CN116890222B - Intelligent positioning method and device of automatic assembling machine - Google Patents
Intelligent positioning method and device of automatic assembling machine Download PDFInfo
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Abstract
The invention discloses an intelligent positioning method and device of an automatic assembling machine, and relates to the field of control of the assembling machine, wherein the method comprises the following steps: obtaining a first product data set to be assembled; performing quality check on the basis of the first product group to be assembled to obtain a product quality check result; when the product quality check result is that the product passes, a product assembly instruction is generated, when the first automatic assembly machine receives the product assembly instruction, the intelligent positioning space is activated, the first product data set to be assembled is subjected to positioning analysis based on the intelligent positioning space, Q assembly positioning schemes are obtained, optimizing analysis is performed based on the Q assembly positioning schemes, an assembly positioning decision scheme is obtained, and assembly positioning of the first product group to be assembled is performed based on the assembly positioning decision scheme. The technical problems of the prior art that the assembly positioning accuracy of an automatic assembly machine is insufficient, the stability is poor, and the assembly positioning effect of the automatic assembly machine is poor are solved.
Description
Technical Field
The invention relates to the field of control of assembling machines, in particular to an intelligent positioning method and device of an automatic assembling machine.
Background
With the continuous development of automatic production, the automatic assembling machine has replaced the traditional manual assembly. In the prior art, the technical problems of insufficient assembly positioning accuracy and poor stability of an automatic assembly machine and poor assembly positioning effect of the automatic assembly machine exist.
Disclosure of Invention
The application provides an intelligent positioning method and device of an automatic assembling machine. The technical problems of the prior art that the assembly positioning accuracy of an automatic assembly machine is insufficient, the stability is poor, and the assembly positioning effect of the automatic assembly machine is poor are solved. The automatic assembling machine has the advantages that the assembling and positioning accuracy and stability of the automatic assembling machine are improved, the assembling and positioning effect of the automatic assembling machine is improved, meanwhile, the assembling and positioning efficiency and quality of products are improved, and the defective rate of manual operation is reduced.
In view of the above problems, the present application provides an intelligent positioning method and device for an automatic assembling machine.
In a first aspect, the present application provides an intelligent positioning method of an automatic assembling machine, where the method is applied to an intelligent positioning device of an automatic assembling machine, the method includes: obtaining a first product group to be assembled, wherein the first product group to be assembled comprises a first product to be assembled and a second product to be assembled; acquiring basic information of the first product group to be assembled to obtain a first product data set to be assembled; performing quality check on the basis of the first product group to be assembled to obtain a product quality check result; when the product quality check result is that the product passes, a product assembly instruction is generated, and the product assembly instruction is sent to a first automatic assembly machine; when the first automatic assembly machine receives the product assembly instruction, an intelligent positioning space is activated, positioning analysis is carried out on the first product data set to be assembled based on the intelligent positioning space, and Q assembly positioning schemes are obtained, wherein Q is a positive integer greater than 2; and carrying out optimizing analysis based on the Q assembly positioning schemes to obtain an assembly positioning decision scheme, and controlling the first automatic assembling machine to carry out assembly positioning of the first product group to be assembled based on the assembly positioning decision scheme.
In a second aspect, the present application further provides an intelligent positioning device of an automatic assembling machine, wherein the device includes: the device comprises a product obtaining module, a first assembling module and a second assembling module, wherein the product obtaining module is used for obtaining a first product group to be assembled, and the first product group to be assembled comprises a first product to be assembled and a second product to be assembled; the product data set obtaining module is used for collecting basic information of the first product group to be assembled to obtain a first product data set to be assembled; the quality checking module is used for performing quality checking based on the first product group to be assembled to obtain a product quality checking result; the assembly instruction sending module is used for generating a product assembly instruction when the product quality check result is passed, and sending the product assembly instruction to a first automatic assembly machine; the positioning analysis module is used for activating an intelligent positioning space when the first automatic assembly machine receives the product assembly instruction, and performing positioning analysis on the first product data set to be assembled based on the intelligent positioning space to obtain Q assembly positioning schemes, wherein Q is a positive integer greater than 2; the assembly positioning module is used for carrying out optimizing analysis based on the Q assembly positioning schemes to obtain an assembly positioning decision scheme, and controlling the first automatic assembling machine to carry out assembly positioning of the first product group to be assembled based on the assembly positioning decision scheme.
In a third aspect, the present application further provides an electronic device, including: a memory for storing executable instructions; and the processor is used for realizing the intelligent positioning method of the automatic assembling machine when executing the executable instructions stored in the memory.
In a fourth aspect, the present application further provides a computer readable storage medium storing a computer program, which when executed by a processor, implements an intelligent positioning method of an automatic assembling machine provided by the present application.
