Metering method and system for realizing efficient spreading of MLCC spreader based on deep learning

Abstract

The invention relates to the technical field of MLCC spreaders, and discloses a metering method and system for realizing efficient spillage of an MLCC spillage machine based on deep learning, and the method comprises the steps: collecting the spillage processing duration and spillage transportation duration of the MLCC spillage machine, calculating the total spillage duration of the MLCC spillage machine, and analyzing the efficiency influence factors of the MLCC spillage machine; calculating a stress value of the geometric model, establishing a motion model of the geometric model, and performing displacement analysis on the motion model; carrying out material scattering simulation on the MLCC material scattering machine through the geometric model and the analysis displacement to obtain a material scattering simulation result, and analyzing material scattering efficiency parameters of the MLCC material scattering machine from the material scattering simulation result; and analyzing a factor-efficiency relationship between the efficiency influence factors and the spreading efficiency parameters, determining a spreading adjustment direction of the MLCC spreader according to the factor-efficiency relationship, and carrying out spreading metering on the MLCC spreader by utilizing the spreading adjustment direction. According to the invention, the high-efficiency scattering simplicity of the MLCC scattering machine can be improved.

Description

Technical Field

[0001] The present invention relates to a metering method and system for realizing efficient material spreading of an MLCC spreader based on deep learning, and belongs to the technical field of MLCC spreaders. Background Art

[0002] The MLCC spreader is an intelligent spreading machine composed of MLCC chips. The MLCC spreader can spread the material to be spread evenly in other places, reducing the inefficiency of manual spreading of materials. The materials that can be spread by the MLCC spreader include seeds needed for the land, particles needed for products, etc. However, the efficiency of the MLCC spreader sometimes cannot meet the needs of the industry. If the spreading efficiency of the MLCC spreader is insufficient and the purchase cost of the MLCC spreader is high, the feasibility of the enterprise using the MLCC spreader is greatly reduced.

[0003] At present, there are many factors that affect the spreading efficiency of the MLCC spreader, such as whether the material can be quickly loaded into the pallet, whether the pallet carrying the material to be spread can be quickly transported to the spreading site, etc. The existing technology manually determines the factors affecting the spreading efficiency, and often blindly increases or decreases the machine parameters of the MLCC spreader. For example, the belt conveyor speed that affects the speed at which the material is transmitted is increased. However, after increasing to a certain speed value, the speed at which the material is transmitted does not change, which requires a large number of experiments on the machine parameters of the MLCC spreader. The existing technology experiments on the machine parameters of the MLCC spreader entity, observes the spreading results of the MLCC spreader, and finally derives a machine parameter adjustment method that can improve the spreading efficiency. This method requires continuous adjustment of the machine parameters of the MLCC spreader entity, and the operator needs to rush to the site for experiments, etc., which leads to insufficient simplicity of the experiment.

[0004] Therefore, it is not easy to realize efficient material spreading of MLCC spreaders. Summary of the invention

[0005] The present invention provides a metering method and system for realizing efficient material spreading of an MLCC spreader based on deep learning, and its main purpose is to improve the simplicity of realizing efficient material spreading of an MLCC spreader.

[0006] To achieve the above-mentioned purpose, the present invention provides a metering method for realizing efficient material spreading of an MLCC spreader based on deep learning, comprising: Determine an MLCC spreader, establish a geometric model of the MLCC spreader, and set model parameters of the geometric model, wherein the MLCC spreader includes a silo, an empty mesh basket, an empty mesh basket conveyor line, a belt conveyor line, and a centrifugal force device; Collect the material spreading processing time and the material spreading transportation time of the MLCC spreader, calculate the total material spreading time of the MLCC spreader based on the material spreading processing time and the material spreading transportation time, and analyze the factors affecting the efficiency of the MLCC spreader according to the total material spreading time; Calculating the force values ​​of the geometric model according to the model parameters, establishing a motion model of the geometric model based on the force values, and performing displacement analysis on the motion model to obtain analysis displacement; According to the efficiency influencing factors, the MLCC spreader is simulated by the geometric model and the analytical displacement to obtain a spreading simulation result, and a spreading efficiency parameter of the MLCC spreader is analyzed from the spreading simulation result; The factor-efficiency relationship between the efficiency influencing factors and the material spreading efficiency parameters is analyzed, and the material spreading adjustment direction of the MLCC spreader is determined according to the factor-efficiency relationship. The material spreading adjustment direction is used to perform material spreading metering on the MLCC spreader to obtain the material spreading metering result of the MLCC spreader.

[0007] Optionally, the collecting of the material spreading processing time and the material spreading transportation time of the MLCC spreading machine includes: The length of time that the collected material has been processed in the silo from the time it arrives at the silo to the time it leaves the silo; Collecting the basket processing time experienced by the material from the time when the material arrives at the empty basket to the time when the empty basket leaves the belt conveyor line; collecting the time for the centrifugal force device to scatter the material from the empty net basket; Collecting the first transport time of the empty mesh basket conveying line transporting the empty mesh basket; Collecting the second transport time of the empty net basket transported by the belt conveyor line; The silo processing time, the basket processing time and the spreading processing time are used as the spreading processing time; The first transport duration and the second transport duration are used as the material spreading transport duration.

