CN118083273A - Automatic packaging system and method for baking sand - Google Patents
Automatic packaging system and method for baking sand Download PDFInfo
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Abstract
The invention relates to the field of electric digital data processing, in particular to an automatic packaging system and method for baking sand. Firstly, acquiring a sand image, converting the sand image into space-time characteristic representation to obtain space-time characteristics, and carrying out characteristic fusion to obtain comprehensive characteristics so as to classify sand and obtain a sand classification result; then analyzing the sand classification result, and carrying out weighting treatment to obtain a weighted feature set; and introducing a decision model, obtaining and analyzing the output of the decision model, converting the output of the decision model into a mechanical operation instruction, and adjusting the operation parameters of the packaging machine according to the mechanical operation instruction. The invention solves the problem that the sand cannot be effectively and automatically adjusted when different types of sand are processed; the small change of the sand grain characteristics cannot be captured, so that the precision is insufficient in the packing process; the dependence on manual intervention is high, and the labor intensity and the possibility of operation errors are increased; the technical problem that the collected data cannot be fully utilized to optimize the packaging process.
Description
Technical Field
The invention relates to the field of electric digital data processing, in particular to an automatic packaging system and method for baking sand.
Background
The efficiency and accuracy of the packaging process of baked sand, an important material for widespread use in construction, industry and other fields, has an important impact on the overall supply chain. Traditional baking sand packing methods mainly rely on semi-automatic or manual operation, which is not only inefficient, but also easily causes different packing quality due to human factors.
With the development of industrial automation technology, automatic bagging machinery is introduced to improve production efficiency and reduce labor costs. However, existing automated bagging systems often lack sufficient flexibility and adaptability, especially when handling sand particles of different characteristics (e.g., size, shape, and texture). In addition, these systems have shortcomings in accuracy control, real-time monitoring, and data-driven decision support.
Chinese patent application number: CN202310318002.4, publication date: 2023.06.09 discloses an automatic loading device for baked sand, which comprises a feeding mechanism, a sand filling mechanism and a sand pumping mechanism; the feeding mechanism is fixedly arranged on a table top with the protruding ground, the lower end of the feeding mechanism is in butt joint with an outlet of the drying sand drying device, the other end of the feeding mechanism is in butt joint with the sand collecting groove, the effect is that the drying sand is conveyed into the sand filling mechanism, the sand filling mechanism is fixedly arranged on one side of the feeding mechanism, a tank car space is reserved at the lower end of the feeding mechanism and used for filling the drying sand into a tank car, the sand pumping mechanism is fixedly arranged beside the sand filling mechanism, a tank car space is reserved at the lower side of the sand pumping mechanism, and the effect of the sand pumping mechanism is that the drying sand in one tank car is pumped into another tank car, so that the transportation is convenient; when the invention is used, the dried dry sand is directly filled into the tank truck through the feeding mechanism and the sand filling mechanism without bagging, bulk packing and ton packing, thereby greatly saving manpower and material resources, improving the efficiency and reducing the cost.
However, the above technology has at least the following technical problems: in the prior art, when sand grains with different types or characteristics are processed, effective automatic adjustment cannot be performed, so that the packing quality and efficiency are affected; the small change of the sand grain characteristics cannot be accurately captured, so that the precision in the packing process is insufficient; the dependence on manual intervention is high, so that the labor intensity is increased, and the risk of operation errors is also increased; the inability to fully utilize the collected data to optimize the packaging process limits the improvement and optimization potential of the production process, and makes it difficult to adapt to rapidly changing production requirements and market conditions.
Disclosure of Invention
The invention solves the problems that the prior art can not effectively and automatically adjust when processing sand grains with different types or characteristics, and the packing quality and efficiency are affected by providing the automatic packing system and method for the baked sand; the small change of the sand grain characteristics cannot be accurately captured, so that the precision in the packing process is insufficient; the dependence on manual intervention is high, so that the labor intensity is increased, and the risk of operation errors is also increased; the collected data cannot be fully utilized to optimize the packing process, so that the improvement and optimization potential of the production flow are limited, and the technical problems of rapidly changing production requirements and market conditions are difficult to adapt.
The invention provides an automatic packaging system and method for baking sand, which specifically comprise the following technical scheme:
The automatic packing method of the baked sand comprises the following steps:
s1, acquiring a sand image, converting the sand image into space-time characteristics by using a space-time transformation algorithm, and fusing the characteristics to obtain comprehensive characteristics, so as to classify the sand and obtain a sand classification result;
S2, analyzing the sand classification result, and carrying out weighting treatment to obtain a weighted feature set; and introducing a decision model to obtain the output of the decision model, analyzing the output of the decision model, converting the output of the decision model into a mechanical operation instruction, and adjusting the operation parameters of the packaging machine according to the mechanical operation instruction.
