An intelligent monitoring and processing method and system for surplus steel

Abstract

This invention discloses an intelligent monitoring and processing method and system for surplus steel, relating to the field of data processing technology. The method includes: obtaining basic component information of the surplus steel; acquiring multi-angle images of the surplus steel using an image acquisition device to obtain image acquisition results; marking the surplus steel from the front and recording the marking direction; performing feature recognition on the image acquisition results, and constructing a feature library for the surplus steel based on the feature recognition results and the marking direction; acquiring the dimensions of the surplus steel using a dimension measuring device, and integrating the data according to the marking direction to construct a dimension library for the surplus steel; generating management tags for the surplus steel using the dimension library and the feature library, and managing the surplus steel using these management tags. This invention solves the technical problems of insufficient intelligence in surplus steel management and low utilization rate in existing technologies, achieving the technical effects of improving the intelligence level of surplus steel management, increasing management efficiency, and maximizing the utilization of surplus steel.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, specifically to an intelligent monitoring and processing method and system for steel. Background Technology

[0002] The steel industry plays a significant supporting and driving role in the equipment manufacturing industry. With the continuous development of science and technology, steel production and manufacturing technologies are constantly improving, and steel quality is continuously rising. As manufacturing levels improve, the management level of steel utilization also needs to keep pace to improve steel utilization.

[0003] Currently, steel production generates surplus materials. The use of these surplus materials is planned by production technicians: reprocessable steel is utilized, while unprocessable steel is scrapped. However, relying solely on technicians for surplus material management is limited by their capabilities, resulting in low utilization rates. When the surplus material volume is large, timely analysis and processing are impossible, leading to further low utilization. Existing technologies for surplus material management lack intelligent systems, resulting in low utilization rates. Summary of the Invention

[0004] This application provides an intelligent monitoring and processing method and system for surplus steel, which addresses the technical problems of insufficient intelligence in the management of surplus steel and low utilization rate of surplus steel in the prior art.

[0005] In view of the above problems, this application provides an intelligent monitoring and processing method and system for steel.

[0006] The first aspect of this application provides an intelligent monitoring and processing method for steel bars, wherein the method is applied to an intelligent monitoring and processing system, the intelligent monitoring and processing system being communicatively connected to an image acquisition device and a dimension measuring device, and the method includes:

[0007] Obtain basic composition information of Yu Gang;

[0008] The image acquisition device is used to acquire images of the steel from multiple angles to obtain the image acquisition results.

[0009] Mark the remaining steel on the front and record the marking direction;

[0010] The image acquisition results are subjected to feature recognition, and the feature library of Yu Gang is constructed based on the feature recognition results and the identification direction;

[0011] The dimensions of the surplus steel are collected using the dimensional measuring device, and the data is integrated according to the marked direction to construct a dimensional library of the surplus steel.

[0012] Management tags for the surplus steel are generated using the size library and the feature library, and the surplus steel is managed using these management tags.

[0013] A second aspect of this application provides an intelligent monitoring and processing system for Yu Gang, the system comprising:

[0014] A component information acquisition module is used to obtain the basic component information of Yu Gang.

[0015] The acquisition result acquisition module is used to acquire multi-angle images of the steel bar through an image acquisition device and obtain image acquisition results.

[0016] The marking direction recording module is used to mark the front of the remaining steel and record the marking direction;

[0017] A feature library construction module is used to perform feature recognition on the image acquisition results and construct the feature library of Yu Gang based on the feature recognition results and the identification direction;

[0018] A dimension library construction module is used to collect the dimensions of the surplus steel through a dimension measuring device, and integrate the data according to the marked direction to construct a dimension library for the surplus steel.

[0019] The surplus steel management module is used to generate management tags for the surplus steel through the size library and the feature library, and to manage the surplus steel through the management tags.