One or more technical solutions provided in the present application have at least the following technical effects or advantages:
acquiring a first product data set to be assembled by acquiring basic information of a first product group to be assembled; obtaining a product quality check result by performing quality check on the first product group to be assembled; when the product quality check result is that the product passes, a product assembly instruction is generated, and the product assembly instruction is sent to a first automatic assembly machine; when the first automatic assembling machine receives a product assembling instruction, activating an intelligent positioning space, and carrying out positioning analysis on a first product data set to be assembled through the intelligent positioning space to obtain Q assembling positioning schemes; and carrying out optimizing analysis on the Q assembly positioning schemes to obtain an assembly positioning decision scheme, and controlling a first automatic assembly machine to carry out assembly positioning of a first product group to be assembled based on the assembly positioning decision scheme. The automatic assembling machine has the advantages that the assembling and positioning accuracy and stability of the automatic assembling machine are improved, the assembling and positioning effect of the automatic assembling machine is improved, meanwhile, the assembling and positioning efficiency and quality of products are improved, and the defective rate of manual operation is reduced.
The foregoing description is only an overview of the technical solutions of the present application, and may be implemented according to the content of the specification in order to make the technical means of the present application more clearly understood, and in order to make the above-mentioned and other objects, features and advantages of the present application more clearly understood, the following detailed description of the present application will be given.
Drawings
In order to more clearly illustrate the technical solution of the embodiments of the present invention, the following description will briefly explain the drawings of the embodiments of the present invention. It is apparent that the figures in the following description relate only to some embodiments of the invention and are not limiting of the invention.
FIG. 1 is a flow chart of an intelligent positioning method of an automatic assembling machine;
FIG. 2 is a schematic flow chart of obtaining a product quality check result in an intelligent positioning method of an automatic assembling machine;
FIG. 3 is a schematic view of the intelligent positioning device of the automatic assembling machine;
fig. 4 is a schematic structural diagram of an exemplary electronic device of the present application.
Reference numerals illustrate: the device comprises a product obtaining module 11, a product data set obtaining module 12, a quality checking module 13, an assembly instruction sending module 14, a positioning analysis module 15, an assembly positioning module 16, a processor 31, a memory 32, an input device 33 and an output device 34.
Detailed Description
The application provides an intelligent positioning method and device of an automatic assembling machine. The technical problems of the prior art that the assembly positioning accuracy of an automatic assembly machine is insufficient, the stability is poor, and the assembly positioning effect of the automatic assembly machine is poor are solved. The automatic assembling machine has the advantages that the assembling and positioning accuracy and stability of the automatic assembling machine are improved, the assembling and positioning effect of the automatic assembling machine is improved, meanwhile, the assembling and positioning efficiency and quality of products are improved, and the defective rate of manual operation is reduced.
Example 1
Referring to fig. 1, the present application provides an intelligent positioning method of an automatic assembling machine, wherein the method is applied to an intelligent positioning device of the automatic assembling machine, and the method specifically includes the following steps:
step S100: obtaining a first product group to be assembled, wherein the first product group to be assembled comprises a first product to be assembled and a second product to be assembled;
step S200: acquiring basic information of the first product group to be assembled to obtain a first product data set to be assembled;
in particular, an automated assembly machine may be used to assemble the roll and side brackets. And connecting the intelligent positioning device of the automatic assembling machine to obtain a first product group to be assembled, and collecting basic information of the first product group to be assembled to obtain a first product data set to be assembled. The first product group to be assembled comprises a first product to be assembled and a second product to be assembled. The first product group to be assembled can be any coil and any side bracket which are intelligently assembled and positioned by using the intelligent positioning device of the automatic assembling machine. Illustratively, the first product to be assembled is a coil. The second product to be assembled is a side bracket. The first product data set to be assembled comprises basic information such as the size, the shape, the material composition and the like of the first product to be assembled and the second product to be assembled respectively.
Step S300: performing quality check on the basis of the first product group to be assembled to obtain a product quality check result;
further, as shown in fig. 2, step S300 of the present application further includes:
step S310: performing quality check on the first product to be assembled to obtain a first product quality check result;
further, step S310 of the present application further includes:
step S311: collecting sample defect information of the first product to be assembled to obtain a product sample defect database;
step S312: obtaining a preset defect convolution feature set according to the product sample defect database;
step S313: obtaining product image data of the first product to be assembled, and performing grid division on the product image data to obtain image division data;
step S314: performing traversal convolution calculation on the image division data based on the preset defect convolution feature set to obtain product defect information;
step S315: based on the product defect information, obtaining a product defect grade, and judging whether the product defect grade is smaller than a preset defect grade or not to obtain a product defect judging result;
step S316: and generating the first product quality check result based on the product defect judgment result.
Specifically, the intelligent positioning device of the automatic assembling machine is connected, sample defect information of a first product to be assembled is collected, and a product sample defect database is obtained. The product sample defect database includes a plurality of sample defect information. Each sample defect information comprises the shape, position, area and size of the historical surface defect corresponding to the first product to be assembled. Historical surface defects include cracks, oil stains, scratches, and the like. And taking the shape, the position, the area and the size of the historical surface defects corresponding to the plurality of sample defect information in the product sample defect database as a preset defect convolution characteristic set.
