CN116167505B - Method and system for cutting steel bar sleeve - Google Patents
Method and system for cutting steel bar sleeve Download PDFInfo
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- B—PERFORMING OPERATIONS; TRANSPORTING
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Abstract
The application provides a method and a system for cutting a reinforcing steel bar sleeve, and relates to the technical field of data processing. The method comprises the steps of generating trimming influence data according to historical trimming data of trimming equipment, inputting the reinforcing steel bar raw materials, target trimming information and trimming influence data into a planning model to obtain a pre-planning result for image acquisition of trimming to obtain a trimming result, generating optimized trimming data based on the image acquisition result to carry out planning model compensation, and outputting trimming planning according to the compensated model. The technical problem that in the prior art, the cutting control of the steel bar raw materials by cutting equipment depends on manual experience, and the production cost is increased due to the fact that the steel bar raw materials are wasted easily is solved. The intelligent degree of control of the cutting equipment is improved, the resource waste caused by the cutting equipment is reduced, and the technical effect of reducing the production cost waste caused by the raw materials of the reinforcing steel bars is achieved.
Description
Technical Field
The application relates to the technical field of data processing, in particular to a method and a system for cutting a steel bar sleeve.
Background
The reinforcing bar raw materials are usually straight or discoidal longer reinforcing bar, and the user often cuts the reinforcing bar raw materials according to individual needs, thereby cutting equipment is improving reinforcing bar raw materials and cuts efficiency and indirectly reducing user's work load, when improving user's production efficiency, has the risk phenomenon that the length of the reinforcing bar that cuts the gained does not satisfy user's demand.
At the root of the method, the cutting control of the cutting equipment for executing the raw materials of the steel bars at the present stage is semi-automatic, the user inputs the cutting target length or the cutting equipment executes the cutting task of the steel bars based on the cutting target length, and the cutting of the cutting equipment is not timely discovered when deviating from the user requirement, so that waste steel bars are generated by cutting, and the purchase cost of the raw materials of the steel bars of the user is increased.
In the prior art, the cutting control of the steel bar raw materials by cutting equipment depends on manual experience, and the technical problem that the production cost is increased due to the waste of the steel bar raw materials easily occurs.
Disclosure of Invention
The application provides a method and a system for cutting a reinforcing steel bar sleeve, which are used for solving the technical problems that in the prior art, the cutting control of reinforcing steel bar raw materials for cutting equipment depends on manual experience, and the waste of the reinforcing steel bar raw materials easily occurs, so that the production cost is increased.
In view of the above problems, the application provides a method and a system for cutting a reinforcing steel bar sleeve.
In a first aspect of the present application, there is provided a method for cutting a jacket material of a reinforcing bar, the method comprising: acquiring raw material information and target cutting information of the reinforcing steel bars; cutting equipment communication is carried out through the data communication equipment, and historical cutting data of the cutting equipment are read; carrying out data characteristic identification on the historical trimming data to generate screening characteristic data, and generating trimming influence data based on the screening characteristic data; inputting the raw material information of the reinforcing steel bars, the target cutting information and the cutting influence data into a cutting planning model, and outputting a pre-planning result; selecting a cut raw material sample, and cutting the cut raw material sample through the cutting equipment based on the pre-planning result to obtain a cutting result; the image acquisition device is used for carrying out image acquisition on the cutting result to generate an image acquisition result; and generating optimized cutting data based on the image acquisition result, performing model compensation on the cutting planning model through the optimized cutting data, and outputting a cutting planning result based on the compensated cutting planning model.
In a second aspect of the present application, there is provided a bar stock cutting system, the system comprising: the steel bar information acquisition module is used for acquiring steel bar raw material information and target cutting information; the historical data acquisition module is used for communicating the cutting equipment through the data communication equipment and reading historical cutting data of the cutting equipment; the data characteristic identification module is used for carrying out data characteristic identification on the historical trimming data, generating screening characteristic data and generating trimming influence data based on the screening characteristic data; the planning result output module is used for inputting the raw material information of the reinforcing steel bars, the target cutting information and the cutting influence data into a cutting planning model and outputting a pre-planning result; the cutting result generation module is used for selecting a cutting raw material sample, cutting the cutting raw material sample through the cutting equipment based on the pre-planning result, and obtaining a cutting result; the image acquisition execution module is used for carrying out image acquisition of the cutting result through the image acquisition equipment to generate an image acquisition result; and the model compensation execution module is used for generating optimized cutting data based on the image acquisition result, carrying out model compensation on the cutting planning model through the optimized cutting data, and outputting a cutting planning result based on the compensated cutting planning model.