[0020] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0021] This application obtains basic composition information of surplus steel, then acquires multi-angle images of the surplus steel using an image acquisition device, obtains the image acquisition results, marks the surplus steel from the front, and records the marking direction. Feature recognition is performed on the image acquisition results, and a feature library of the surplus steel is constructed based on the feature recognition results and the marking direction. Then, the dimensions of the surplus steel are acquired using a dimensional measuring device, and the data is integrated according to the marking direction to construct a dimensional library of the surplus steel. Finally, a management tag for the surplus steel is generated using the dimensional library and the feature library, and the surplus steel is managed through the management tag. This achieves the technical effect of improving the intelligence level of surplus steel management and increasing processing efficiency. Attached Figure Description

[0022] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0023] Figure 1 A schematic flowchart of an intelligent monitoring and processing method for steel bars provided in this application embodiment;

[0024] Figure 2 A flowchart illustrating the process of adding size identifiers to management tags in an intelligent monitoring and processing method for surplus steel provided in this application embodiment;

[0025] Figure 3 A flowchart illustrating the process of adding size correlation coefficients and size identifiers to management tags in an intelligent monitoring and processing method for surplus steel provided in this application embodiment;

[0026] Figure 4 This is a schematic diagram of the intelligent monitoring and processing system for Yugang provided in an embodiment of this application.

[0027] Figure labeling: Module 11 for obtaining composition information, Module 12 for obtaining acquisition results, Module 13 for recording orientation, Module 14 for building feature library, Module 15 for building size library, and Module 16 for managing surplus steel. Detailed Implementation

[0028] This application provides an intelligent monitoring and processing method for surplus steel, which addresses the technical problems of insufficient intelligence in the management of surplus steel and low utilization rate of surplus steel in the prior art.

[0029] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0030] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or server that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or modules not explicitly listed or inherent to such processes, methods, products, or devices. Example 1

[0031] like Figure 1 As shown, this application provides an intelligent monitoring and processing method for steel bars, wherein the method is applied to an intelligent monitoring and processing system, the intelligent monitoring and processing system being communicatively connected to an image acquisition device and a dimension measuring device, and the method includes:

[0032] Step S100: Obtain the basic composition information of the steel;

[0033] Specifically, the image acquisition device is used to acquire images of the surplus steel from multiple angles, including infrared cameras, video cameras, and still cameras. The dimension measuring device is used to accurately measure the dimensions of the surplus steel, including total stations, rulers, profilometers, length measuring machines, and vertical optical meters. To scientifically and intelligently monitor the surplus steel, the dimensions and images of the surplus steel are measured using the image acquisition device and the dimension measuring device, thereby providing a basis for management. The basic composition information is obtained by collecting data on the elements constituting the surplus steel. Preferably, this basic composition information is obtained from steel procurement information, including the constituent elements of steel, thickness, special elements, carbon content, etc. Obtaining the basic composition information of the surplus steel provides a basis for subsequent management and utilization of the surplus steel, allowing for the retrieval of corresponding types of surplus steel for production according to product needs.

[0034] Step S200: The image acquisition device is used to acquire multi-angle images of the steel bar to obtain image acquisition results;

[0035] Specifically, the image acquisition device is used to acquire images of the stored steel bars from multiple angles, preferably from the front, side, and top views. The images acquired from multiple angles are used as the image acquisition results, which reflect the basic appearance information of the steel bars, including their shape, surface condition, and corrosion status.

[0036] Step S300: Mark the remaining steel from the front and record the marking direction;

[0037] Specifically, the type of surplus steel is determined based on its cross-sectional shape, such as square steel, round steel, flat steel, angle steel, I-beam, etc. The front side of the surplus steel for use is then determined and marked, resulting in the marking direction. This marking direction reflects the direction in which the surplus steel is used. For example, during the use of an I-beam, the side with the groove is placed face up, facilitating welding of the groove edge to other steel materials. This also lays the groundwork for subsequent identification of the surplus steel type.