Preferably, the S1 further specifically includes:
in the process of feature fusion, a fusion formula is introduced to fuse the space-time features into comprehensive features.
Preferably, the S1 further specifically includes:
In the process of classifying the sand grains, a classification formula is introduced, and the classification of the sand grains is output based on the comprehensive characteristics.
Preferably, the S2 further specifically includes:
in the analysis process of the sand classification result, a decision tree model is introduced, the feature importance of the sand classification result is estimated, and weighting treatment is carried out according to the estimation result of the feature importance of the sand classification result, so that weighted features are obtained, and a weighted feature set is formed.
Preferably, the S2 further specifically includes:
In the implementation process of the decision model, the data in the weighted feature set is converted into a format suitable for the decision model by utilizing a feature conversion algorithm, so that the converted feature set is obtained.
Preferably, the S2 further specifically includes:
In the implementation process of the decision model, based on the converted feature set, the decision logic is extracted, and then the optimal packaging machinery operation parameter adjustment scheme is output.
Preferably, the S2 further specifically includes:
in the process of converting the optimal packaging machinery operation parameter adjustment scheme into a machinery operation instruction, designing a self-adaptive algorithm, analyzing the optimal packaging machinery operation parameter adjustment scheme, and adjusting the operation parameters of the packaging machinery.
An automatic packaging system for baking sand, which comprises the following parts:
the system comprises an image acquisition module, a space-time conversion module, a fusion module, a classification module, an analysis module, a decision module, a parameter adjustment module and an instruction generation module;
The image acquisition module is used for capturing information of sand grains by using the camera to obtain a sand grain image; the image acquisition module is connected with the space-time conversion module in a data transmission mode;
The space-time transformation module is used for converting the sand image into space-time characteristics by applying a space-time transformation algorithm; the space-time conversion module is connected with the fusion module in a data transmission mode;
The fusion module is used for fusing the time-space characteristics to obtain comprehensive characteristics, and the hyperbolic sine function and the nonlinear operation enhancement characteristics are used for fusion; the fusion module is connected with the classification module in a data transmission mode;
The classifying module is used for classifying based on comprehensive characteristics by utilizing a dynamic network, outputting the types of sand grains and obtaining a sand grain classifying result; the classification module is connected with the analysis module in a data transmission mode;
The analysis module is used for analyzing sand classification results, determining characteristics influencing the packing process, weighting the characteristics influencing the packing process to obtain weighted characteristics, and further forming a weighted characteristic set; the analysis module is connected with the decision module in a data transmission mode;
the decision module is used for constructing a decision model, converting the data in the weighted feature set to obtain a converted feature set, and outputting an optimal packaging machinery operation parameter adjustment scheme based on the converted feature set; the decision module is connected with the parameter adjustment module in a data transmission mode;
the parameter adjustment module is used for designing a self-adaptive algorithm and adjusting the operation parameters of the packaging machine according to an optimal packaging machine operation parameter adjustment scheme; the parameter adjustment module is connected with the instruction generation module in a data transmission mode;
the instruction generation module is used for converting the operation parameters of the packaging machine output by the parameter adjustment module into machine operation instructions.
The automatic packing method is applied to the automatic packing method of the dried sand.
The technical scheme of the invention has the beneficial effects that:
1. By using a data processing technology and a space-time transformation technology, fine characteristics of sand grains such as size, shape and texture are accurately identified, so that the precision and efficiency of an automatic packing process are improved, and the consistency and quality of each package are ensured; the operation parameters of the packing machine are dynamically adjusted according to different characteristics of sand grains by utilizing a complex mathematical model and a self-adaptive algorithm, so that the packing machine is flexibly adapted to different types of sand grains, and a wider application range is realized;
2. by continuously monitoring the packing process and feeding back in real time, the operation flow is optimized by self-adjustment, the packing efficiency is improved, and the resource waste and the risk of operation errors are reduced; the automatic feature recognition and parameter adjustment reduce the manual intervention requirement, reduce the labor intensity and improve the production safety;
3. by collecting and analyzing a large amount of data, data-driven decision support can be provided, and optimization suggestions can be provided for future operations; the high automation and the intellectualization of the whole automatic packaging system can obviously improve the automation degree of the production line, reduce the labor cost and improve the yield and the product consistency.