[0008] Optionally, analyzing the factors affecting the efficiency of the MLCC spreader according to the total spreading time includes: Obtain the silo processing time, basket processing time, spreading processing time, first transportation time, and second transportation time corresponding to the total spreading time; Query the silo exit related to the silo processing time; Obtaining the outlet angle and outlet size of the silo outlet; Querying a net basket plane related to the net basket processing time; Obtaining the plane angle and plane size of the net basket plane; Query the centrifugal force device related to the duration of the spill treatment; Obtaining the device rotation speed and device direction of the centrifugal force device; Querying the empty basket conveyor line associated with the first transport duration; Obtaining the conveying line speed of the empty basket conveying line; Querying the belt conveyor line associated with the second transport duration; Obtaining the belt speed of the belt conveyor line; The outlet angle, the outlet size, the plane angle, the plane size, the device rotation speed, the device rotation direction, the conveyor line speed and the belt speed are used as efficiency influencing factors.

[0009] Optionally, calculating the force value of the geometric model according to the model parameters includes: According to the model parameters, the falling normal force of the geometric model is calculated using the following formula: ; in, is the normal force when falling, Indicates The material and The normal deformation between the materials, represents the elastic normal coefficient in the model parameters, Indicates The material and The vector between the materials, represents the normal damping coefficient in the model parameters, Indicates The material and The speed of the contact point between the materials; According to the model parameters, the falling tangential force of the geometric model is calculated using the following formula: ; in, is the tangential force during falling, represents the tangential stiffness coefficient in the model parameters, represents the tangential displacement, represents the tangential damping coefficient in the model parameters, Indicates The material and The speed of the contact point between the materials; The falling normal force and the falling tangential force are used as force values ​​of the geometric model.

[0010] Optionally, establishing the motion model of the geometric model based on the force value includes: According to the force value, the falling motion model of the geometric model is constructed using the following formula: ; in, represents the falling motion model, is the normal force when falling, is the tangential force during falling, is the normal damping force during falling, It represents the tangential damping force during falling. Indicates The falling speed of the material, Indicates The weight of the material, Indicates the number of materials in the silo. represents the acceleration due to gravity, represents the time variable; The planar motion model of the geometric model is constructed using the following formula: ; in, represents the planar motion model, Indicates The weight of the material, Indicates the number of materials in the silo. represents the time variable, Indicates The translation speed of a material on a plane, represents the normal force on the plane, represents the tangential force on the plane, represents the normal damping force on the plane, It represents the tangential damping force on the plane; The falling motion model and the plane motion model are used as motion models of the geometric model.

[0011] Optionally, performing displacement analysis on the motion model to obtain analysis displacement includes: The falling motion displacement corresponding to the motion model is calculated using the following formula: ; in, represents the displacement of the falling motion, Indicates the unit duration, represents the normal force on the plane, represents the tangential force on the plane, represents the normal damping force on the plane, represents the tangential damping force on the plane, Indicates The weight of the material, Indicates the number of materials in the silo. represents the acceleration due to gravity, represents the time variable;

[0012] The plane motion displacement corresponding to the motion model is calculated using the following formula: ; in, represents the plane motion displacement, Indicates The weight of the material, Indicates the number of materials in the silo. represents the time variable, represents the normal force on the plane, represents the tangential force on the plane, represents the normal damping force on the plane, represents the tangential damping force on the plane, Indicates unit duration; The falling motion displacement and the plane motion displacement are taken as analysis displacements.

[0013] Optionally, analyzing the spreading efficiency parameters of the MLCC spreader from the spreading simulation results includes: Using a preset image collector to collect a material spreading image of the material in the material spreading simulation result; Using a preset image recognition technology to identify a material target in the material spilling image; Calculating a material dispensing position corresponding to the material target; Get material requirement area; Performing regional segmentation on the material demand area to obtain segmented areas; The material spreading uniformity of the material spreading position in the segmented area is calculated using the following formula: ; in, Indicates the uniformity of spreading. Indicates The number of material spill locations within a segmented area, express The mean number of material spill locations in each segmented area, Indicates the number of segmented regions; The material spreading coverage of the material spreading position in the segmented area is calculated using the following formula: ; in, Indicates the coverage of the material. Indicates the The material spill location is the center. is the area of ​​a circle with radius, Indicates the number of materials in the silo. Indicates the area of ​​the material requirement region; The material spreading rate is calculated using the following formula: ; in, Indicates the spreading rate, Indicates the number of material requirement areas, Expressed as The time required to spread materials in each material demand area; The material spreading uniformity, the material spreading coverage and the material spreading rate are used as material spreading efficiency parameters.

[0014] Optionally, determining the material spreading adjustment direction of the MLCC spreader according to the factor-efficiency relationship includes: Calculating the slope of the factor-efficiency relationship curve; Extracting a target slope greater than a preset threshold value from the slopes of the curve; querying the target relationship corresponding to the target slope in the factor-efficiency relationship; Arranging the efficiency influencing factors in the target relationship in order from low efficiency to high efficiency to obtain a factor sequence; The factor sequence is used as the material spreading adjustment direction.