Further, image acquisition is carried out on the first product to be assembled to obtain product image data, and grid division is carried out on the product image data to obtain image division data. Then, performing traversal convolution calculation on the image division data according to the preset defect convolution feature set to obtain product defect information, and performing defect grade matching on the product defect information to obtain product defect grade. The traversal convolution calculation means that traversal feature recognition is carried out on the image division data according to a preset convolution feature set. The product defect information comprises the shape, the position, the area and the size of the surface defect corresponding to the first product to be assembled. Illustratively, when defect levels are matched with product defect information, the product defect information is input into a defect level identification library to obtain a product defect level. The defect level identification library includes a plurality of sets of historical defect level identification data. Each set of historical defect level identification data includes historical product defect information and historical product defect levels.
Further, judging whether the defect grade of the product is smaller than the preset defect grade, and obtaining a product defect judging result. The product defect judging result comprises that the product defect grade is smaller than the preset defect grade, or the product defect grade is larger than/equal to the preset defect grade. When the product defect judging result is that the product defect grade is smaller than the preset defect grade, the obtained first product quality checking result is passed. Otherwise, the obtained first product quality check result is not passed.
The technical effects of obtaining an accurate first product quality check result by carrying out quality check on the first product to be assembled and improving the assembly positioning reliability of the automatic assembly machine are achieved.
Step S320: performing quality check on the second product to be assembled to obtain a second product quality check result;
step S330: inputting the first product quality check result and the second product quality check result into a bilateral judgment device, and obtaining the product quality check result according to bilateral judgment rules in the bilateral judgment device.
Specifically, the quality of the second product to be assembled is checked, and a second product quality check result is obtained. The second product quality check results in pass/fail. The method of performing quality check on the second product to be assembled is the same as the method of performing quality check on the first product to be assembled, and for brevity of description, details are not repeated here. Further, the first product quality check result and the second product quality check result are input into a bilateral judgment device, and the bilateral judgment device comprises a bilateral judgment rule. The bilateral judgment rule comprises: and when the first product quality check result is passing and the second product quality check result is passing, the obtained product quality check result is passing. Otherwise, the obtained product quality check result is not passed. The technical effects of analyzing the first product quality check result and the second product quality check result through the bilateral judgment device and obtaining an accurate product quality check result are achieved, and therefore the assembly positioning accuracy of the product is improved.
Further, step S330 of the present application further includes:
step S331: when the product quality check result is that the product quality check result does not pass, obtaining a product abnormality check factor;
step S332: generating a product abnormality instruction based on the product abnormality verification factor;
step S333: and carrying out abnormal management on the first product group to be assembled according to the product abnormal instruction.
Specifically, when the product quality check result is that the product fails, a product abnormality check factor is obtained. When the product abnormality checking factor is obtained, the intelligent positioning device of the automatic assembling machine automatically generates a product abnormality instruction, and performs abnormality management on the first product group to be assembled according to the product abnormality instruction. And when the product quality check result is not passed, the corresponding first product quality check result and/or second product quality check result which are not passed. The product abnormality instruction is early warning prompt information used for representing that the product quality check result is not passed and abnormality management is required to be carried out on the first product to be assembled and/or the second product to be assembled in the first product group to be assembled. The anomaly management includes product replacement or the like of the first product to be assembled and/or the second product to be assembled in the first product group to be assembled. When the product quality check result is that the product fails, abnormal management is carried out on the first product group to be assembled according to the product abnormal instruction, so that the comprehensive technical effect of assembly positioning management of the automatic assembly machine is improved.
Step S400: when the product quality check result is that the product passes, a product assembly instruction is generated, and the product assembly instruction is sent to a first automatic assembly machine;
step S500: when the first automatic assembly machine receives the product assembly instruction, an intelligent positioning space is activated, positioning analysis is carried out on the first product data set to be assembled based on the intelligent positioning space, and Q assembly positioning schemes are obtained, wherein Q is a positive integer greater than 2;
further, step S500 of the present application further includes:
step S510: based on the big data, obtaining a plurality of assembly positioning databases;
step S520: traversing the plurality of assembly positioning databases to train and test to obtain a plurality of assembly positioners, wherein the plurality of assembly positioners are provided with a plurality of corresponding test positioning accuracy marks;
step S530: and performing descending order arrangement on the plurality of assembly positioners based on the plurality of test positioning accuracy marks, marking the first Q assembly positioners as Q intelligent positioners, and integrating the Q intelligent positioners into the intelligent positioning space.