One or more technical schemes provided by the application have at least the following technical effects or advantages:
the method provided by the embodiment of the application acquires the raw material information of the reinforcing steel bar and the target cutting information; the data communication equipment is used for communicating the cutting equipment, historical cutting data of the cutting equipment are read, the historical cutting data are optimized for cutting planning of the cutting equipment to be carried out later, and original data are provided for realizing the obtainment of a jacking cutting scheme for saving raw materials of the steel bars; carrying out data characteristic identification on the historical cutting data to generate screening characteristic data, and generating cutting influence data based on the screening characteristic data, wherein the cutting influence data provides numerical control optimization reference of cutting equipment for low-loss cutting of the steel bar raw materials to be carried out subsequently; inputting the raw material information of the reinforcing steel bars, the target cutting information and the cutting influence data into a cutting planning model, outputting a pre-planning result, replacing cutting planning of cutting equipment based on manual experience with the cutting planning model, and improving scientificity of a cutting planning scheme; selecting a cut raw material sample, and cutting the cut raw material sample through the cutting equipment based on the pre-planning result to obtain a cutting result; the image acquisition device is used for carrying out image acquisition of the cutting result to generate an image acquisition result, and the image acquisition result provides effective reference data for control optimization of subsequent cutting equipment; and generating optimized cutting data based on the image acquisition result, performing model compensation on the cutting planning model through the optimized cutting data, and outputting a cutting planning result based on the compensated cutting planning model. The intelligent degree of control of the cutting equipment is improved, the resource waste caused by the cutting equipment is reduced, and the technical effect of reducing the production cost waste caused by the raw materials of the reinforcing steel bars is achieved.
Drawings
Fig. 1 is a schematic flow chart of a method for cutting a reinforcing steel bar jacket according to an embodiment;
FIG. 2 is a flow diagram of generating optimized cutoff data in one embodiment;
FIG. 3 is a flowchart of obtaining a trimming and planning result in one embodiment;
fig. 4 is a block diagram of a system for cutting a bar stock according to an embodiment.
Reference numerals illustrate: the system comprises a base steel bar information acquisition module 1, a historical data acquisition module 2, a data characteristic identification module 3, a planning result output module 4, a cutting result generation module 5, an image acquisition execution module 6 and a model compensation execution module 7.
Detailed Description
The application provides a method and a system for cutting a reinforcing steel bar sleeve, which are used for solving the technical problems that in the prior art, the cutting control of reinforcing steel bar raw materials for cutting equipment depends on manual experience, and the waste of the reinforcing steel bar raw materials easily occurs, so that the production cost is increased. The intelligent degree of control of the cutting equipment is improved, the resource waste caused by the cutting equipment is reduced, and the technical effect of reducing the production cost waste caused by the raw materials of the reinforcing steel bars is achieved.
The technical scheme of the application obtains, stores, uses, processes and the like the data, which all meet the relevant regulations of national laws and regulations.
In the following, the technical solutions of the present application will be clearly and completely described with reference to the accompanying drawings, and it should be understood that the described embodiments are only some embodiments of the present application, but not all embodiments of the present application, and that the present application is not limited by the exemplary embodiments described herein. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application. It should be further noted that, for convenience of description, only some, but not all of the drawings related to the present application are shown.
Example 1
As shown in fig. 1, the application provides a method for cutting out a steel bar sleeve, which is applied to an intelligent cutting control system, wherein the intelligent cutting control system is in communication connection with image acquisition equipment and data communication equipment, and the method comprises the following steps:
s100, acquiring raw material information and target cutting information of the reinforcing steel bar;
specifically, in the present embodiment, the bar raw material is a straight or coiled steel material for use in the production process of a prefabricated section beam for construction in combination with a cement material, and the bar raw material information includes bar raw material single length information and bar raw material cross-sectional shape information, for example, bar raw material having a cross section of a smooth circular single length of 17 m.
The target cutting information is cutting length data for cutting the raw materials of the steel bars, which is obtained based on the calculation of the structural shape data of the steel bars required by the manufacturing of the prefabricated section beams, the raw materials of the steel bars can be processed into the required length of the prefabricated section beams by theoretically executing the target cutting information based on cutting equipment, and then the manufacturing of the prefabricated section beams is completed by executing the subsequent casting of the steel bar cement.
Therefore, the actual cutting length of the cutting equipment needs to be larger than the target cutting information so as to provide a reserved space for processing and removing the cutting defect, and the requirement that the length of the cutting result of the reinforcing steel bar is shorter than the target cutting information after the defect treatment of the cutting result of the reinforcing steel bar is avoided.
S200, communicating cutting equipment through the data communication equipment, and reading historical cutting data of the cutting equipment;
specifically, in this embodiment, the cutting device is a cutting function device capable of cutting a raw material of a reinforcing steel bar according to a user requirement, the data communication device is an information transmission device for performing data extraction and transmission, and the data communication device is in communication connection with the cutting device so as to implement extraction of historical cutting data of the cutting device, where the historical cutting data includes user historical target cutting information and historical actual cutting data, which is shorter than the user historical target cutting information, for example, the length of the reinforcing steel bar required by the user is 5m, for avoiding the length loss of the reinforcing steel bar caused by cutting defect removal, and the steel bar cutting data of the actual cutting device is 5.1m. The historical cutting data is obtained by optimizing cutting planning of subsequent cutting equipment, and provides original data for realizing a sleeve cutting scheme for saving raw materials of the steel bars.