[0015] Optionally, the method of using the material spreading adjustment direction to perform material spreading metering on the MLCC spreader to obtain a material spreading metering result of the MLCC spreader includes: Query whether the current value of the MLCC spreader is within the spreading adjustment direction; If the current value of the MLCC spreader is within the spreading adjustment direction, the current value is numerically adjusted according to the spreading adjustment direction to obtain a spreading metering result of the MLCC spreader; If the current value of the MLCC spreader is not within the spreading adjustment direction, calculate the value-small boundary distance and the value-large boundary distance between the current value and the minimum boundary and the maximum boundary of the spreading adjustment direction respectively; Select the minimum distance from the value-small boundary distance and the value-large boundary distance; Obtaining a target boundary of the minimum distance within the material spreading adjustment direction; The current value is adjusted to the target boundary to obtain the material spreading metering result of the MLCC spreader.

[0016] In order to solve the above problems, the present invention also provides a metering system based on deep learning to realize efficient material spreading of MLCC spreaders, the system comprising: A parameter setting module, used to determine an MLCC spreader, establish a geometric model of the MLCC spreader, and set model parameters of the geometric model, wherein the MLCC spreader includes a silo, an empty mesh basket, an empty mesh basket conveyor line, a belt conveyor line, and a centrifugal force device; A factor analysis module is used to collect the material spreading processing time and the material spreading transportation time of the MLCC spreader, calculate the total material spreading time of the MLCC spreader based on the material spreading processing time and the material spreading transportation time, and analyze the efficiency influencing factors of the MLCC spreader according to the total material spreading time; A displacement analysis module, used to calculate the force value of the geometric model according to the model parameters, establish a motion model of the geometric model based on the force value, and perform displacement analysis on the motion model to obtain analysis displacement; A parameter analysis module, for performing material spreading simulation on the MLCC spreader according to the efficiency influencing factors through the geometric model and the analysis displacement to obtain a material spreading simulation result, and analyzing a material spreading efficiency parameter of the MLCC spreader from the material spreading simulation result; The material spreading metering module is used to analyze the factor-efficiency relationship between the efficiency influencing factors and the material spreading efficiency parameters, determine the material spreading adjustment direction of the MLCC spreader according to the factor-efficiency relationship, and use the material spreading adjustment direction to measure the material of the MLCC spreader to obtain the material spreading metering result of the MLCC spreader.

[0017] Compared with the problems described in the background technology, the embodiment of the present invention collects the material spreading processing time and the material spreading transportation time of the MLCC spreader for subsequent analysis of the machine parameters of the MLCC spreader that affect the material spreading time. The embodiment of the present invention calculates the force value of the geometric model according to the model parameters to perform a force analysis on the movement of the material in the MLCC spreader, thereby subsequently using the analyzed force value to calculate the speed of the material. Furthermore, the embodiment of the present invention performs a displacement analysis on the motion model to analyze the movement of the material at different positions of the MLCC spreader, thereby simulating in the subsequent simulation software to obtain the position change of the material from the starting point to the end point in the entire MLCC spreader. The embodiment of the present invention simulates the material spreading of the MLCC spreader according to the efficiency influencing factors, through the geometric model and the analysis displacement, so as to simulate the material spreading process of the MLCC spreader in a virtual scene, reduce the complexity of the on-site simulation, and improve the simplicity of the simulation of the material spreading process of the MLCC spreader. Therefore, the metering method and system for realizing efficient material spreading of an MLCC spreader based on deep learning provided in the embodiments of the present invention can improve the convenience of realizing efficient material spreading of an MLCC spreader. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 A schematic flow chart of a method for achieving efficient material spreading of an MLCC spreader based on deep learning provided in one embodiment of the present invention; Figure 2 A schematic diagram of a module for implementing a metering method for efficiently spreading materials with an MLCC spreader based on deep learning provided in one embodiment of the present invention.

[0019] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings in conjunction with the embodiments. DETAILED DESCRIPTION

[0020] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0021] The embodiment of the present application provides a metering method for efficiently spreading materials with an MLCC spreader based on deep learning. The execution subject of the metering method for efficiently spreading materials with an MLCC spreader based on deep learning includes but is not limited to at least one of the electronic devices such as a server and a terminal that can be configured to execute the method provided in the embodiment of the present application. In other words, the metering method for efficiently spreading materials with an MLCC spreader based on deep learning can be executed by software or hardware installed on a terminal device or a server device. The server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, etc.

[0022] Embodiment 1: Reference Figure 1 As shown, it is a flow chart of a method for metering a MLCC spreader to efficiently spread materials based on deep learning provided by an embodiment of the present invention. In this embodiment, the method for metering a MLCC spreader to efficiently spread materials based on deep learning includes: S1. Determine an MLCC spreader, establish a geometric model of the MLCC spreader, and set model parameters of the geometric model, wherein the MLCC spreader includes a silo, an empty mesh basket, an empty mesh basket conveyor line, a belt conveyor line, and a centrifugal force device.

[0023] In an embodiment of the present invention, the geometric model refers to a virtual three-dimensional model of the MLCC spreader, including size information of the MLCC spreader. Furthermore, the silo is used to be filled with materials to be spread, and the empty mesh basket is used to load materials, which are particles flowing out of the outlet of the silo. The empty mesh basket conveyor line is used to take the empty mesh basket, transport the empty mesh basket to the top of the belt conveyor line, and when the empty mesh basket on the belt conveyor line is full of materials, it is also necessary to receive the mesh basket filled with materials and transport the mesh basket filled with materials to the centrifugal force device. The belt conveyor line is used to move the mesh basket up, down, left, and right, so as to evenly cover the upper, lower, left, and right positions of the mesh basket with materials. The centrifugal force device is used to emit centrifugal force so that the material in the mesh basket is spread out under the left and right of the centrifugal force.