Specifically, a plurality of assembly location databases are obtained based on the big data collection history assembly location data. Each assembly location database includes a plurality of sets of historical assembly location data. Each set of historical assembly location data includes a historical to-be-assembled product dataset and a historical assembly location scheme. And then traversing a plurality of assembly positioning databases for training and testing to obtain a plurality of assembly positioners. Illustratively, random data division is performed on each assembly positioning database according to a preset data division ratio, so as to obtain a training data set and a test data set corresponding to each assembly positioning database. The preset data dividing ratio includes 7: 3. 6:4, etc. Based on the BP neural network, cross supervision training is respectively carried out on training data sets corresponding to each assembly positioning database, and a plurality of assembly positioners are obtained. And inputting the test data set of each assembly positioning database into the corresponding assembly positioner to obtain a plurality of test positioning accuracy marks corresponding to the plurality of assembly positioners. Each test positioning accuracy identifier comprises an output accuracy parameter corresponding to each assembly positioner. The BP neural network is a multi-layer feedforward neural network trained according to an error back propagation algorithm. The BP neural network comprises an input layer, a plurality of layers of neurons and an output layer. The BP neural network can perform forward calculation and backward calculation. When calculating in the forward direction, the input information is processed layer by layer from the input layer through a plurality of layers of neurons and is turned to the output layer, and the state of each layer of neurons only affects the state of the next layer of neurons. If the expected output cannot be obtained at the output layer, the reverse calculation is carried out, the error signal is returned along the original connecting path, and the weight of each neuron is modified to minimize the error signal. Each assembly locator includes an input layer, an hidden layer, and an output layer.
Further, the plurality of assembly positioners are arranged in descending order according to the plurality of test positioning accuracy marks, namely, the plurality of assembly positioners are ordered according to the sizes of the plurality of test positioning accuracy marks, the larger the test positioning accuracy marks are, the more forward the ordering of the corresponding assembly positioners is, the first Q assembly positioners are set to be Q intelligent positioners, and the Q intelligent positioners are added to the intelligent positioning space. And then, when the product quality check result is that the product quality check result passes, the intelligent positioning device of the automatic assembling machine automatically generates a product assembling instruction and sends the product assembling instruction to the first automatic assembling machine. When the first automatic assembling machine receives a product assembling instruction, the intelligent positioning space is activated, and the first product data set to be assembled is respectively input into Q intelligent positioners in the intelligent positioning space to obtain Q assembling and positioning schemes. The product assembly instruction is instruction information used for representing that a product quality check result is passing and capable of assembling and positioning a first product group to be assembled. The first automatic assembling machine may be a spindle automatic assembling machine in the prior art. The intelligent positioning space comprises Q intelligent positioners. And Q is a positive integer greater than 2. Each assembly positioning scheme comprises an assembly positioning position corresponding to the first product group to be assembled and control parameter information of the first automatic assembling machine. The technical effects of carrying out positioning analysis on the first product data set to be assembled through the intelligent positioning space and obtaining Q assembly positioning schemes are achieved, so that the assembly positioning reliability of the automatic assembly machine is improved.
Step S600: and carrying out optimizing analysis based on the Q assembly positioning schemes to obtain an assembly positioning decision scheme, and controlling the first automatic assembling machine to carry out assembly positioning of the first product group to be assembled based on the assembly positioning decision scheme.
Further, step S600 of the present application further includes:
step S610: obtaining optimizing excitation conditions according to Q test positioning accuracy marks corresponding to the Q intelligent positioners;
step S620: based on the optimizing excitation condition, acquiring a first assembly positioning scheme according to the Q assembly positioning schemes, and carrying out fitness analysis on the first assembly positioning scheme to acquire first assembly positioning fitness;
further, step S620 of the present application further includes:
step S621: building an fitness analysis model, wherein the fitness analysis model comprises multistage preset fitness analysis indexes, and the multistage preset fitness analysis indexes comprise assembly positioning adhesion degree, assembly positioning stability and assembly positioning uniformity;
step S622: inputting the first assembly positioning scheme into the fitness analysis model to obtain a multi-stage fitness analysis index value;
step S623: and weighting the multi-stage fitness analysis index values based on preset fitness weighting constraint features to generate the first assembly positioning fitness.
Specifically, Q test positioning accuracy marks corresponding to Q intelligent positioners are set as optimizing excitation conditions. And sequencing the Q assembly positioning schemes according to the optimizing excitation condition to obtain a first assembly positioning scheme, a second assembly positioning scheme, a third assembly positioning scheme … … and a Q assembly positioning scheme.
And further, carrying out fitness analysis on the first assembly positioning scheme, namely taking the first assembly positioning scheme as input information, inputting a fitness analysis model, evaluating the first assembly positioning scheme by the fitness analysis model according to a multi-level preset fitness analysis index to obtain a multi-level fitness analysis index value, and weighting the multi-level fitness analysis index value according to a preset fitness weighting constraint characteristic to generate the first assembly positioning fitness. The fitness analysis model comprises a multi-level preset fitness analysis index, wherein the multi-level preset fitness analysis index comprises assembly positioning adhesion degree, assembly positioning stability and assembly positioning uniformity. The multi-level fitness analysis index value comprises an assembly positioning adhesion evaluation index, an assembly positioning stability evaluation index and an assembly positioning uniformity evaluation index which correspond to the first assembly positioning scheme. The preset fitness weighting constraint characteristic comprises an assembly positioning adhesiveness weight coefficient, an assembly positioning stability weight coefficient and an assembly positioning uniformity weight coefficient which are preset and determined by the intelligent positioning device of the automatic assembly machine. Illustratively, when the multi-level fitness analysis index value is weighted according to the preset fitness weighting constraint feature, the assembly positioning adhesion evaluation index and the assembly positioning adhesion weight coefficient are multiplied to obtain the weighted assembly positioning adhesion. And similarly, multiplying the assembly positioning stability evaluation index by an assembly positioning stability weight coefficient to obtain the weighted assembly positioning stability. And multiplying the assembly positioning uniformity evaluation index by an assembly positioning uniformity weight coefficient to obtain weighted assembly positioning uniformity. Outputting the sum of the weighted assembly positioning adhesion degree, the weighted assembly positioning stability and the weighted assembly positioning uniformity as the first assembly positioning fitness.