S300, carrying out data characteristic identification on the historical cutting data, generating screening characteristic data, and generating cutting influence data based on the screening characteristic data;
specifically, it should be understood that the actual bar stock material cut amount of the cutting device during the cutting process is greater than the user's target cutting information, thereby providing a remedial space for defect removal caused by the bar cutting.
Thus, in this embodiment, the historical cut data includes the user historical target cut information and historical actual cut data that results in a shorter actual cut length than the user historical target cut information to avoid the length loss of the rebar caused by the elimination of the cut defect.
Defining the cutting length required by the user history target cutting information as a fixed value, defining the history actual cutting data as an expansion value, respectively generating data characteristic identifiers by the fixed value and the expansion value, wherein the data characteristic identifiers comprise fixed characteristic identifiers and expansion characteristic identifiers, classifying the history cutting data by taking the cutting length required by the user target cutting information as a judging classification standard of the history cutting data, classifying the two types of data based on the data characteristic identifiers to obtain screening characteristic data, wherein the screening characteristic data is two types of screening characteristic data which are classified and marked based on a numerical deviation relation between the history cutting data and the judging classification standard, and the screening characteristic data is used as cutting influence data, and the cutting influence data provides numerical control optimization reference of cutting equipment for the subsequent low-loss cutting of the steel bar raw materials.
S400, inputting the raw material information of the reinforcing steel bars, the target cutting information and the cutting influence data into a cutting planning model, and outputting a pre-planning result;
specifically, in this embodiment, the pre-planning result is a cutting plan scheme for the raw materials of the reinforcing steel bar, and a cutting equipment control parameter corresponding to the cutting plan scheme for the raw materials of the reinforcing steel bar, which still satisfies the target cutting information after the cutting and the defect elimination.
The pre-planning result is a model analysis output result of a cutting planning model, the cutting planning model is formed by constructing the cutting planning model based on the BP neural network, the cutting planning model replaces cutting planning of cutting equipment based on manual experience, and scientificity of a cutting planning scheme is improved.
The construction method of the cutting planning model is to acquire sample historical cutting data of a plurality of sample cutting devices of the same type, obtain sample cutting influence data based on the mode of steps S100-S300, and acquire sample steel bar raw material information, sample target cutting information and sample planning results of the plurality of sample cutting devices. Dividing the sample steel bar raw material information, the sample target cutting information, the sample planning result and the sample cutting influence data into a training set, a testing set and a verification set according to the 8:1:1 mark, performing supervised training test of the cutting planning model based on the training set and the testing set, and performing accuracy verification of the output result of the cutting planning model based on the verification set until the output accuracy of the cutting planning model approaches 99%, and stopping training of the cutting planning model. And inputting the raw material information of the reinforcing steel bars, the target cutting information and the cutting influence data into the trained cutting planning model, and outputting the pre-planning result.
S500, selecting a cut raw material sample, and cutting the cut raw material sample through the cutting equipment based on the pre-planning result to obtain a cutting result;
s600, carrying out image acquisition of the cutting result through the image acquisition equipment to generate an image acquisition result;
specifically, in this embodiment, the cut raw material sample is a test sample steel bar that is consistent with the steel bar raw material specification, and the shortest required length of the cut raw material sample is greater than the expansion value.
And adjusting and controlling the cutting equipment to cut the cut raw material sample based on the pre-planning result to obtain the cutting result, wherein the cutting result is the steel bar obtained by cutting the actual cut raw material sample by the cutting equipment, and the cutting result has various defects such as a melting cooling sphere of the section of the steel bar and cutting accidental injury of the surface of the steel bar.
The image acquisition equipment is in the prior art, and the image acquisition result is obtained based on the image acquisition of the cutting result by the image acquisition equipment. Based on the image acquisition result, the condition of the section and the surface defect of the steel bar of the cutting result can be known, and the image acquisition result provides effective reference data for the control optimization of the subsequent cutting equipment.
And S700, generating optimized cutting data based on the image acquisition result, carrying out model compensation on the cutting planning model through the optimized cutting data, and outputting a cutting planning result based on the compensated cutting planning model.
In one embodiment, as shown in fig. 2, the method steps provided by the present application further include:
s710, constructing a defect feature set through big data, wherein the defect feature set comprises an associated processing size;
s720, carrying out feature matching on the image acquisition result based on the defect feature set to obtain a feature matching result;
s730, obtaining a matching association processing size according to the feature matching result;
and S740, obtaining a characteristic position based on the image acquisition result, and generating the optimized cutting data according to the characteristic position and the associated processing size.
Specifically, in this embodiment, defect characteristics of the surface and the cross section of the cut steel bar and the associated processing dimensions of the dimension of the defect characteristic parallel to the length direction of the steel bar are obtained based on big data, for example, the defect characteristics are that the cross section of the steel bar has a melted cooling hemispherical shape, and the associated processing dimensions are 14mm.