[0024] S2. Collect the material spreading processing time and the material spreading transportation time of the MLCC spreader, calculate the total material spreading time of the MLCC spreader based on the material spreading processing time and the material spreading transportation time, and analyze the factors affecting the efficiency of the MLCC spreader according to the total material spreading time.

[0025] The embodiment of the present invention collects the material spreading processing time and the material spreading transportation time of the MLCC spreader for subsequent analysis of the machine parameters of the MLCC spreader that affect the material spreading time.

[0026] In one embodiment of the present invention, the collecting of the material spreading processing time and the material spreading transportation time of the MLCC spreader includes: collecting the silo processing time experienced by the material from the time it arrives at the silo to the time it leaves the silo; collecting the net basket processing time experienced by the material from the time it arrives at the empty net basket to the time the empty net basket leaves the belt conveyor line; collecting the scattering processing time when the centrifugal force device scatters the material from the empty net basket; collecting the first transportation time when the empty net basket conveyor line transports the empty net basket; collecting the second transportation time when the belt conveyor line transports the empty net basket; using the silo processing time, the net basket processing time and the scattering processing time as the material spreading processing time; using the first transportation time and the second transportation time as the material spreading transportation time.

[0027] Among them, the first transportation time refers to the total time of taking the empty net basket, transporting the empty net basket to the top of the belt conveyor line, receiving the net basket full of materials when the empty net basket above the belt conveyor line is full of materials, and transporting the net basket full of materials to the centrifugal force device; the second transportation time refers to the total time of the belt conveyor line moving to the bottom of the silo to wait for receiving materials after receiving the empty net basket, and the time of transporting the net basket full of materials to the empty net basket conveyor line.

[0028] Furthermore, in the embodiment of the present invention, the total material spreading time refers to the sum of the material spreading processing time and the material spreading transportation time.

[0029] In one embodiment of the present invention, the efficiency influencing factors of the MLCC spreader are analyzed based on the total material spreading time, including: obtaining the silo processing time, net basket processing time, spreading processing time, first transportation time and second transportation time corresponding to the total material spreading time; querying the silo outlet related to the silo processing time; obtaining the outlet angle and outlet size of the silo outlet; querying the net basket plane related to the net basket processing time; obtaining the plane angle and plane size of the net basket plane; querying the centrifugal force device related to the spreading processing time; obtaining the device speed and device direction of the centrifugal force device; querying the empty net basket conveyor line related to the first transportation time; obtaining the conveyor line speed of the empty net basket conveyor line; querying the belt conveyor line related to the second transportation time; obtaining the belt speed of the belt conveyor line; taking the outlet angle, the outlet size, the plane angle, the plane size, the device speed, the device direction, the conveyor line speed and the belt speed as efficiency influencing factors.

[0030] S3. Calculate the force values ​​of the geometric model according to the model parameters, establish a motion model of the geometric model based on the force values, perform displacement analysis on the motion model, and obtain analysis displacement.

[0031] The embodiment of the present invention calculates the force values ​​of the geometric model according to the model parameters to perform a force analysis on the movement of the material in the MLCC spreader, and then uses the analyzed force values ​​to calculate the speed of the material.

[0032] In one embodiment of the present invention, the calculating the force value of the geometric model according to the model parameters includes: calculating the falling normal force of the geometric model according to the model parameters using the following formula: ; in, is the normal force when falling, Indicates The material and The normal deformation between the materials, represents the elastic normal coefficient in the model parameters, Indicates The material and The vector between the materials, represents the normal damping coefficient in the model parameters, Indicates The material and The speed of the contact point between the materials; According to the model parameters, the falling tangential force of the geometric model is calculated using the following formula: ; in, is the tangential force during falling, represents the tangential stiffness coefficient in the model parameters, represents the tangential displacement, represents the tangential damping coefficient in the model parameters, Indicates The material and The speed of the contact point between the materials; The falling normal force and the falling tangential force are used as force values ​​of the geometric model.

[0033] In one embodiment of the present invention, the establishing of the motion model of the geometric model based on the force value includes: constructing the falling motion model of the geometric model using the following formula according to the force value: ; in, represents the falling motion model, is the normal force when falling, is the tangential force during falling, is the normal damping force during falling, It represents the tangential damping force during falling. Indicates The falling speed of the material, Indicates The weight of the material, Indicates the number of materials in the silo. represents the acceleration due to gravity, represents the time variable; The planar motion model of the geometric model is constructed using the following formula: ; in, represents the planar motion model, Indicates The weight of the material, Indicates the number of materials in the silo. represents the time variable, Indicates The translation speed of a material on a plane, represents the normal force on the plane, represents the tangential force on the plane, represents the normal damping force on the plane, It represents the tangential damping force on the plane; The falling motion model and the plane motion model are used as motion models of the geometric model.

[0034] Among them, the falling motion model refers to the motion change model of the material falling from the silo to the empty mesh basket, and the plane motion model refers to the plane motion change model of the material when it is in the empty mesh basket. It should be noted that the empty mesh basket refers to a planar tray.

