prediction model; S4, comparing the predicted value of the discharge port size of the cone crusher with the threshold value of the discharge port size of the cone crusher, obtaining an adjustment instruction according to the comparison result and sending it to the fixed cone assembly control system, and the fixed cone assembly control system adjusts the discharge port size of the cone crusher, and changes the particle size of the aggregate through the adjusted discharge port size of the cone crusher; S5, repeating steps S1-S4 until the predicted value of the cone crusher discharge opening size is within a cone crusher discharge opening size threshold range.

2. The method for intelligently adjusting the discharge opening size of a cone crusher according to claim 1, characterized in that: The first average aggregate particle size and the second average aggregate particle size are average values ​​of aggregate particle sizes within a period of time.

3. The method for intelligently adjusting the discharge opening size of a cone crusher according to claim 1, characterized in that: The area difference of the first aggregate grading curve and the area difference of the second aggregate grading curve are the area differences between the current aggregate grading curve and the standard aggregate grading curve, wherein the standard aggregate grading curve is an average aggregate grading curve within a period of time calculated based on the aggregate image of the finished aggregate produced by the cone crusher after the discharge port has just been adjusted, and the current aggregate grading curve is an average aggregate grading curve calculated based on the aggregate image within a period of time before the current time.

4. The method for intelligently adjusting the discharge opening size of a cone crusher according to claim 1, characterized in that: The first average aggregate needle shape and the second average aggregate needle shape are the average values ​​of the needle shapes of all aggregates within a period of time. The aggregate needle shape is the ratio of the major axis to the minor axis of each aggregate particle size. The first average aggregate number and the second average aggregate number are the average values ​​of the number of aggregates contained in each aggregate image within a period of time.

5. The method for intelligently adjusting the discharge opening size of a cone crusher according to any one of claims 1 to 4, characterized in that: The fixed cone assembly control system includes a PLC, a hydraulic motor and a fixed cone assembly. The hydraulic motor is controlled by the PLC to drive the fixed cone assembly to move so as to change the size of the discharge port of the cone crusher.

6. The method for intelligently adjusting the discharge opening size of a cone crusher according to any one of claims 1 to 4, characterized in that: The instance segmentation model is a trained Mask-RCNN network model.

7. An intelligent adjustment device for the discharge opening size of a cone crusher, characterized in that: include: An instance segmentation module is configured to acquire a first aggregate image of finished aggregate outside a discharge port of a cone crusher in real time, and segment the first aggregate image using an instance segmentation model to obtain a first segmentation result; a parameter calculation module, configured to obtain first aggregate parameters according to the first segmentation result, wherein the first aggregate parameters include one or more of a first average aggregate particle size, a first aggregate gradation curve area difference, a first average aggregate needle shape, and a first average number of aggregates; The size prediction module is configured to construct a cone crusher discharge port size prediction model, input the first aggregate parameter into the cone crusher discharge port size prediction model, and output a cone crusher discharge port size prediction value. The construction of the cone crusher discharge port size prediction model in the size prediction module specifically includes: Acquire a second aggregate image of finished aggregate of the cone crusher at different discharge opening sizes; Segmenting the second aggregate image using an instance segmentation model to obtain a second segmentation result; Acquire second aggregate parameters according to the second segmentation result, where the second aggregate parameters include one or more of a second average aggregate particle size, a second aggregate gradation curve area difference, a second average aggregate needle shape, and a second average number of aggregates; The second aggregate parameter is used as the characteristic variable and a multivariate nonlinear regression model is established: i =f(x i ,β)+ε i ; Among them, the vector y i For different discharge opening sizes, i = 1, 2, ..., n, the vector x i =(x i1 ,x i2 ,x i3 ,x i4 ) is the characteristic variable under the influence of different discharge opening sizes, x i1 is the second average aggregate size, x i2 is the area difference of the second aggregate gradation curve, x i3 is the second average aggregate acicularity, x i4 is the second average number of aggregates, β is the parameter vector, ε i is the random error term; The multivariate nonlinear regression model is converted into a multivariate linear model, and the regression coefficients in the multivariate linear model are calculated by using the least square method in combination with the second aggregate parameter to obtain a cone crusher discharge port size prediction model; A size adjustment module is configured to compare the predicted value of the discharge port size of the cone crusher with a threshold value of the discharge port size of the cone crusher, obtain an adjustment instruction according to the comparison result and send it to the fixed cone assembly control system, and the fixed cone assembly control system adjusts the discharge port size of the cone crusher, and changes the particle size of the aggregate through the adjusted discharge port size of the cone crusher; The loop module is configured to repeatedly execute the instance segmentation module to the size adjustment module until the predicted value of the cone crusher discharge opening size is within a cone crusher discharge opening size threshold range.

8. An electronic device comprising: one or more processors; a storage device for storing one or more programs, When the one or more programs are executed by the one or more processors, the one or more processors implement the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

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