[0038] Step S400: Perform feature recognition on the image acquisition results, and construct the feature library of Yu Gang based on the feature recognition results and the identification direction;

[0039] Specifically, feature recognition is performed based on the image acquisition results. Preferably, feature recognition is performed on the images in the image acquisition results from three dimensions: the surface condition of the surplus steel, the usable area, and the cross-sectional shape of the surplus steel. The result of feature recognition for each image is used as the feature recognition result. The feature recognition result reflects the type and usability of the surplus steel, including surface condition characteristics, usability characteristics, and cross-sectional shape characteristics. The surface condition characteristics of the surplus steel describe the surface defects, including scabs, bends, pits, and dents.

[0040] Specifically, by determining the feature type of the surplus steel based on the feature recognition result, and determining the steel usage direction corresponding to the feature based on the identification direction, a feature library of the surplus steel is constructed. The feature library is a database that stores the usable surplus steel types and corresponding usage direction features determined based on the feature recognition result and the identification direction.

[0041] Step S500: The dimensions of the surplus steel are collected by the dimension measuring device, and the data is integrated according to the marked direction to construct the dimension library of the surplus steel;

[0042] Specifically, the dimensional measuring device collects dimensions of the surplus steel to determine its usable dimensions, resulting in dimensional acquisition results. These results reflect the available dimensions of the surplus steel, providing a basis for subsequent retrieval of steel of the required dimensions. Furthermore, the dimensional acquisition results are integrated using the indicated directions; that is, the dimensions in the indicated directions are used as the actual usable dimensions of the surplus steel, thus creating a dimensional database for the surplus steel. This database is a summary of the dimensions of the surplus steel. This achieves the technical effect of providing a basis for subsequent dimensional retrieval.

[0043] Step S600: Generate a management tag for the surplus steel using the size library and the feature library, and manage the surplus steel using the management tag.

[0044] Furthermore, such as Figure 2 As shown, step S600 in this embodiment further includes:

[0045] Step S610: Input the size library and the feature library into the three-dimensional steel fitting model, and output the steel fitting result;

[0046] Step S620: Analyze the usage dimensions of the surplus steel based on the fitting results of the surplus steel, and generate a usage dimension identifier based on the usage dimension analysis results;

[0047] Step S630: Add the size identifier to the management label.

[0048] Furthermore, such as Figure 3 As shown, step S600 in this embodiment further includes:

[0049] Step S640: Collect and obtain the factory's historical processing information;

[0050] Step S650: Match the processing parameters of the historical processing information with the basic component information and the size library to obtain the processing parameter matching result;

[0051] Step S660: Generate dimensional correlation coefficients based on the processing parameter matching results;

[0052] Step S670: Add the size correlation coefficient and the size identifier to the management label.

[0053] Specifically, the management tag is a label used to identify the availability of surplus steel during the process of managing and calling upon it. This allows for quick management, processing, and monitoring of the surplus steel. The three-dimensional surplus steel fitting model is a functional model that performs a three-dimensional simulation of the morphology of surplus steel based on the dimensions and features in the size and feature libraries. This model is constructed using a BP neural network framework, with the size and feature libraries as input data and the surplus steel fitting result as output data. The surplus steel fitting result is obtained after a three-dimensional simulation of the availability of surplus steel. By analyzing different usage sizes and features of the surplus steel, the distribution of usable surplus steel is obtained.

[0054] Specifically, by extracting all the usable dimensions of the surplus steel and the corresponding features of the surplus steel for different usable dimensions from the surplus steel fitting results, the usable dimensions of the surplus steel are analyzed based on the surplus steel features. For example, based on the surface condition of the surplus steel, dimensions corresponding to bending degrees exceeding the usable range are discarded, thus the remaining dimensions are taken as the usable dimensions of the surplus steel. Preferably, through different processing techniques, such as plate rolling and cutting, the machinable dimensions of the surplus steel are analyzed according to different processing techniques to obtain multiple machinable dimensions. The largest dimension among these multiple machinable dimensions is taken as the usable dimension analysis result, whereby the usable dimension analysis result is the maximum usable dimension of the surplus steel, i.e., the usability limit value of the surplus steel. The usable dimension identifier is a specific identifier for the corresponding usable dimension value in the usable dimension analysis result, thereby providing a reliable identifier for subsequent retrieval of the corresponding dimension. Furthermore, the dimension identifier is added to the management tag for convenient retrieval, identification, and extraction in subsequent management.