Illustratively, when the fitness analysis model is built, the intelligent positioning device of the automatic assembling machine is connected, and multiple groups of fitness analysis data are collected. Each group of fitness analysis data comprises historical assembly positioning scheme information, and a historical assembly positioning adhesion evaluation index, a historical assembly positioning stability evaluation index and a historical assembly positioning uniformity evaluation index which correspond to the historical assembly positioning scheme information. And (3) continuously self-training and learning the multiple groups of fitness analysis data to a convergence state to obtain the fitness analysis model. The fitness analysis model comprises an input layer, an implicit layer and an output layer. The fitness analysis model has the function of evaluating the input assembly positioning scheme according to the multi-level preset fitness analysis index.
Step S630: based on the optimizing excitation condition, obtaining a second assembly positioning scheme according to the Q assembly positioning schemes, and carrying out fitness analysis on the second assembly positioning scheme to obtain second assembly positioning fitness;
step S640: judging whether the first assembly positioning fitness is smaller than the second assembly positioning fitness;
step S650: if the first assembly positioning fitness is greater than/equal to the second assembly positioning fitness, setting the first assembly positioning scheme as a current optimal assembly positioning scheme, and eliminating the second assembly positioning scheme;
Step S660: if the first assembly positioning fitness is smaller than the second assembly positioning fitness, setting the second assembly positioning scheme as the current optimal assembly positioning scheme, and eliminating the first assembly positioning scheme;
step S670: and carrying out iterative optimization based on the current optimal assembly positioning scheme until the optimization analysis iterative characteristic is met, and obtaining the assembly positioning decision scheme.
Specifically, the second assembly positioning scheme is subjected to fitness analysis, and the second assembly positioning fitness is obtained. And then judging whether the first assembly positioning fitness is smaller than the second assembly positioning fitness. When the first assembly positioning fitness is greater than/equal to the second assembly positioning fitness, setting the first assembly positioning scheme as the current optimal assembly positioning scheme, and eliminating the second assembly positioning scheme. When the first assembly positioning fitness is smaller than the second assembly positioning fitness, setting the second assembly positioning scheme as the current optimal assembly positioning scheme, and eliminating the first assembly positioning scheme. And further, performing iterative optimization based on the current optimal assembly positioning scheme until the optimization analysis iterative characteristic is met, obtaining an assembly positioning decision scheme, and controlling a first automatic assembly machine to perform assembly positioning on the first product group to be assembled according to the assembly positioning decision scheme. The second assembly positioning fitness is the same as the first assembly positioning fitness in the same manner, and is not described herein for brevity. The iterative optimization based on the current optimal assembly positioning scheme is the same as the current optimal assembly positioning scheme, and is not repeated here for the sake of brevity of the description. The optimizing analysis iteration characteristic comprises an iteration optimizing frequency threshold value which is preset and determined by the intelligent positioning device of the automatic assembling machine. The assembly positioning decision scheme comprises a current optimal assembly positioning scheme meeting optimization analysis iteration characteristics. The technical effects of obtaining an accurate assembly positioning decision scheme by carrying out iterative optimization analysis on Q assembly positioning schemes and improving the assembly positioning accuracy of an automatic assembly machine are achieved.
In summary, the intelligent positioning method of the automatic assembling machine provided by the application has the following technical effects:
1. acquiring a first product data set to be assembled by acquiring basic information of a first product group to be assembled; obtaining a product quality check result by performing quality check on the first product group to be assembled; when the product quality check result is that the product passes, a product assembly instruction is generated, and the product assembly instruction is sent to a first automatic assembly machine; when the first automatic assembling machine receives a product assembling instruction, activating an intelligent positioning space, and carrying out positioning analysis on a first product data set to be assembled through the intelligent positioning space to obtain Q assembling positioning schemes; and carrying out optimizing analysis on the Q assembly positioning schemes to obtain an assembly positioning decision scheme, and controlling a first automatic assembly machine to carry out assembly positioning of a first product group to be assembled based on the assembly positioning decision scheme. The automatic assembling machine has the advantages that the assembling and positioning accuracy and stability of the automatic assembling machine are improved, the assembling and positioning effect of the automatic assembling machine is improved, meanwhile, the assembling and positioning efficiency and quality of products are improved, and the defective rate of manual operation is reduced.