Drawings
FIG. 1 is a block diagram of an automatic packing system for dry sand according to one embodiment of the present invention;
fig. 2 is a flowchart of a method for automatically packing the dried sand according to an embodiment of the present invention.
Detailed Description
In order to further illustrate the technical means and effects adopted by the present invention to achieve the preset purpose, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
In order to better understand the above technical solutions, the following detailed description will refer to the accompanying drawings and specific embodiments.
Referring to fig. 1, a structure diagram of an automatic packing system for dry sand according to an embodiment of the present invention includes:
the system comprises an image acquisition module, a space-time conversion module, a fusion module, a classification module, an analysis module, a decision module, a parameter adjustment module and an instruction generation module;
the image acquisition module is used for capturing visual information of sand grains by using a high-resolution camera, including size, shape and texture; the image acquisition module is connected with the space-time conversion module in a data transmission mode;
The space-time transformation module is used for converting the traditional static image into multidimensional space-time characteristics showing the change of sand grains along with time and space by applying a space-time transformation algorithm; the space-time conversion module is connected with the fusion module in a data transmission mode;
The fusion module is used for fusing the multidimensional space-time characteristics converted by the space-time conversion algorithm, synthesizing the multidimensional space-time characteristics captured at different time points into a comprehensive characteristic, and using hyperbolic sine functions and nonlinear operation enhancement characteristics for fusion; the fusion module is connected with the classification module in a data transmission mode;
the classifying module is used for classifying the comprehensive characteristics by utilizing a dynamic network, outputting the types of sand grains and obtaining a sand grain classifying result; the classification module is connected with the analysis module in a data transmission mode;
The analysis module is used for analyzing the sand classification result, determining key characteristics in the packing decision, weighting the key characteristics to obtain weighted characteristics, and further forming a weighted characteristic set; the analysis module is connected with the decision module in a data transmission mode;
The decision module is used for constructing a decision model, converting the data in the weighted feature set to obtain a converted feature set, extracting decision logic based on the converted feature set, and outputting an optimal bagging machinery operation parameter adjustment scheme; the decision module is connected with the parameter adjustment module in a data transmission mode;
the parameter adjustment module is used for designing a self-adaptive algorithm and adjusting the operation parameters of the packaging machine according to an optimal packaging machine operation parameter adjustment scheme; the parameter adjustment module is connected with the instruction generation module in a data transmission mode;
The instruction generation module is used for converting the operation parameters of the packaging machine output by the parameter adjustment module into specific machine operation instructions, such as motor rotation speed, servo control signals and the like.
Referring to fig. 2, a flowchart of a method for automatically packing the dried sand according to an embodiment of the present invention includes the steps of:
s1, acquiring a sand image, converting the sand image into space-time characteristics by using a space-time transformation algorithm, and fusing the characteristics to obtain comprehensive characteristics, so as to classify the sand and obtain a sand classification result;
And acquiring sand images from an automatic packaging system, wherein an image acquisition module captures visual information of sand at different time points through a high-resolution camera. The acquired image is clear, and the size, shape and texture of sand grains can be displayed. The acquired sand image is subjected to standardization processing, including image size adjustment, unnecessary background clipping, contrast adjustment and the like, and the standardization processing method is adopted by the prior art.
The space-time transformation module applies a space-time transformation algorithm to transform the sand image into multi-dimensional space-time characteristics, and transforms the traditional static image into dynamic characteristic representation capable of expressing the time and space changes of sand, so as to capture the time dynamic changes of sand, such as tiny changes of color and shape, and help to highlight the unique characteristics of sand, and provide information for deeper analysis. The space-time transformation formula is:
，
Wherein, Representing a spatiotemporal data transformation function, representing the sand image/>Converting into multidimensional space-time features, representing feature fusion operation, representing combining features extracted by different scales, and using convolution kernel/>For each channel of the grit imageA convolution operation is performed and the parameters/>, is passedAnd/>Introducing nonlinearity to enhance the expressive force of the feature,/>Is/>Index of the convolution kernel,/>Is/>Index of the individual channels.
And fusing the multidimensional space-time characteristics converted by the space-time conversion algorithm, and synthesizing the multidimensional space-time characteristics captured from different time points into a comprehensive characteristic so that the comprehensive characteristic can comprehensively represent the comprehensive properties of sand grains. The fusion module enhances the fusion of different dimension characteristics through hyperbolic sine functions and nonlinear operation, and the fusion formula is as follows:
，
Wherein, Is a comprehensive feature obtained by fusing multidimensional space-time features,/>Is/>Overall weight of row element,/>First/>, representing multi-dimensional spatiotemporal featuresLine/>Column element,/>Represents the/>Line/>Offset of column element,/>Representing control of the first/>Line/>Parameters of nonlinear transformation intensity of column elements,/>An exponential parameter representing the degree of adjustment nonlinearity.