[0055] Specifically, the historical processing information is information obtained by recording data generated during the factory's historical processing, including processed product type, processing technology, steel utilization rate, and steel type. The processing parameters in the historical processing information are matched based on the basic component information and the size database. In other words, the processing parameters corresponding to the processing information in the historical processing information that successfully matches the basic component information and the size database are matched, using the processing parameter matching results to determine if the surplus steel can be processed. The processing parameter matching results are the parameters for processing the surplus steel, including processing technology, processing equipment, and processing equipment operating parameters. Furthermore, based on the processing parameter matching results, size-related coefficients are obtained for each matching processing parameter. The closer the size is to the corresponding processing size in the processing parameter matching results, the larger the corresponding size correlation coefficient, indicating a better fit for processing requirements. Finally, the surplus steel is managed by adding the size correlation coefficient and the size identifier to the management tag.

[0056] Furthermore, step S630 in this embodiment of the application also includes:

[0057] Step S631: Collect and obtain the machining dimension information of the workpiece;

[0058] Step S632: Perform size adaptation analysis using the size identifier in the management label and the processing size information to obtain the adaptation determination result;

[0059] Step S633: When the adaptation determination result is passed, the processing size information and the size identifier are obtained to generate a size difference;

[0060] Step S634: The size difference is weighted and calculated using the size correlation coefficient, and the matching value between the surplus steel and the workpiece is generated based on the weighted calculation result;

[0061] Step S635: Manage the call of the remaining steel using the adaptation value.

[0062] Furthermore, step S633 in this embodiment of the application also includes:

[0063] Step S6331: Obtain the machining feature information of the workpiece, adjust the part identification according to the machining feature information and the feature library, and obtain the part identification result;

[0064] Step S6332: Generate the difference influence coefficient based on the location identification results;

[0065] Step S6333: Generate the size difference based on the difference influence coefficient, the processing size information, and the size identifier.

[0066] Specifically, the workpiece refers to the components that the factory needs to process, including shells, steel plates, bolts, etc. The processing dimension information refers to the corresponding dimensions of the workpiece, including thickness and height. The size of the surplus steel is obtained based on the dimension identifier in the management tag, and then matched with the size in the processing dimension information to obtain the adaptation determination result. The adaptation determination result is obtained after judging whether the size of the surplus steel can meet the processing dimension requirements of the workpiece. Preferably, a processing threshold is obtained based on the processing dimension information, i.e., the range of raw material dimensions required for the workpiece is obtained, and then it is determined whether the dimension identifier is within the raw material dimension range. If it is, the adaptation determination result is passed; if not, the adaptation determination result is failed.

[0067] Specifically, when the adaptation determination result is passed, the difference between the size of the surplus steel corresponding to the size identifier in the management tag and the size in the processing size information is calculated to obtain the size difference value. Then, the size difference value is weighted using the size correlation coefficient to obtain the adaptation value. The adaptation value reflects the degree of fit between the surplus steel and the workpiece size. By setting an error threshold, the closer the size difference in the weighted calculation result is to the error threshold, the higher the adaptation value. A value far from the error threshold indicates that the surplus steel size is too large or too small. A size difference smaller than the error threshold indicates that the surplus steel size is too small, and the finished workpiece processed using the surplus steel cannot meet the processing size requirements. A size difference larger than the error threshold indicates that the surplus steel size is too large, and the surplus steel cannot be fully utilized. Therefore, based on the obtained adaptation value, the surplus steel is managed and selected for management based on the surplus steel corresponding to the high adaptation value.