And acquiring defect characteristics generated by cutting processing of multi-type sample steel bars based on big data acquisition to form a defect characteristic set, traversing and comparing the image acquisition result based on the defect characteristic set, and obtaining specific positions of defect characteristics existing on the surface of the steel bars of the cutting result, namely the characteristic positions and the associated processing sizes, wherein the defect characteristics are apparent and the sizes of the defect characteristics are consistent.
And obtaining a characteristic position based on the image acquisition result, generating optimized cutting data according to the characteristic position and the associated processing size, wherein the optimized cutting data is data for compensating the output result of the cutting planning model, and the pre-planning result output by the cutting planning model after optimization based on the optimized cutting data is control optimized data and actual target cutting length data of the cutting equipment, wherein the length of the cutting result still meets the target cutting information length even if the cutting defect characteristic is removed after the cutting equipment is adopted to cut the raw steel bar material.
The adaptation degree of the cutting length of the raw materials of the steel bars and the demand of the prefabricated section beam is improved, the defect that the raw materials of the steel bars are cut and generated is avoided, after correction, the length of the steel bars does not meet the processing demand of the prefabricated section beam, the problems of resource waste and the rising of the production cost of the prefabricated section beam are caused, and the technical effects of guaranteeing the stability of the production cost of the prefabricated section beam and reducing the waste of the raw materials of the steel bars are achieved.
In one embodiment, the method steps provided by the application further comprise:
s741, obtaining a sample processing interval based on the characteristic position and the associated processing size;
s742, carrying out sample monitoring on all samples, and carrying out statistics to construct a sample processing interval set, wherein the sample processing interval set is provided with a time mark;
s743, judging whether the sample processing interval sets all meet the cutting influence data;
s744, when the sample processing interval sets all meet the cutting influence data, obtaining fuzzy aggregation modes of the sample processing interval sets;
s745, expanding the interval range through the fuzzy aggregation mode matching, and obtaining the optimized cutting data based on the expanded interval range.
In one embodiment, the method steps provided by the application further comprise:
s744-1, when the sample processing interval set has data which does not meet the cutting influence data, obtaining an abnormal value of abnormal data;
s744-2, generating equipment regulation information of the cutting equipment through the abnormal value;
s744-3, resampling to generate the optimized cutting data after the cutting equipment is regulated based on the equipment regulation information.
Specifically, in this embodiment, a sample processing section is obtained based on the feature position and the associated processing size, where the sample processing section is a secondary trimming (or polishing) processing size range of the reinforcing steel bar that is required to be subjected to secondary trimming (or polishing) to remove the defect feature currently existing for the trimming result. Illustratively, the position of the defect feature of the cutting result is 2mm away from the tail end of the reinforcing steel bar corresponding to the cutting result, the associated processing size of the defect feature is 3mm, the sample processing interval is 5mm, and the secondary cutting or polishing processing of the cutting result is performed based on the sample processing interval, so that the part with the defect feature is removed.
In order to improve the credibility of the sample processing interval, the secondary application is performed on the sample history cutting data of the plurality of sample cutting devices of the same type acquired during the cutting planning model training in the step S400. Specifically, data extraction is performed on sample history cutting data, eye history cutting data record sample monitoring corresponding to the same type of feature defects of the same associated size data and feature positions is obtained, sample processing interval statistics with one-to-one correspondence with the sample history cutting data is obtained, and a sample processing interval set is constructed, wherein the sample processing interval set is provided with a time mark.
The cutting influence data is a numerical range set by taking target cutting information as a reference, and the minimum value is a fixed value and the maximum value is an expansion value.
Judging whether the sample processing interval sets all meet the cutting influence data; when the sample processing interval sets all meet the cutting influence data, the performance of the cutting equipment is stable, the defect characteristics generated by cutting of the cutting equipment are stable at the fixed positions of the cutting results of the reinforcing steel bars, and therefore the cutting equipment can be used for carrying out the cutting processing of the reinforcing steel bar raw materials.
In order to further reduce the reinforcement loss of cutting and defect feature removal polishing processing behaviors, the embodiment obtains a fuzzy aggregation mode of the sample processing section set, wherein the method for obtaining the fuzzy aggregation mode is to set a plurality of continuous data sections to be subjected to traversal comparison with each sample processing section in the sample processing section set, consider data with smaller data deviation of the sample processing section to be the same data in a fuzzy manner, and take the data section with the largest sample processing section distribution quantity as the fuzzy aggregation mode.
And carrying out numerical expansion on the fuzzy aggregation mode by combining the fuzzy aggregation mode with the maximum value of the data interval before the data interval where the fuzzy aggregation mode is located, comparing the fuzzy aggregation mode after numerical expansion with the cutting influence data, so as to replace the expansion value of the cutting influence data with the fuzzy aggregation mode, thereby narrowing the interval range of the cutting influence data, obtaining the optimized cutting data based on the narrowed interval range, optimizing a cutting planning model, reducing the cutting length of a pre-planning result, reducing the waste of reinforcing steel bar raw materials, for example, the length of a reinforcing steel bar required by a user is 5m, the cutting data of the reinforcing steel bar of the actual cutting equipment is 5.1m for avoiding the reinforcing steel bar length loss caused by cutting defect removal, and the cutting data of the reinforcing steel bar of the actual cutting equipment after optimization is 5.05m.