[0035] Furthermore, the embodiment of the present invention performs displacement analysis on the motion model to analyze the movement of the material at different positions of the MLCC spreader, thereby simulating in subsequent simulation software the position change of the material from the starting point to the end point in the entire MLCC spreader.

[0036] In one embodiment of the present invention, performing displacement analysis on the motion model to obtain analysis displacement includes: calculating the falling motion displacement corresponding to the motion model using the following formula: ; in, represents the displacement of the falling motion, Indicates the unit duration, represents the normal force on the plane, represents the tangential force on the plane, represents the normal damping force on the plane, represents the tangential damping force on the plane, Indicates The weight of the material, Indicates the number of materials in the silo. represents the acceleration due to gravity, represents the time variable; The plane motion displacement corresponding to the motion model is calculated using the following formula: ; in, represents the plane motion displacement, Indicates The weight of the material, Indicates the number of materials in the silo. represents the time variable, represents the normal force on the plane, represents the tangential force on the plane, represents the normal damping force on the plane, represents the tangential damping force on the plane, Indicates unit duration; The falling motion displacement and the plane motion displacement are taken as analysis displacements.

[0037] in, The speed of the material is The falling displacement at The speed of the material is The plane displacement at time.

[0038] S4. According to the efficiency influencing factors, the MLCC spreader is simulated by using the geometric model and the analytical displacement to obtain a spreading simulation result, and the spreading efficiency parameters of the MLCC spreader are analyzed from the spreading simulation result.

[0039] The embodiment of the present invention simulates the material spreading of the MLCC spreader according to the efficiency influencing factors, through the geometric model and the analytical displacement, so as to simulate the material spreading process of the MLCC spreader in a virtual scene, reduce the complexity of the on-site simulation, and improve the simplicity of the simulation of the material spreading process of the MLCC spreader.

[0040] Optionally, the process of simulating the material spreading of the MLCC spreader through the geometric model and the analytical displacement according to the efficiency influencing factors to obtain the material spreading simulation result refers to: after establishing the geometric model in the simulation software, setting a set of numerical values ​​of the efficiency influencing factors, calculating the change of the analytical displacement of the material on the geometric model under the premise of this numerical value, and using the calculated analytical displacement to determine the positions of material particles in different places of the geometric model until the material is spread out of the MLCC spreader, wherein the material spreading simulation result refers to the result of the material being spread in the spreading place after the material is spread out of the MLCC spreader, and the simulation software is, for example, ADAMS.

[0041] In one embodiment of the present invention, analyzing the spreading efficiency parameters of the MLCC spreader from the spreading simulation results includes: using a preset image collector to collect a material spreading image of the material in the spreading simulation results; using a preset image recognition technology to identify a material target in the material spreading image; calculating a material spreading position corresponding to the material target; obtaining a material demand area; performing regional segmentation on the material demand area to obtain a segmented area; and calculating the spreading uniformity of the material spreading position in the segmented area using the following formula: ; in, Indicates the uniformity of spreading. Indicates The number of material spill locations within a segmented area, express The mean number of material spill locations in each segmented area, Indicates the number of segmented regions; The material spreading coverage of the material spreading position in the segmented area is calculated using the following formula: ; in, Indicates the coverage of the material. Indicates the The material spill location is the center. is the area of ​​a circle with radius, Indicates the number of materials in the silo. Indicates the area of ​​the material requirement region; The material spreading rate is calculated using the following formula: ; in, Indicates the spreading rate, Indicates the number of material requirement areas, Expressed as The time required to spread materials in each material demand area; The material spreading uniformity, the material spreading coverage and the material spreading rate are used as material spreading efficiency parameters.

[0042] Among them, the material scattering image refers to an image of the state of the material being scattered, for example, if the material is scattered on the ground, then the material scattering image refers to the distribution image of the material on the ground, the image recognition technology refers to a deep learning model, such as a YOLOX target detection model, the material scattering position refers to the physical position of the material in the scattered area, and the position of each material in the image can be calculated from the material scattering image. The camera focal length corresponding to the image collector can be used to obtain the mapping relationship between the object in the image and the real object. Therefore, the position distribution relationship of the material in reality can be determined by the position distribution of the material in the image, and the segmentation area refers to a rectangular area of ​​the same size obtained by segmenting the material demand area.

[0043] S5. Analyze the factor-efficiency relationship between the efficiency influencing factors and the material spreading efficiency parameters, determine the material spreading adjustment direction of the MLCC spreader according to the factor-efficiency relationship, and use the material spreading adjustment direction to perform material spreading metering on the MLCC spreader to obtain the material spreading metering result of the MLCC spreader.

[0044] Among them, the factor-efficiency relationship refers to a curve graph between each efficiency influencing factor and the material spreading efficiency parameter. For example, the efficiency influencing factor is the outlet angle, the abscissa of the rectangular coordinate system is the gradually increasing outlet angle, and the ordinate is each material spreading efficiency parameter. That is to say, the factor-efficiency relationship between one type of efficiency influencing factor and one type of material spreading efficiency parameter. Therefore, the number of factor-efficiency relationships is the number of categories of efficiency influencing factors multiplied by the number of categories of material spreading efficiency parameters.