[0068] Specifically, the processing feature information refers to the technological features of processing the raw materials of the workpiece, including processing direction features, processing position features, and processing method features. Then, based on the processing feature information and the feature library, the part identification is adjusted to obtain the part identification result. In other words, based on the features in the processing feature information and the residual steel features in the feature library, if the residual steel features do not meet the feature requirements in the processing feature information, the processing position of the residual steel is adjusted and identified, resulting in the part identification result. The part identification result includes the original processing position and the adjusted processing position.

[0069] Specifically, the difference influence coefficient is obtained based on the distance difference between the original processing position and the adjusted processing position in the part identification results. The larger the distance difference, the greater the impact on the processing size, leading to a reduction in the usable processing size in the surplus steel. Furthermore, the difference result is obtained based on the size information and the size identification, and the difference influence coefficient is used to weight and optimize the result to obtain the size difference.

[0070] Furthermore, step S670 in this embodiment of the application also includes:

[0071] Step S671: Obtain historical call data;

[0072] Step S672: Perform Yu Gang call statistics based on the historical call data to generate call frequency data;

[0073] Step S673: Manage the storage location of the surplus steel using the call frequency data.

[0074] Furthermore, step S670 in this embodiment of the application also includes:

[0075] Step S674: Evaluate the utilization value of surplus steel in the feature library, the size library, and the historical call data, and generate the value evaluation result;

[0076] Step S675: Determine whether the value evaluation result meets the preset value threshold;

[0077] Step S676: When the value evaluation result cannot meet the preset value threshold, the remaining steel shall be scrapped.

[0078] Specifically, the historical retrieval data refers to the data records generated during the management of surplus steel, specifically the retrieval of surplus steel from the warehouse within a historical time period. This includes the type of surplus steel retrieved, the retrieval time, and the size of the retrieved surplus steel. By statistically analyzing the frequency of surplus steel retrieval based on the type and time of retrieval in the historical retrieval data, the retrieval frequency data is obtained. This retrieval frequency data reflects the number of times different types and sizes of surplus steel were retrieved within a historical time period. Furthermore, the storage location of the surplus steel is managed based on this retrieval frequency data, placing frequently retrieved surplus steel in easily accessible areas within the warehouse, thereby achieving better retrieval efficiency and improving retrieval effectiveness.

[0079] Specifically, the utilization value of surplus steel is evaluated based on the feature library, the size library, and the historical call data. Preferably, the call frequency and call quantity in the historical call data are used as evaluation indicators to evaluate the value of surplus steel with different features and sizes, resulting in the value evaluation result. The value evaluation result reflects the usability of the surplus steel. The preset value threshold is a pre-set minimum value at which surplus steel can be utilized and generate profit. It is set by the staff and is not restricted here. When the value evaluation result does not meet the preset value threshold, it indicates that the surplus steel stored in the warehouse has too low a usage frequency and quantity, and needs to be scrapped.

[0080] In summary, the embodiments of this application have at least the following technical effects:

[0081] This application embodiment obtains basic composition information of surplus steel and acquires multi-angle images of the surplus steel using an image acquisition device, achieving the goal of providing a management basis for subsequent analysis and management of the surplus steel. Then, the surplus steel is marked from the front, and the marking direction is recorded. Feature recognition is then performed on the image acquisition results, and a feature library of the surplus steel is constructed based on the feature recognition results and the marking direction, achieving the goal of collecting and summarizing the surplus steel features. Next, the dimensions of the surplus steel are acquired using a dimensional measuring device, and the data is integrated according to the marking direction to construct a dimensional library of the surplus steel, achieving the goal of analyzing the dimensions of the surplus steel. Finally, management tags for the surplus steel are generated using the dimensional and feature libraries, and the surplus steel is managed through these management tags. This achieves the technical effect of efficient management of surplus steel and improved processing efficiency. Example 2