When the sample processing interval set has data which do not meet the cutting influence data, the defect characteristic generated by cutting of the current cutting equipment is not stabilized at the fixed position of the cutting result of the reinforcing steel bar, and the abnormal value which is used for reference to the stability coefficient adjustment of the cutting equipment is compared with the abnormal value which is used for obtaining part of the sample processing interval which does not meet the cutting influence data and is used as the abnormal data.
Generating equipment regulation information of the cutting equipment through the abnormal value; and (4) resampling after the cutting equipment is regulated based on the equipment regulation information, and executing steps S741-S744 to generate the optimized cutting data.
According to the embodiment, the operation stability analysis and adjustment of the cutting equipment are realized, and the sample processing interval data analysis is performed based on the cutting equipment in a stable state, so that the value range of cutting influence data is reduced, the cutting length of the reinforcing steel bar raw material of a pre-planning result is reduced, and the technical effect of waste of the reinforcing steel bar raw material is reduced.
In one embodiment, as shown in fig. 3, the method steps provided by the present application further include:
s410, constructing a margin constraint hidden layer, and coupling the margin constraint hidden layer to the cutting planning model;
s420, judging whether the target cutting information comprises a plurality of size cutting specifications or not;
s430, when the target cutting information comprises a plurality of cutting specifications, calling the allowance constraint hidden layer to carry out cutting planning;
s440, outputting and obtaining the cutting planning result.
Specifically, in this embodiment, step S400 builds the training completion cutting plan model, and has a target cutting information judgment layer between the data input layer and the pre-planning result analysis generation layer, and a margin constraint hidden layer in the cutting plan model, except for the data input layer, the pre-planning result analysis generation layer, and the data output layer, where the margin constraint hidden layer is a selection calling layer and is not in an application state in real time.
And the allowance constraint hidden layer is used for planning whether the residual amount of the raw materials of the reinforcing steel bar is enough or not when the target cutting information contains the multi-type cutting length information.
And after the target cutting information is input into the cutting planning model, carrying out data identification processing based on a target cutting information judging layer, and judging whether the target cutting information comprises a plurality of size cutting specifications or not, for example, cutting 23m steel bar segments and 5 4m steel bar segments.
When the target cutting information comprises a plurality of cutting specifications, the allowance constraint hidden layer is called to carry out cutting planning according to the existing length of the raw materials of the reinforcing steel bars, the cutting planning result is output to obtain, and the current allowance of the raw materials of the reinforcing steel bars is 7m and 23m, and the allowance constraint hidden layer can allocate cutting sources of reinforcing steel bars with different cutting specifications based on the allowance of the raw materials of the reinforcing steel bars so as to reduce the cutting allowance waste amount of the reinforcing steel bars, thereby realizing the technical effects of reducing the waste reinforcing steel bar amount generated by cutting the raw materials of the reinforcing steel bars in the cutting process of the reinforcing steel bars with multiple specifications, and reducing the using amount of the raw materials of the reinforcing steel bars and the production cost of the prefabricated section beam.
In one embodiment, the method steps provided by the application further comprise:
s810, performing cutting execution based on the cutting planning result;
s820, data acquisition is carried out on the cut product subjected to cutting, and a finished product data acquisition result is generated;
s830, generating cutting early warning information based on the finished product data acquisition result;
and S840, performing cutting control feedback according to the cutting early warning information.
Specifically, in this embodiment, the cutting device performs the cutting process of the raw material of the reinforcing steel bar based on the cutting planning result, obtains a plurality of finished products of cutting the reinforcing steel bar, performs data collection based on the cut products, and each cut product data includes the length data of the reinforcing steel bar, the characteristic position of the defect characteristic on the surface of the cut product, and the associated size data, and combines as the result of the data collection of the finished product.
And obtaining a sample processing interval set based on the finished product data acquisition result, calculating a fluctuation value of a sample processing interval value along with time, judging whether the fluctuation value meets a preset early warning fluctuation value, generating cutting early warning information based on the finished product data acquisition result when the fluctuation value meets the preset early warning fluctuation value, and performing cutting control feedback through the cutting early warning information, so that the stability early warning of the cutting equipment is performed in real time in the using process of the cutting equipment, and the technical effect of reducing the probability of wasting the raw materials of the reinforcing steel bars caused by unstable cutting of the cutting equipment is indirectly realized.
In one embodiment, the method steps provided by the application further comprise:
s910, obtaining cutting allowance information according to the cutting planning result;
s920, generating equipment stability data of the cutting equipment according to the historical cutting data;
s930, judging whether the stability data meets a preset stability threshold value;
and S940, when the stability data cannot meet the preset stability threshold, carrying out allowance distribution on the cutting allowance, and adjusting the cutting planning result according to the allowance distribution result.