[0045] In one embodiment of the present invention, determining the material spreading adjustment direction of the MLCC spreader based on the factor-efficiency relationship includes: calculating the slope of the curve of the factor-efficiency relationship; extracting the target slope greater than a preset threshold value from the curve slope; querying the target relationship corresponding to the target slope in the factor-efficiency relationship; arranging the efficiency influencing factors in the target relationship in order from low efficiency to high efficiency to obtain a factor sequence; and using the factor sequence as the material spreading adjustment direction.

[0046] The preset slope is set to 0, and the factor sequence refers to a sequence of different numerical values ​​of the same type of efficiency influencing factors.

[0047] In one embodiment of the present invention, the material spreading adjustment direction is used to measure the material spreading of the MLCC spreader to obtain the material spreading measurement result of the MLCC spreader, including: querying whether the current value of the MLCC spreader is within the material spreading adjustment direction; if the current value of the MLCC spreader is within the material spreading adjustment direction, numerically adjusting the current value according to the material spreading adjustment direction to obtain the material spreading measurement result of the MLCC spreader; if the current value of the MLCC spreader is not within the material spreading adjustment direction, calculating the value-small boundary distance and the value-large boundary distance between the current value and the minimum boundary and the maximum boundary of the material spreading adjustment direction respectively; selecting the minimum distance from the value-small boundary distance and the value-large boundary distance; obtaining the target boundary of the minimum distance within the material spreading adjustment direction; adjusting the current value to the target boundary to obtain the material spreading measurement result of the MLCC spreader.

[0048] Among them, the current value refers to the value of the efficiency influencing factor corresponding to the MLCC spreader, the minimum boundary refers to the starting point of the factor sequence, and the maximum boundary refers to the ending point of the factor sequence.

[0049] Optionally, the process of numerically adjusting the current value according to the material spreading adjustment direction refers to a process of gradually changing the current value in the order of factor values ​​from low efficiency to high efficiency.

[0050] Compared with the problems described in the background technology, the embodiments of the invention collect the material spreading processing time and the material spreading transportation time of the MLCC spreader for subsequent analysis of the machine parameters of the MLCC spreader that affect the material spreading time. The embodiments of the invention calculate the force values ​​of the geometric model according to the model parameters to perform force analysis on the movement of the material in the MLCC spreader, thereby subsequently using the analyzed force values ​​to calculate the speed of the material. Furthermore, the embodiments of the invention perform displacement analysis on the motion model to analyze the movement of the material at different positions of the MLCC spreader, thereby simulating in the subsequent simulation software the position change of the material from the starting point to the end point in the entire MLCC spreader. The embodiments of the invention simulate the material spreading of the MLCC spreader according to the efficiency influencing factors, through the geometric model and the analysis displacement, so as to simulate the material spreading process of the MLCC spreader in a virtual scene, reduce the complexity of the on-site simulation, and improve the simplicity of the simulation of the material spreading process of the MLCC spreader. Therefore, the metering method and system for realizing efficient material spreading of an MLCC spreader based on deep learning provided in the embodiments of the present invention can improve the convenience of realizing efficient material spreading of an MLCC spreader.

[0051] Embodiment 2: like Figure 2 As shown, it is a functional module diagram of a metering system for realizing efficient material spreading of an MLCC spreader based on deep learning according to the present invention.

[0052] The metering system 200 for realizing efficient material spreading of MLCC spreaders based on deep learning described in the present invention can be installed in an electronic device. According to the functions implemented, the metering system for realizing efficient material spreading of MLCC spreaders based on deep learning can include a parameter setting module 201, a factor analysis module 202, a displacement analysis module 203, a parameter analysis module 204 and a material spreading metering module 205. The module described in the present invention can also be referred to as a unit, which refers to a series of computer program segments that can be executed by an electronic device processor and can complete fixed functions, which are stored in the memory of the electronic device.

[0053] In the embodiment of the present invention, the functions of each module / unit are as follows: The parameter setting module 201 is used to determine the MLCC spreader, establish a geometric model of the MLCC spreader, and set model parameters of the geometric model, wherein the MLCC spreader includes a silo, an empty mesh basket, an empty mesh basket conveyor line, a belt conveyor line, and a centrifugal force device; The factor analysis module 202 is used to collect the material spreading processing time and the material spreading transportation time of the MLCC spreader, calculate the total material spreading time of the MLCC spreader based on the material spreading processing time and the material spreading transportation time, and analyze the efficiency influencing factors of the MLCC spreader according to the total material spreading time; The displacement analysis module 203 is used to calculate the force value of the geometric model according to the model parameters, establish a motion model of the geometric model based on the force value, and perform displacement analysis on the motion model to obtain analysis displacement; The parameter analysis module 204 is used to perform material spreading simulation on the MLCC spreader according to the efficiency influencing factors through the geometric model and the analysis displacement to obtain a material spreading simulation result, and analyze the material spreading efficiency parameters of the MLCC spreader from the material spreading simulation result; The material spreading metering module 205 is used to analyze the factor-efficiency relationship between the efficiency influencing factors and the material spreading efficiency parameters, determine the material spreading adjustment direction of the MLCC spreader according to the factor-efficiency relationship, and use the material spreading adjustment direction to measure the material of the MLCC spreader to obtain the material spreading metering result of the MLCC spreader.