2. And analyzing the first product quality check result and the second product quality check result through the bilateral judgment device to obtain an accurate product quality check result, thereby improving the assembly positioning accuracy of the product.
3. And positioning analysis is carried out on the first product data set to be assembled through the intelligent positioning space, and Q assembly positioning schemes are obtained, so that the assembly positioning reliability of the automatic assembly machine is improved.
Example two
Based on the same inventive concept as the intelligent positioning method of an automatic assembling machine in the foregoing embodiment, the present invention also provides an intelligent positioning device of an automatic assembling machine, referring to fig. 3, the device includes:
a product obtaining module 11, wherein the product obtaining module 11 is configured to obtain a first product group to be assembled, and the first product group to be assembled includes a first product to be assembled and a second product to be assembled;
a product data set obtaining module 12, where the product data set obtaining module 12 is configured to collect basic information of the first product group to be assembled to obtain a first product data set to be assembled;
the quality checking module 13 is used for performing quality checking based on the first product group to be assembled to obtain a product quality checking result;
the assembly instruction sending module 14 is configured to generate a product assembly instruction when the product quality check result is passed, and send the product assembly instruction to a first automatic assembly machine;
The positioning analysis module 15 is configured to activate an intelligent positioning space when the first automatic assembly machine receives the product assembly instruction, and perform positioning analysis on the first product data set to be assembled based on the intelligent positioning space to obtain Q assembly positioning schemes, where Q is a positive integer greater than 2;
the assembly positioning module 16 is configured to perform optimizing analysis based on the Q assembly positioning schemes, obtain an assembly positioning decision scheme, and control the first automatic assembling machine to perform assembly positioning of the first product group to be assembled based on the assembly positioning decision scheme.
Further, the device further comprises:
the first product quality checking module is used for checking the quality of the first product to be assembled to obtain a first product quality checking result;
the second product quality checking module is used for checking the quality of the second product to be assembled to obtain a second product quality checking result;
the product quality check result obtaining module is used for inputting the first product quality check result and the second product quality check result into a double-sided judging device and obtaining the product quality check result according to a double-sided judging rule in the double-sided judging device.
Further, the device further comprises:
the sample acquisition module is used for acquiring sample defect information of the first product to be assembled to obtain a product sample defect database;
the defect convolution characteristic obtaining module is used for obtaining a preset defect convolution characteristic set according to the product sample defect database;
the grid division module is used for obtaining the product image data of the first product to be assembled, and carrying out grid division on the product image data to obtain image division data;
the product defect information obtaining module is used for performing traversal convolution calculation on the image division data based on the preset defect convolution feature set to obtain product defect information;
the defect grade judging module is used for obtaining the defect grade of the product based on the defect information of the product, judging whether the defect grade of the product is smaller than a preset defect grade or not and obtaining a product defect judging result;
the first execution module is used for generating the first product quality check result based on the product defect judgment result.
Further, the device further comprises:
the abnormal verification factor obtaining module is used for obtaining the abnormal verification factor of the product when the product quality verification result is not passed;
the product abnormal instruction generation module is used for generating a product abnormal instruction based on the product abnormal verification factor;
and the abnormality management module is used for carrying out abnormality management on the first product group to be assembled according to the product abnormality instruction.
Further, the device further comprises:
the second execution module is used for obtaining a plurality of assembly positioning databases based on big data;
the training test module is used for traversing the plurality of assembly positioning databases to train and test to obtain a plurality of assembly positioners, and the plurality of assembly positioners are provided with a plurality of corresponding test positioning accuracy marks;
the integration module is used for carrying out descending arrangement on the plurality of assembly positioners based on the plurality of test positioning accuracy marks, marking the first Q assembly positioners as Q intelligent positioners and integrating the Q intelligent positioners into the intelligent positioning space.
Further, the device further comprises:
the optimizing excitation condition obtaining module is used for obtaining optimizing excitation conditions according to Q test positioning accuracy marks corresponding to the Q intelligent positioners;
the first assembly positioning fitness obtaining module is used for obtaining a first assembly positioning scheme according to the Q assembly positioning schemes based on the optimizing excitation condition, and carrying out fitness analysis on the first assembly positioning scheme to obtain first assembly positioning fitness;
the second assembly positioning fitness obtaining module is used for obtaining a second assembly positioning scheme according to the Q assembly positioning schemes based on the optimizing excitation condition, and carrying out fitness analysis on the second assembly positioning scheme to obtain second assembly positioning fitness;
the fitness judging module is used for judging whether the first assembly positioning fitness is smaller than the second assembly positioning fitness or not;
the third execution module is used for setting the first assembly positioning scheme as the current optimal assembly positioning scheme and eliminating the second assembly positioning scheme if the first assembly positioning fitness is greater than/equal to the second assembly positioning fitness;
The fourth execution module is used for setting the second assembly positioning scheme as the current optimal assembly positioning scheme and eliminating the first assembly positioning scheme if the first assembly positioning fitness is smaller than the second assembly positioning fitness;
and the iterative optimization module is used for carrying out iterative optimization based on the current optimal assembly positioning scheme until the optimization analysis iterative characteristics are met, and then the assembly positioning decision scheme is obtained.