Specifically, it should be understood that the length of the reinforcing steel bar corresponding to the cutting allowance information is the reinforcing steel bar which is not used in theory, and the cutting allowance information is obtained through calculation of the cutting planning result and the length data of the actual cutting reinforcing steel bar raw material.
The historical cutting data comprises user historical target cutting information and historical actual cutting data, wherein the actual cutting length is shorter than the user historical target cutting information due to the fact that the length loss of the steel bars caused by cutting defect elimination is avoided. And calculating a difference value between the historical actual trimming data and the target trimming data with corresponding relations through the historical trimming data, generating a discrete image according to the trimming time, and obtaining the equipment stability data of the trimming equipment based on the discrete image.
Based on the preset stability threshold given by an automation field expert, judging whether the stability data meets the preset stability threshold; when the stability data cannot meet the preset stability threshold, the fact that the cutting treatment of the raw materials of the reinforcing steel bars based on the current cutting planning result has defects that the length of the reinforcing steel bars does not meet the requirements of target cutting information after the secondary cutting of defect characteristics is carried out on the basis of the cutting result, the cutting allowance is distributed, for example, the cutting allowance is uniformly added into two actual cutting lengths, the cutting planning result is adjusted according to the allowance distribution result, for example, the cutting of the raw materials of the reinforcing steel bars with the length of 10m is adopted to obtain two reinforcing steel bars with the length of 4m, the actual cutting length is 4.2m, the cutting allowance information is 10 < -4.2 > -4.2=0.8m, and the cutting allowance is 0.8 and is added into the actual cutting length, so that the actual cutting length is 4.6m. The embodiment realizes that the raw materials of the reinforcing steel bar caused by the unstable operation of the slow blanking cutting equipment are cut too short, and achieves the technical effect of avoiding the waste of the raw materials of the reinforcing steel bar.
In one embodiment, as shown in fig. 4, there is provided a bar stock cutting system comprising: the system comprises a base steel bar information acquisition module 1, a historical data acquisition module 2, a data characteristic identification module 3, a planning result output module 4, a cutting result generation module 5, an image acquisition execution module 6 and a model compensation execution module 7, wherein:
the steel bar information acquisition module 1 is used for acquiring steel bar raw material information and target cutting information;
the historical data acquisition module 2 is used for communicating cutting equipment through data communication equipment and reading historical cutting data of the cutting equipment;
the data characteristic identification module 3 is used for carrying out data characteristic identification on the historical trimming data, generating screening characteristic data and generating trimming influence data based on the screening characteristic data;
the planning result output module 4 is used for inputting the raw material information of the reinforcing steel bars, the target cutting information and the cutting influence data into a cutting planning model and outputting a pre-planning result;
the cutting result generation module 5 is used for selecting a cutting raw material sample, cutting the cutting raw material sample through the cutting equipment based on the pre-planning result, and obtaining a cutting result;
the image acquisition execution module 6 is used for carrying out image acquisition of the cutting result through image acquisition equipment to generate an image acquisition result;
and the model compensation execution module 7 is used for generating optimized cutting data based on the image acquisition result, carrying out model compensation on the cutting planning model through the optimized cutting data, and outputting a cutting planning result based on the compensated cutting planning model.
In one embodiment, the model compensation execution module 7 further includes:
a defect feature construction unit for constructing a defect feature set by big data, wherein the defect feature set comprises an associated processing size;
the feature matching execution unit is used for carrying out feature matching on the image acquisition result based on the defect feature set to obtain a feature matching result;
the processing size matching unit is used for obtaining matching association processing sizes according to the characteristic matching result;
and the cutting data optimizing unit is used for obtaining a characteristic position based on the image acquisition result and generating the optimized cutting data according to the characteristic position and the associated processing size.
In one embodiment, the trimming data optimizing unit further includes:
a processing section obtaining unit configured to obtain a sample processing section based on the feature position and the associated processing size;
the sample monitoring execution unit is used for carrying out sample monitoring on all samples and carrying out statistics to construct a sample processing interval set, wherein the sample processing interval set is provided with a time mark;
a processing section judging unit for judging whether the sample processing section sets all satisfy the cutting influence data;
the fuzzy aggregation execution unit is used for obtaining the fuzzy aggregation mode of the sample processing interval set when the sample processing interval set meets the cutting influence data;
and the optimized data obtaining unit is used for obtaining the optimized cutting data based on the expanded interval range through the fuzzy aggregation mode matching expanded interval range.
In one embodiment, the fuzzy aggregation execution unit further includes:
an abnormal data obtaining unit configured to obtain an abnormal value of abnormal data when the sample processing section set has data that does not satisfy the trimming influence data;
a regulation information obtaining unit for generating device regulation information of the cutting device through the abnormal value;
and the optimized data generation unit is used for resampling to generate the optimized cutting data after the cutting equipment is regulated based on the equipment regulation information.