And inputting the comprehensive characteristics into a dynamic network of the classification module for classification, analyzing the comprehensive characteristics by utilizing a nonlinear function and a parameterized structure, and finally outputting the types of sand grains. The classification formula is:
，
Wherein, Representing a dynamic network classification function for converting composite features into grit classification results,/>Represents the/>Weights of individual network nodes,/>Represents the/>The individual network node is directed to the/>Scaling factor of individual features,/>Representing control of the first/>Pair of individual network nodes/>Parameters of individual feature sensitivity,/>Represents the/>The individual network node is processing the/>Reference feature value at each feature/(Representing the bias term. The dynamic network classification result is a multi-dimensional vector, and subtle differences of sand grains, such as the size, shape and texture of the grain size, can be identified, so that efficient and accurate operation is realized in the automatic packaging process.
S2, analyzing the sand classification result, and carrying out weighting treatment to obtain a weighted feature set; and introducing a decision model to obtain the output of the decision model, analyzing the output of the decision model, converting the output of the decision model into a mechanical operation instruction, and adjusting the operation parameters of the packaging machine according to the mechanical operation instruction.
The sand classification results are analyzed to identify key features that affect the packing efficiency. First, feature importance assessment is performed, an analysis module uses statistical or machine learning methods to determine features that play a key role in classification decisions, and feature importance is assessed by a decision tree model, such as random forests. And according to the evaluation result of the importance of the features, a weight is given to each feature, and the larger the weight is, the more important the feature is represented. The characteristics after the weighting process are expressed as:
，
Wherein, A/>, representing the weighted grit classification resultThe characteristics of the weighted sand classification result form a weighted characteristic set; /(I)Is a weight coefficient for weighting each feature in the classification result; is designed to amplify the nonlinear relationship in the features, ensuring that even small changes can be identified and amplified; /(I) Represents the/>, in the sand classification resultSuch as the size, shape, or texture of the sand particles.
The analysis module converts the data in the weighted feature set into a format which is more suitable for decision model processing by utilizing a feature conversion algorithm, obtains the converted feature set by utilizing a conversion formula, and maps the classification result to a feature space with higher dimension, thereby enhancing the relativity and the degree of distinction between the data. The conversion formula is:
，
Wherein, Representing the nth feature in the transformed feature set,/>Is a parameter controlling the intensity of nonlinear conversion. The conversion formula combines the form of sine and cosine functions and a rational function to create a nonlinear feature conversion process to better capture the subtle differences between sand characteristics.
The decision module builds a decision model, extracts decision logic from the converted feature set to guide the operation parameter adjustment of the packaging machine, and outputs an optimal packaging machine operation parameter adjustment scheme. The formula used by the decision model is:
，
Wherein, Representing the output of the decision model,/>Weight coefficient representing nth feature in converted feature set,/>Representing the/>, in the feature set after control transformationParameters of individual characteristic transition intensities,/>Representing the/>, in the transformed feature setAnd (3) adjusting the bias of the decision model according to the baseline offset of each feature.
Based on the output of the decision model, the parameter adjustment module designs a self-adaptive algorithm, analyzes the output of the decision model, converts the output into a specific mechanical operation instruction, and adjusts the operation parameters of the packaging machine according to the specific mechanical operation instruction so as to optimize the packaging process. The algorithm formula is realized:
，
Wherein, Indicating the operating parameters of the adjusted packaging machine,/>Representing the coefficient controlling the conversion strength of the arctangent function,/>Baseline offset representing square root transition,/>And/>Is an adjustment coefficient for balancing and adjusting the intensity and sensitivity of parameter adjustment,/>Is the operating parameter of the packaging machine before adjustment.
Instruction generation module interpretationTo determine which specific operations of the packaging machine they respectively control. For example, certain operating parameters may control the speed of the bagging machine, certain operating parameters may control the range of motion of the robotic arm, and operating parameters that relate to the accuracy or strength of the bagging. Will/>The values of each of the operating parameters of (a) are converted into specific instructions for the operation of the machine, such as motor speed, servo control signals, etc.