Further, the device further comprises:
the building module is used for building an adaptability analysis model, wherein the adaptability analysis model comprises multistage preset adaptability analysis indexes, and the multistage preset adaptability analysis indexes comprise assembly positioning adhesion degree, assembly positioning stability and assembly positioning uniformity;
the fitness analysis index value obtaining module is used for inputting the first assembly positioning scheme into the fitness analysis model to obtain multi-stage fitness analysis index values;
and the index value weighting module is used for weighting the multi-stage fitness analysis index values based on preset fitness weighting constraint features to generate the first assembly positioning fitness.
The intelligent positioning device of the automatic assembling machine provided by the embodiment of the invention can execute the intelligent positioning method of the automatic assembling machine provided by any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method.
All the included modules are only divided according to the functional logic, but are not limited to the above-mentioned division, so long as the corresponding functions can be realized; in addition, the specific names of the functional modules are only for distinguishing from each other, and are not used for limiting the protection scope of the present invention.
Example III
Fig. 4 is a schematic structural diagram of an electronic device provided in a third embodiment of the present invention, and shows a block diagram of an exemplary electronic device suitable for implementing an embodiment of the present invention. The electronic device shown in fig. 4 is only an example and should not be construed as limiting the functionality and scope of use of the embodiments of the present invention. As shown in fig. 4, the electronic device includes a processor 31, a memory 32, an input device 33, and an output device 34; the number of processors 31 in the electronic device may be one or more, in fig. 4, one processor 31 is taken as an example, and the processors 31, the memory 32, the input device 33 and the output device 34 in the electronic device may be connected by a bus or other means, in fig. 4, by bus connection is taken as an example.
The memory 32 is used as a computer readable storage medium for storing software programs, computer executable programs and modules, such as program instructions/modules corresponding to an intelligent positioning method of an automatic assembling machine in an embodiment of the present invention. The processor 31 executes various functional applications of the computer device and data processing by running software programs, instructions and modules stored in the memory 32, i.e. implements the above-described intelligent positioning method of an automatic assembling machine.
The application provides an intelligent positioning method of an automatic assembling machine, wherein the method is applied to an intelligent positioning device of the automatic assembling machine, and the method comprises the following steps: acquiring a first product data set to be assembled by acquiring basic information of a first product group to be assembled; obtaining a product quality check result by performing quality check on the first product group to be assembled; when the product quality check result is that the product passes, a product assembly instruction is generated, and the product assembly instruction is sent to a first automatic assembly machine; when the first automatic assembling machine receives a product assembling instruction, activating an intelligent positioning space, and carrying out positioning analysis on a first product data set to be assembled through the intelligent positioning space to obtain Q assembling positioning schemes; and carrying out optimizing analysis on the Q assembly positioning schemes to obtain an assembly positioning decision scheme, and controlling a first automatic assembly machine to carry out assembly positioning of a first product group to be assembled based on the assembly positioning decision scheme. The technical problems of the prior art that the assembly positioning accuracy of an automatic assembly machine is insufficient, the stability is poor, and the assembly positioning effect of the automatic assembly machine is poor are solved. The automatic assembling machine has the advantages that the assembling and positioning accuracy and stability of the automatic assembling machine are improved, the assembling and positioning effect of the automatic assembling machine is improved, meanwhile, the assembling and positioning efficiency and quality of products are improved, and the defective rate of manual operation is reduced.
Note that the above is only a preferred embodiment of the present invention and the technical principle applied. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, while the invention has been described in connection with the above embodiments, the invention is not limited to the embodiments, but may be embodied in many other equivalent forms without departing from the spirit or scope of the invention, which is set forth in the following claims.