[0054] In detail, the modules in the metering system 200 for realizing efficient material spreading of MLCC spreaders based on deep learning in the embodiment of the present invention are used in the same manner as described above. Figure 1The same technical means as described in the method for achieving efficient material spreading of MLCC spreaders based on deep learning are used and can produce the same technical effects, so I will not go into details here.

[0055] It is obvious to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0056] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention.

Claims

1. A metering method for realizing efficient material spreading of MLCC spreader based on deep learning, characterized in that: The method comprises: Determine an MLCC spreader, establish a geometric model of the MLCC spreader, and set model parameters of the geometric model, wherein the MLCC spreader includes a silo, an empty mesh basket, an empty mesh basket conveyor line, a belt conveyor line, and a centrifugal force device; Collect the material spreading processing time and the material spreading transportation time of the MLCC spreader, calculate the total material spreading time of the MLCC spreader based on the material spreading processing time and the material spreading transportation time, and analyze the factors affecting the efficiency of the MLCC spreader according to the total material spreading time; Calculating the force values ​​of the geometric model according to the model parameters, establishing a motion model of the geometric model based on the force values, and performing displacement analysis on the motion model to obtain analysis displacement; According to the efficiency influencing factors, the MLCC spreader is simulated by the geometric model and the analytical displacement to obtain a spreading simulation result, and a spreading efficiency parameter of the MLCC spreader is analyzed from the spreading simulation result; The factor-efficiency relationship between the efficiency influencing factors and the material spreading efficiency parameters is analyzed, and the material spreading adjustment direction of the MLCC spreader is determined according to the factor-efficiency relationship. The material spreading adjustment direction is used to perform material spreading metering on the MLCC spreader to obtain the material spreading metering result of the MLCC spreader.

2. The metering method for realizing efficient material spreading of MLCC spreader based on deep learning as claimed in claim 1 is characterized in that: The collecting of the material spreading processing time and the material spreading transportation time of the MLCC spreading machine includes: The length of time that the collected material has been processed in the silo from the time it arrives at the silo to the time it leaves the silo; Collecting the basket processing time experienced by the material from the time when the material arrives at the empty basket to the time when the empty basket leaves the belt conveyor line; collecting the time for the centrifugal force device to scatter the material from the empty net basket; Collecting the first transport time of the empty mesh basket conveying line transporting the empty mesh basket; Collecting the second transport time of the empty net basket transported by the belt conveyor line; The silo processing time, the basket processing time and the spreading processing time are used as the spreading processing time; The first transport duration and the second transport duration are used as the material spreading transport duration.

3. The metering method for realizing efficient material spreading of MLCC spreader based on deep learning as claimed in claim 1 is characterized in that: According to the total spreading time, the factors affecting the efficiency of the MLCC spreading machine are analyzed, including: Obtain the silo processing time, basket processing time, spreading processing time, first transportation time, and second transportation time corresponding to the total spreading time; Query the silo exit related to the silo processing time; Obtaining the outlet angle and outlet size of the silo outlet; Querying a net basket plane related to the net basket processing time; Obtaining the plane angle and plane size of the net basket plane; Query the centrifugal force device related to the duration of the spill treatment; Obtaining the device rotation speed and device direction of the centrifugal force device; Querying the empty basket conveyor line associated with the first transport duration; Obtaining the conveying line speed of the empty basket conveying line; Querying the belt conveyor line associated with the second transport duration; Obtaining the belt speed of the belt conveyor line; The outlet angle, the outlet size, the plane angle, the plane size, the device rotation speed, the device rotation direction, the conveyor line speed and the belt speed are used as efficiency influencing factors.

4. The metering method for realizing efficient material spreading of MLCC spreader based on deep learning as claimed in claim 1 is characterized in that: Calculating the force value of the geometric model according to the model parameters includes: According to the model parameters, the falling normal force of the geometric model is calculated using the following formula: ； in, is the normal force when falling, Indicates The material and The normal deformation between the materials, represents the elastic normal coefficient in the model parameters, Indicates The material and The vector between the materials, represents the normal damping coefficient in the model parameters, Indicates The material and The speed of the contact point between the materials; According to the model parameters, the falling tangential force of the geometric model is calculated using the following formula: ； in, is the tangential force during falling, represents the tangential stiffness coefficient in the model parameters, represents the tangential displacement, represents the tangential damping coefficient in the model parameters, Indicates The material and The speed of the contact point between the materials; The falling normal force and the falling tangential force are used as force values ​​of the geometric model.

5. The metering method for realizing efficient material spreading of MLCC spreader based on deep learning as claimed in claim 1 is characterized in that: The step of establishing the motion model of the geometric model based on the force value comprises: According to the force value, the falling motion model of the geometric model is constructed using the following formula: ； in, represents the falling motion model, is the normal force when falling, is the tangential force during falling, is the normal damping force during falling, It represents the tangential damping force during falling. Indicates The falling speed of the material, Indicates The weight of the material, Indicates the number of materials in the silo. represents the acceleration due to gravity, represents the time variable; The planar motion model of the geometric model is constructed using the following formula: ； in, represents the planar motion model, Indicates The weight of the material, Indicates the number of materials in the silo. represents the time variable, Indicates The translation speed of a material on a plane, represents the normal force on the plane, represents the tangential force on the plane, represents the normal damping force on the plane, It represents the tangential damping force on the plane; The falling motion model and the plane motion model are used as motion models of the geometric model.