During the automatic packing process, the automatic packing system continuously monitors packing quality and efficiency. If any deviation or problem in the packing process is detected, the automatic packing system needs to feed back the detected deviation or problem in real time, and further adjusts the operation parameters of the packing machine to optimize the automatic packing of the baking sand.
In summary, the automatic packaging system and method for the baked sand are completed.
The sequence of the embodiments of the invention is merely for description and does not represent the advantages or disadvantages of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
In this specification, each embodiment is described in a progressive manner, and the same or similar parts of each embodiment are referred to each other, and each embodiment mainly describes differences from other embodiments.
The above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention, and are intended to be included in the scope of the present invention.
Claims (8)
1. The automatic packing method for the baked sand is characterized by comprising the following steps of:
s1, acquiring a sand image, converting the sand image into space-time characteristics by using a space-time transformation algorithm, and fusing the characteristics to obtain comprehensive characteristics, so as to classify the sand and obtain a sand classification result;
S2, analyzing the sand classification result, and carrying out weighting treatment to obtain a weighted feature set; and introducing a decision model to obtain the output of the decision model, analyzing the output of the decision model, converting the output of the decision model into a mechanical operation instruction, and adjusting the operation parameters of the packaging machine according to the mechanical operation instruction.
2. The method for automatically packing the dried sand according to claim 1, wherein the step S1 specifically comprises:
in the process of feature fusion, a fusion formula is introduced to fuse the space-time features into comprehensive features.
3. The method for automatically packing the dried sand according to claim 2, wherein the step S1 specifically comprises:
In the process of classifying the sand grains, a classification formula is introduced, and the classification of the sand grains is output based on the comprehensive characteristics.
4. The automatic packing method of the dried sand according to claim 1, wherein the step S2 specifically comprises:
in the analysis process of the sand classification result, a decision tree model is introduced, the feature importance of the sand classification result is estimated, and weighting treatment is carried out according to the estimation result of the feature importance of the sand classification result, so that weighted features are obtained, and a weighted feature set is formed.
5. The method for automatically packing the dried sand according to claim 4, wherein the step S2 specifically comprises:
In the implementation process of the decision model, the data in the weighted feature set is converted into a format suitable for the decision model by utilizing a feature conversion algorithm, so that the converted feature set is obtained.
6. The method for automatically packing the dried sand according to claim 5, wherein the step S2 specifically comprises:
Based on the converted feature set, extracting decision logic, and further outputting an optimal packaging machinery operation parameter adjustment scheme.
7. The method for automatically packing the dried sand according to claim 6, wherein the step S2 specifically comprises:
in the process of converting the optimal packaging machinery operation parameter adjustment scheme into a machinery operation instruction, designing a self-adaptive algorithm, and analyzing the optimal packaging machinery operation parameter adjustment scheme.
8. An automatic packaging system for the dried sand, which is applied to the automatic packaging method for the dried sand according to claim 1, and is characterized by comprising the following parts:
the system comprises an image acquisition module, a space-time conversion module, a fusion module, a classification module, an analysis module, a decision module, a parameter adjustment module and an instruction generation module;
The image acquisition module is used for capturing information of sand grains and obtaining a sand grain image; the image acquisition module is connected with the space-time conversion module in a data transmission mode;
The space-time transformation module is used for converting the sand image into space-time characteristics by applying a space-time transformation algorithm; the space-time conversion module is connected with the fusion module in a data transmission mode;
The fusion module is used for fusing the time-space characteristics to obtain comprehensive characteristics; the fusion module is connected with the classification module in a data transmission mode;
The classifying module is used for classifying based on comprehensive characteristics by utilizing a dynamic network, outputting the types of sand grains and obtaining a sand grain classifying result; the classification module is connected with the analysis module in a data transmission mode;
The analysis module is used for analyzing sand classification results, determining characteristics influencing the packing process, weighting the characteristics influencing the packing process to obtain weighted characteristics, and further forming a weighted characteristic set; the analysis module is connected with the decision module in a data transmission mode;
the decision module is used for constructing a decision model, converting the data in the weighted feature set to obtain a converted feature set, and outputting an optimal packaging machinery operation parameter adjustment scheme based on the converted feature set; the decision module is connected with the parameter adjustment module in a data transmission mode;
the parameter adjustment module is used for designing a self-adaptive algorithm and adjusting the operation parameters of the packaging machine according to an optimal packaging machine operation parameter adjustment scheme; the parameter adjustment module is connected with the instruction generation module in a data transmission mode;
The instruction generation module is used for converting the operation parameters of the packaging machine into machine operation instructions.
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