Claims (4)
1. An intelligent positioning method of an automatic assembling machine, which is characterized by comprising the following steps:
obtaining a first product group to be assembled, wherein the first product group to be assembled comprises a first product to be assembled and a second product to be assembled;
acquiring basic information of the first product group to be assembled to obtain a first product data set to be assembled;
performing quality check on the basis of the first product group to be assembled to obtain a product quality check result;
when the product quality check result is that the product passes, a product assembly instruction is generated, and the product assembly instruction is sent to a first automatic assembly machine;
When the first automatic assembly machine receives the product assembly instruction, an intelligent positioning space is activated, positioning analysis is carried out on the first product data set to be assembled based on the intelligent positioning space, and Q assembly positioning schemes are obtained, wherein Q is a positive integer greater than 2;
optimizing analysis is carried out based on the Q assembly positioning schemes to obtain an assembly positioning decision scheme, and the first automatic assembling machine is controlled to carry out assembly positioning of the first product group to be assembled based on the assembly positioning decision scheme;
the quality check is performed based on the first product group to be assembled to obtain a product quality check result, which comprises the following steps:
performing quality check on the first product to be assembled to obtain a first product quality check result;
performing quality check on the second product to be assembled to obtain a second product quality check result;
inputting the first product quality check result and the second product quality check result into a bilateral judgment device, and obtaining the product quality check result according to bilateral judgment rules in the bilateral judgment device;
the quality check of the first product to be assembled is performed to obtain a first product quality check result, which comprises the following steps:
Collecting sample defect information of the first product to be assembled to obtain a product sample defect database;
obtaining a preset defect convolution feature set according to the product sample defect database;
obtaining product image data of the first product to be assembled, and performing grid division on the product image data to obtain image division data;
performing traversal convolution calculation on the image division data based on the preset defect convolution feature set to obtain product defect information;
based on the product defect information, obtaining a product defect grade, and judging whether the product defect grade is smaller than a preset defect grade or not to obtain a product defect judging result;
generating the first product quality check result based on the product defect judgment result;
when the product quality check result is that the product quality check result does not pass, obtaining a product abnormality check factor;
generating a product abnormality instruction based on the product abnormality verification factor;
performing exception management on the first product group to be assembled according to the product exception instruction;
based on the big data, obtaining a plurality of assembly positioning databases;
traversing the plurality of assembly positioning databases to train and test to obtain a plurality of assembly positioners, wherein the plurality of assembly positioners are provided with a plurality of corresponding test positioning accuracy marks;
The plurality of assembly positioners are arranged in descending order based on the plurality of test positioning accuracy marks, the first Q assembly positioners are marked as Q intelligent positioners, and the Q intelligent positioners are integrated into the intelligent positioning space;
optimizing analysis is carried out based on the Q assembly positioning schemes to obtain an assembly positioning decision scheme, which comprises the following steps:
obtaining optimizing excitation conditions according to Q test positioning accuracy marks corresponding to the Q intelligent positioners;
based on the optimizing excitation condition, acquiring a first assembly positioning scheme according to the Q assembly positioning schemes, and carrying out fitness analysis on the first assembly positioning scheme to acquire first assembly positioning fitness;
based on the optimizing excitation condition, obtaining a second assembly positioning scheme according to the Q assembly positioning schemes, and carrying out fitness analysis on the second assembly positioning scheme to obtain second assembly positioning fitness;
judging whether the first assembly positioning fitness is smaller than the second assembly positioning fitness;
if the first assembly positioning fitness is greater than/equal to the second assembly positioning fitness, setting the first assembly positioning scheme as a current optimal assembly positioning scheme, and eliminating the second assembly positioning scheme;
If the first assembly positioning fitness is smaller than the second assembly positioning fitness, setting the second assembly positioning scheme as the current optimal assembly positioning scheme, and eliminating the first assembly positioning scheme;
performing iterative optimization based on the current optimal assembly positioning scheme until optimization analysis iterative characteristics are met, and obtaining the assembly positioning decision scheme;
wherein obtaining a first assembly positioning fitness comprises:
building an fitness analysis model, wherein the fitness analysis model comprises multistage preset fitness analysis indexes, and the multistage preset fitness analysis indexes comprise assembly positioning adhesion degree, assembly positioning stability and assembly positioning uniformity;
inputting the first assembly positioning scheme into the fitness analysis model to obtain a multi-stage fitness analysis index value;
and weighting the multi-stage fitness analysis index values based on preset fitness weighting constraint features to generate the first assembly positioning fitness.
2. An intelligent positioning device of an automated assembly machine, wherein the device is configured to perform the method of claim 1, the device comprising:
the device comprises a product obtaining module, a first assembling module and a second assembling module, wherein the product obtaining module is used for obtaining a first product group to be assembled, and the first product group to be assembled comprises a first product to be assembled and a second product to be assembled;
The product data set obtaining module is used for collecting basic information of the first product group to be assembled to obtain a first product data set to be assembled;
the quality checking module is used for performing quality checking based on the first product group to be assembled to obtain a product quality checking result;
the assembly instruction sending module is used for generating a product assembly instruction when the product quality check result is passed, and sending the product assembly instruction to a first automatic assembly machine;
the positioning analysis module is used for activating an intelligent positioning space when the first automatic assembly machine receives the product assembly instruction, and performing positioning analysis on the first product data set to be assembled based on the intelligent positioning space to obtain Q assembly positioning schemes, wherein Q is a positive integer greater than 2;
the assembly positioning module is used for carrying out optimizing analysis based on the Q assembly positioning schemes to obtain an assembly positioning decision scheme, and controlling the first automatic assembling machine to carry out assembly positioning of the first product group to be assembled based on the assembly positioning decision scheme.
3. An electronic device, the electronic device comprising:
a memory for storing executable instructions;
a processor for implementing an intelligent positioning method of an automatic assembling machine as claimed in claim 1 when executing executable instructions stored in said memory.
4. A computer readable storage medium having stored thereon a computer program, characterized in that the program, when executed by a processor, implements an intelligent positioning method of an automatic assembling machine according to claim 1.
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