[0082] Based on the same inventive concept as the intelligent monitoring and processing method for surplus steel in the foregoing embodiments, such as Figure 4 As shown, this application provides an intelligent monitoring and processing system for steel. The system and method embodiments in this application are based on the same inventive concept. The system includes:

[0083] Composition information acquisition module 11, the composition information acquisition module 11 is used to obtain the basic composition information of Yu Gang;

[0084] The acquisition result acquisition module 12 is used to acquire multi-angle images of the steel bar through an image acquisition device and obtain image acquisition results.

[0085] The marking direction recording module 13 is used to mark the front of the remaining steel and record the marking direction;

[0086] Feature library construction module 14 is used to perform feature recognition on the image acquisition results and construct the feature library of Yu Gang based on the feature recognition results and the identification direction;

[0087] The dimension library construction module 15 is used to collect the dimensions of the surplus steel through a dimension measuring device, and integrate the data according to the marked direction to construct the dimension library of the surplus steel.

[0088] The surplus steel management module 16 is used to generate management tags for the surplus steel through the size library and the feature library, and to manage the surplus steel through the management tags.

[0089] Furthermore, the system also includes:

[0090] The fitting result output unit is used to input the size library and the feature library into the three-dimensional steel fitting model and output the steel fitting result.

[0091] A size label generation unit is used to perform a usage size analysis of the surplus steel based on the fitting results of the surplus steel, and generate a usage size label based on the usage size analysis results.

[0092] A size label adding unit is used to add the size label to the management label.

[0093] Furthermore, the system also includes:

[0094] A historical processing information acquisition unit is used to collect and obtain historical processing information of the factory.

[0095] A parameter matching result obtaining unit is used to match the processing parameters of the historical processing information based on the basic component information and the size library, and obtain the processing parameter matching result.

[0096] A correlation coefficient generation unit is used to generate a size correlation coefficient based on the processing parameter matching result;

[0097] A label adding unit is used to add the size correlation coefficient and the size identifier to the management label.

[0098] Furthermore, the system also includes:

[0099] A machining dimension information acquisition unit is used to acquire machining dimension information of the workpiece;

[0100] A determination result obtaining unit is used to perform size adaptation analysis through the size identifier in the management label and the processing size information to obtain an adaptation determination result;

[0101] A size difference generation unit is used to generate a size difference by obtaining the processing size information and the size identifier when the adaptation determination result is a pass.

[0102] The adaptation value generation unit is used to perform a weighted calculation on the size difference using the size correlation coefficient, and generate an adaptation value between the surplus steel and the workpiece based on the weighted calculation result.

[0103] The call management unit is used to manage the call of Yu Gang through the adaptation value.

[0104] Furthermore, the system also includes:

[0105] A part identification result obtaining unit is used to obtain the processing feature information of the workpiece, adjust the part identification according to the processing feature information and the feature library, and obtain the part identification result;

[0106] An influence coefficient generation unit is used to generate a difference influence coefficient based on the location identification result.

[0107] A difference generation unit is used to generate the size difference based on the difference influence coefficient, the processing size information, and the size identifier.

[0108] Furthermore, the system also includes:

[0109] A historical call data acquisition unit, wherein the historical call data acquisition unit is used to acquire historical call data;

[0110] A frequency data generation unit is used to perform Yu Gang call statistics based on the historical call data and generate call frequency data.

[0111] A storage location management unit is used to manage the storage location of the surplus steel using the call frequency data.

[0112] Furthermore, the system also includes:

[0113] A value evaluation unit is used to evaluate the value of surplus steel utilization based on the feature library, the size library, and the historical call data, and to generate a value evaluation result.

[0114] A value threshold determination unit is used to determine whether the value evaluation result meets a preset value threshold.

[0115] The scrapping unit is used to scrap the surplus steel when the value evaluation result fails to meet the preset value threshold.