In one embodiment, the planning result output module 4 further includes:
a constraint layer construction unit, configured to construct a margin constraint hidden layer, and couple the margin constraint hidden layer to the cropping planning model;
a cutting information judging unit for judging whether the target cutting information includes a plurality of size cutting specifications;
the cutting planning execution unit is used for calling the allowance constraint hidden layer to carry out cutting planning when the target cutting information comprises a plurality of cutting specifications;
and the podophyllum result generation unit is used for outputting and obtaining the cutting planning result.
In one embodiment, the system provided by the application further comprises:
the cutting planning execution unit is used for performing cutting execution based on the cutting planning result;
the data acquisition execution unit is used for carrying out data acquisition on the cut product to be cut to generate a finished product data acquisition result;
the cutting early warning generation unit is used for generating cutting early warning information based on the finished product data acquisition result;
and the control feedback execution unit is used for carrying out cutting control feedback through the cutting early warning information.
In one embodiment, the system provided by the application further comprises:
the cutting allowance obtaining unit is used for obtaining cutting allowance information according to the cutting planning result;
a stability factor generating unit configured to generate device stability data of the trimming device from the history trimming data;
the stability coefficient judging unit is used for judging whether the stability data meets a preset stability threshold value or not;
and the margin distribution reference unit is used for carrying out margin distribution on the cutting margin when the stability data cannot meet the preset stability threshold value, and adjusting the cutting planning result according to the margin distribution result.
Any of the methods or steps described above may be stored as computer instructions or programs in various non-limiting types of computer memories, and identified by various non-limiting types of computer processors, thereby implementing any of the methods or steps described above.
Based on the above-mentioned embodiments of the present application, any improvements and modifications to the present application without departing from the principles of the present application should fall within the scope of the present application.
Claims (8)
1. The method is characterized by being applied to an intelligent cutting control system, wherein the intelligent cutting control system is in communication connection with image acquisition equipment and data communication equipment, and the method comprises the following steps:
acquiring raw material information and target cutting information of the reinforcing steel bars;
cutting equipment communication is carried out through the data communication equipment, and historical cutting data of the cutting equipment are read;
performing data characteristic identification on the historical cutting data to generate screening characteristic data, generating cutting influence data based on the screening characteristic data, constructing the cutting influence data through the screening characteristic data, classifying the historical cutting data according to the judging classification standard of the historical cutting data of which the cutting length is required by the cutting information of a user target, and performing classification identification generation of two types of data based on the data characteristic identification;
inputting the raw material information of the reinforcing steel bars, the target cutting information and the cutting influence data into a cutting planning model, and outputting a pre-planning result;
selecting a cut raw material sample, and cutting the cut raw material sample through the cutting equipment based on the pre-planning result to obtain a cutting result;
the image acquisition device acquires the image of the cutting result to generate an image acquisition result;
and generating optimized cutting data based on the image acquisition result, performing model compensation on the cutting planning model through the optimized cutting data, and outputting a cutting planning result based on the compensated cutting planning model.
2. The method of claim 1, wherein the method comprises:
constructing a defect feature set through big data, wherein the defect feature set comprises an associated processing size;
performing feature matching on the image acquisition result based on the defect feature set to obtain a feature matching result;
obtaining a matching association processing size according to the feature matching result;
and obtaining a characteristic position based on the image acquisition result, and generating the optimized cutting data according to the characteristic position and the associated processing size.
3. The method according to claim 2, wherein the method comprises:
obtaining a sample processing interval based on the feature location and the associated processing size;
carrying out sample monitoring on all samples, and carrying out statistics to construct a sample processing interval set, wherein the sample processing interval set is provided with a time mark;
judging whether the sample processing interval sets all meet the cutting influence data;
when the sample processing interval sets all meet the cutting influence data, obtaining fuzzy aggregation modes of the sample processing interval sets;
and expanding the interval range through the fuzzy aggregation mode matching, and obtaining the optimized cutting data based on the expanded interval range.
4. A method according to claim 3, wherein the method comprises:
when the sample processing interval set has data which does not meet the cutting influence data, an abnormal value of abnormal data is obtained;
generating equipment regulation information of the cutting equipment through the abnormal value;
and resampling to generate the optimized cutting data after the cutting equipment is regulated based on the equipment regulation information.
5. The method of claim 1, wherein the method comprises:
constructing a margin constraint hidden layer, and coupling the margin constraint hidden layer to the cutting planning model;
judging whether the target cutting information comprises a plurality of size cutting specifications or not;
when the target cutting information comprises a plurality of cutting specifications, calling the allowance constraint hidden layer to carry out cutting planning;
and outputting and obtaining the cutting planning result.
6. The method of claim 1, wherein the method comprises:
performing cutting execution based on the cutting planning result;
performing data acquisition on the cut product to be cut to generate a finished product data acquisition result;
generating cutting early warning information based on the finished product data acquisition result;
and performing cutting control feedback according to the cutting early warning information.