6. The metering method for realizing efficient material spreading of MLCC spreader based on deep learning as claimed in claim 1 is characterized in that: The performing displacement analysis on the motion model to obtain analysis displacement includes: The falling motion displacement corresponding to the motion model is calculated using the following formula: ； in, represents the displacement of the falling motion, Indicates the unit duration, represents the normal force on the plane, represents the tangential force on the plane, represents the normal damping force on the plane, represents the tangential damping force on the plane, Indicates The weight of the material, Indicates the number of materials in the silo. represents the acceleration due to gravity, represents the time variable; The plane motion displacement corresponding to the motion model is calculated using the following formula: ； in, represents the plane motion displacement, Indicates The weight of the material, Indicates the number of materials in the silo. represents the time variable, represents the normal force on the plane, represents the tangential force on the plane, represents the normal damping force on the plane, represents the tangential damping force on the plane, Indicates unit duration; The falling motion displacement and the plane motion displacement are taken as analysis displacements.

7. The metering method for realizing efficient material spreading of MLCC spreader based on deep learning as claimed in claim 1 is characterized in that: The analyzing the spreading efficiency parameters of the MLCC spreading machine from the spreading simulation results includes: Using a preset image collector to collect a material spreading image of the material in the material spreading simulation result; Using a preset image recognition technology to identify a material target in the material spilling image; Calculating a material dispensing position corresponding to the material target; Get material requirement area; Performing regional segmentation on the material demand area to obtain segmented areas; The material spreading uniformity of the material spreading position in the segmented area is calculated using the following formula: ； in, Indicates the uniformity of spreading. Indicates The number of material spill locations within a segmented area, express The mean number of material spill locations in each segmented area, Indicates the number of segmented regions; The material spreading coverage of the material spreading position in the segmented area is calculated using the following formula: ； in, Indicates the coverage of the material. Indicates the The material spill location is the center. is the area of ​​a circle with radius, Indicates the number of materials in the silo. Indicates the area of ​​the material requirement region; The material spreading rate is calculated using the following formula: ； in, Indicates the spreading rate, Indicates the number of material requirement areas, Expressed as The time required to spread materials in each material demand area; The material spreading uniformity, the material spreading coverage and the material spreading rate are used as material spreading efficiency parameters.

8. The metering method for realizing efficient material spreading of MLCC spreader based on deep learning as claimed in claim 1 is characterized in that: Determining the material spreading adjustment direction of the MLCC spreader according to the factor-efficiency relationship includes: Calculating the slope of the factor-efficiency relationship curve; Extracting a target slope greater than a preset threshold value from the slopes of the curve; querying the target relationship corresponding to the target slope in the factor-efficiency relationship; Arranging the efficiency influencing factors in the target relationship in order from low efficiency to high efficiency to obtain a factor sequence; The factor sequence is used as the material spreading adjustment direction.

9. The metering method for realizing efficient material spreading of MLCC spreader based on deep learning as claimed in claim 1 is characterized in that: The method of using the material spreading adjustment direction to measure the material spreading of the MLCC spreader to obtain the material spreading measurement result of the MLCC spreader includes: Query whether the current value of the MLCC spreader is within the spreading adjustment direction; If the current value of the MLCC spreader is within the spreading adjustment direction, the current value is numerically adjusted according to the spreading adjustment direction to obtain a spreading metering result of the MLCC spreader; If the current value of the MLCC spreader is not within the spreading adjustment direction, calculate the value-small boundary distance and the value-large boundary distance between the current value and the minimum boundary and the maximum boundary of the spreading adjustment direction respectively; Select the minimum distance from the value-small boundary distance and the value-large boundary distance; Obtaining a target boundary of the minimum distance within the material spreading adjustment direction; The current value is adjusted to the target boundary to obtain the material spreading metering result of the MLCC spreader.

10. A metering system based on deep learning to achieve efficient material spreading of MLCC spreaders, characterized in that: The system comprises: A parameter setting module, used to determine an MLCC spreader, establish a geometric model of the MLCC spreader, and set model parameters of the geometric model, wherein the MLCC spreader includes a silo, an empty mesh basket, an empty mesh basket conveyor line, a belt conveyor line, and a centrifugal force device; A factor analysis module is used to collect the material spreading processing time and the material spreading transportation time of the MLCC spreader, calculate the total material spreading time of the MLCC spreader based on the material spreading processing time and the material spreading transportation time, and analyze the efficiency influencing factors of the MLCC spreader according to the total material spreading time; A displacement analysis module, used to calculate the force value of the geometric model according to the model parameters, establish a motion model of the geometric model based on the force value, and perform displacement analysis on the motion model to obtain analysis displacement; A parameter analysis module, for performing material spreading simulation on the MLCC spreader according to the efficiency influencing factors through the geometric model and the analysis displacement to obtain a material spreading simulation result, and analyzing a material spreading efficiency parameter of the MLCC spreader from the material spreading simulation result; The material spreading metering module is used to analyze the factor-efficiency relationship between the efficiency influencing factors and the material spreading efficiency parameters, determine the material spreading adjustment direction of the MLCC spreader according to the factor-efficiency relationship, and use the material spreading adjustment direction to measure the material of the MLCC spreader to obtain the material spreading metering result of the MLCC spreader.

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