[0116] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, specific embodiments have been described above. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps described in the claims can be performed in a different order than that shown in the embodiments and still achieve the desired result. Additionally, the processes depicted in the drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0117] The above description is only a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

[0118] This specification and accompanying drawings are merely illustrative examples of this application and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Clearly, those skilled in the art can make various alterations and modifications to this application without departing from its scope. Therefore, if such modifications and modifications fall within the scope of this application and its equivalents, this application intends to include such modifications and modifications.

Claims

1. A method for intelligent monitoring and processing of surplus steel, characterized in that, The method is applied to an intelligent monitoring and processing system, which is communicatively connected to an image acquisition device and a size measuring device. The method includes: Obtain basic composition information of Yu Gang; The image acquisition device is used to acquire images of the steel from multiple angles to obtain the image acquisition results. Mark the remaining steel on the front and record the marking direction; The image acquisition results are subjected to feature recognition, and the feature library of Yu Gang is constructed based on the feature recognition results and the identification direction; The dimensions of the surplus steel are collected using the dimensional measuring device, and the data is integrated according to the marked direction to construct a dimensional library of the surplus steel. Management tags for the surplus steel are generated using the size library and the feature library, and the surplus steel is managed using the management tags. The method includes: Input the size library and the feature library into the three-dimensional steel fitting model, and output the steel fitting result; The usage dimensions of the remaining steel are analyzed based on the fitting results of the remaining steel, and a usage dimension identifier is generated based on the usage dimension analysis results. Add the size identifier to the management label; collect and obtain the factory's historical processing information; Based on the basic component information and the size library, the processing parameters of the historical processing information are matched to obtain the processing parameter matching result; Generate a size correlation coefficient based on the processing parameter matching result; Add the size correlation coefficient and the size identifier to the management label; Collect and obtain the workpiece's machining dimension information; Size adaptation analysis is performed using the size identifier in the management label and the processing size information to obtain the adaptation determination result; When the adaptation determination result is passed, the processing size information and the size identifier are obtained to generate a size difference; The size difference is weighted by the size correlation coefficient, and the matching value between the surplus steel and the workpiece is generated based on the weighted calculation result. The remaining steel is managed and accessed through the adaptation value. Obtain the machining feature information of the workpiece, adjust the part identification according to the machining feature information and the feature library, and obtain the part identification result; Generate a difference influence coefficient based on the location identification results; The size difference is generated based on the difference influence coefficient, the processing size information, and the size identifier.

2. The method as described in claim 1, characterized in that, The method includes: Obtain historical call data; Based on the historical call data, Yu Gang's call statistics are performed to generate call frequency data; The storage location of the surplus steel is managed using the call frequency data.

3. The method as described in claim 2, characterized in that, The method includes: The value of surplus steel utilization is evaluated based on the feature library, the size library, and the historical call data, and a value evaluation result is generated. Determine whether the value evaluation result meets the preset value threshold; If the value evaluation result fails to meet the preset value threshold, the remaining steel shall be scrapped.

4. An intelligent monitoring and processing system for surplus steel, characterized in that, The system is used to implement the intelligent monitoring and processing method for surplus steel as described in any one of claims 1-3, and the system includes: A component information acquisition module is used to obtain the basic component information of Yu Gang. The acquisition result acquisition module is used to acquire multi-angle images of the steel bar through an image acquisition device and obtain image acquisition results. The marking direction recording module is used to mark the front of the remaining steel and record the marking direction; A feature library construction module is used to perform feature recognition on the image acquisition results and construct the feature library of Yu Gang based on the feature recognition results and the identification direction; A dimension library construction module is used to collect the dimensions of the surplus steel through a dimension measuring device, and integrate the data according to the marked direction to construct a dimension library for the surplus steel. The surplus steel management module is used to generate management tags for the surplus steel through the size library and the feature library, and to manage the surplus steel through the management tags.

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