7. The method of claim 1, wherein the method comprises:
obtaining cutting allowance information through the cutting planning result;
generating device stability data of the cutting device through the historical cutting data;
judging whether the stability data meets a preset stability threshold value or not;
and when the stability data cannot meet the preset stability threshold, carrying out allowance distribution on the cutting allowance, and adjusting the cutting planning result according to the allowance distribution result.
8. A system for cutting out a rebar jacket, the system comprising:
the steel bar information acquisition module is used for acquiring steel bar raw material information and target cutting information;
the historical data acquisition module is used for communicating the cutting equipment through the data communication equipment and reading historical cutting data of the cutting equipment;
the data characteristic identification module is used for carrying out data characteristic identification on the historical cutting data, generating screening characteristic data, generating cutting influence data based on the screening characteristic data, constructing the cutting influence data through the screening characteristic data, carrying out classification on the historical cutting data according to the requirement of cutting length of user target cutting information as a judging classification standard of the historical cutting data, and carrying out classification identification generation of two types of data based on the data characteristic identification;
the planning result output module is used for inputting the raw material information of the reinforcing steel bars, the target cutting information and the cutting influence data into a cutting planning model and outputting a pre-planning result;
the cutting result generation module is used for selecting a cutting raw material sample, cutting the cutting raw material sample through the cutting equipment based on the pre-planning result, and obtaining a cutting result;
the image acquisition execution module is used for carrying out image acquisition of the cutting result through the image acquisition equipment to generate an image acquisition result;
and the model compensation execution module is used for generating optimized cutting data based on the image acquisition result, carrying out model compensation on the cutting planning model through the optimized cutting data, and outputting a cutting planning result based on the compensated cutting planning model.
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Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107608393A (en) * | 2017-08-31 | 2018-01-19 | 保定钞票纸业有限公司 | A kind of positioning cutting system and method based on machine vision technique |
| CN110728059A (en) * | 2019-10-16 | 2020-01-24 | 北京首钢股份有限公司 | Intelligent plate roll head and tail slitting method based on industrial big data platform and industrial big data platform |
| CN111709573A (en) * | 2020-06-15 | 2020-09-25 | 重庆钢铁股份有限公司 | Optimal daughter board planning method and system for hot-cutting and shearing steel plate to be sheared |
| CN113778110A (en) * | 2021-11-11 | 2021-12-10 | 山东中天宇信信息技术有限公司 | Intelligent agricultural machine control method and system based on machine learning |
| CN114563992A (en) * | 2022-03-01 | 2022-05-31 | 昆山缔微致精密电子有限公司 | Method and system for improving blanking precision of injection mold |
| CN115047162A (en) * | 2022-06-24 | 2022-09-13 | 张家港沙龙精密管业有限公司 | Defect detection method and system for steel pipe heat treatment |
| CN115060214A (en) * | 2022-05-18 | 2022-09-16 | 广西广盛新材料科技有限公司 | Steel shearing method, device, server and storage medium |
-
2023
- 2023-01-09 CN CN202310024471.5A patent/CN116167505B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107608393A (en) * | 2017-08-31 | 2018-01-19 | 保定钞票纸业有限公司 | A kind of positioning cutting system and method based on machine vision technique |
| CN110728059A (en) * | 2019-10-16 | 2020-01-24 | 北京首钢股份有限公司 | Intelligent plate roll head and tail slitting method based on industrial big data platform and industrial big data platform |
| CN111709573A (en) * | 2020-06-15 | 2020-09-25 | 重庆钢铁股份有限公司 | Optimal daughter board planning method and system for hot-cutting and shearing steel plate to be sheared |
| CN113778110A (en) * | 2021-11-11 | 2021-12-10 | 山东中天宇信信息技术有限公司 | Intelligent agricultural machine control method and system based on machine learning |
| CN114563992A (en) * | 2022-03-01 | 2022-05-31 | 昆山缔微致精密电子有限公司 | Method and system for improving blanking precision of injection mold |
| CN115060214A (en) * | 2022-05-18 | 2022-09-16 | 广西广盛新材料科技有限公司 | Steel shearing method, device, server and storage medium |
| CN115047162A (en) * | 2022-06-24 | 2022-09-13 | 张家港沙龙精密管业有限公司 | Defect detection method and system for steel pipe heat treatment |
Non-Patent Citations (4)
| Title |
|---|
| 冷轧切边剪薄带钢起筋控制策略研究;李志超;;金属世界(03);全文 * |
| 基于图像处理的钢坯定尺定重智能切割***;安辉耀, 沈德耀;中南工业大学学报(自然科学版)(06);全文 * |
| 基于神经网络的连铸板坯质量在线诊断***;郭贤利;彭世恒;仇圣桃;;冶金自动化(03);全文 * |
| 影响控轧控冷钢筋生产不畅的因素及对策;丁礼权;丁文胜;董水要;;武钢技术(03);全文 * |
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