Intelligent equipment and control method for merging multiple angle steel production lines

Abstract

This invention relates to an intelligent device and control method for merging multiple angle steel production lines, belonging to the field of angle steel production technology. The invention integrates real-time data into an Internet of Things (IoT) platform to acquire real-time processing data. Based on multi-scale processing parameter features, a processing anomaly identification model is constructed. This model identifies anomalies in the processing parameter data of each processing step, performs processing inspection on the angle steel during processing, and marks the angle steel according to the inspection results. By integrating IoT technology and adversarial neural networks, this invention combines historical index factors corresponding to each processing parameter and trains the adversarial neural network to identify processing anomalies caused by parameter coupling. It can quickly and accurately locate areas of abnormal processing parameters, preventing processing from continuing due to anomalies and reducing the generation of defective products.

Description

Technical Field

[0001] This invention relates to the field of angle steel production technology, and in particular to an intelligent device and control method for merging multiple angle steel production lines. Background Technology

[0002] Angle steel is a type of carbon structural steel suitable for the construction industry. It can be used to fix various buildings, serving as a fixed angle. Angle steel can be assembled into various load-bearing components according to different structural needs, and can also be used as connecting parts between components. It is widely used in various building and engineering structures, such as roof beams, bridges, transmission towers, lifting and transportation machinery, ships, industrial furnaces, reaction towers, container racks, cable trench supports, power piping, busbar support installation, and warehouse shelves. Angle steel belongs to carbon structural steel for construction, and is a simple cross-section steel material, mainly used for metal components and factory frames. In use, it requires good weldability, plastic deformation performance, and certain mechanical strength. The raw material for producing angle steel is low-carbon square steel billet, and the finished angle steel is delivered in hot-rolled, normalized, or hot-rolled condition. Nowadays, angle steel production equipment is gradually shifting to intelligent production. However, abnormal situations can occur during the processing of angle steel, leading to unreasonable processing parameters and a large number of scrap products. Moreover, there is a certain potential influence relationship between processing parameters. Different combinations of processing parameters will lead to different processing results, which will reduce the accuracy of abnormal identification of processing parameters. Summary of the Invention

[0003] This invention overcomes the shortcomings of the prior art and provides an intelligent device and control method for merging multiple angle steel production lines.

[0004] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0005] The first aspect of this invention provides an intelligent device for merging multiple angle steel production lines, comprising:

[0006] The feeding module transfers the angle steel to be processed to the conveying module, which then transfers it to the processing module. After processing by the processing module, the unloading module unloads the processed angle steel.

[0007] The detection module performs processing inspection on the angle steel during the processing and marks the angle steel according to the processing inspection results;

[0008] The Internet of Things (IoT) platform transmits real-time data from the feeding module, conveying module, processing module, and unloading module to the IoT platform, and identifies anomalies in the real-time data through the IoT platform.

[0009] A second aspect of the present invention provides a control method for intelligent equipment in a combined production line of multiple angle steel lines, comprising the following steps:

[0010] The real-time data from the feeding module, conveying module, processing module, and unloading module are integrated into the Internet of Things (IoT) platform, and real-time processing data information is obtained through the IoT platform.

[0011] A processing anomaly identification model is constructed based on the real-time processing data information;

[0012] The aforementioned processing anomaly identification model is used to identify anomalies in the processing parameter data information of each processing flow.

[0013] Anomaly identification is used to inspect the angle steel during the processing, and the angle steel is marked according to the inspection results.

[0014] Furthermore, in the control method of intelligent equipment for multiple angle steel combined production lines, real-time data from the feeding module, conveying module, processing module, and unloading module are integrated into an Internet of Things (IoT) platform. Real-time processing data information is obtained through the IoT platform, specifically as follows:

[0015] The file size information of real-time data from the feeding module, conveying module, processing module, and unloading module is obtained, and several information transmission paths are constructed. Several information transmission paths are randomly selected for information transmission, and the transmission paths are tested based on the file size information of the real-time data.

[0016] By conducting information transmission tests, real-time information transmission rate information is obtained, and a preset information transmission rate threshold is obtained. It is then determined whether the real-time information transmission rate information is greater than the preset information transmission rate threshold.

[0017] When the real-time information transmission rate is greater than the preset information transmission rate threshold, information is transmitted according to the current information transmission paths.

[0018] When the real-time information transmission rate is not greater than the preset information transmission rate threshold, several information transmission paths are reset for information transmission, the data is integrated into the Internet of Things (IoT) platform, and real-time processing data information is obtained through the IoT platform.

[0019] Furthermore, in the control method of intelligent equipment for multiple angle steel combined production lines, a processing anomaly identification model is constructed based on the real-time processing data information, specifically including:

[0020] A processing anomaly identification model is constructed based on generative adversarial networks. Processing parameter data information in each processing flow is obtained from the real-time processing data information. Multi-scale processing parameter features are constructed based on the processing parameter data information in each processing flow.

[0021] A Bayesian network is introduced, and the influencing factors of the processing parameter index are set. The influencing factors of the processing parameter index are used as dependent variables, and the multi-scale processing parameter features are all used as independent events. The dependent variable and the independent events are input into the Bayesian network.

[0022] A multi-parameter optimization objective function is constructed based on the independent events. The multi-parameter optimization objective function is then input into a Bayesian network to obtain the potential relationship between the multi-parameter optimization objective function and the dependent variable.

[0023] The potential relationship between the multi-parameter optimization objective function and the dependent variable is input into the processing anomaly identification model for training, thereby obtaining a processing anomaly identification model that meets expectations.

[0024] Furthermore, in the control method of intelligent equipment for multiple angle steel combined production lines, the processing anomaly identification model is used to identify anomalies in the processing parameter data information of each processing flow, specifically including:

[0025] Obtain processing parameter data information for each processing step, and input the processing parameter data information for each processing step into the processing anomaly identification model;

[0026] The generator predicts abnormal parameters based on the processing parameter data in the processing flow and obtains the initial parameter identification results.

[0027] The initial parameter identification result is input into the discriminator for judgment. The discriminator determines whether to accept the initial parameter identification result. If accepted, the initial parameter identification result is output.

[0028] If the initial parameter identification result is the preset parameter identification result, an abnormal parameter identification result is output; if the initial parameter identification result is not the preset parameter identification result, a normal parameter identification result is output.

[0029] Furthermore, in the control method of intelligent equipment for multiple angle steel production lines, anomaly identification is used to detect and process the angle steel during processing. Specifically:

[0030] By identifying anomalies, abnormal processing parameter data information is obtained, and a processing parameter data evaluation index is set to determine whether the abnormal processing parameter data information is greater than the processing parameter data evaluation index.

[0031] When the abnormal processing parameter data exceeds the processing parameter data evaluation index, the corresponding angle steel will be scrapped.

[0032] When the abnormal processing parameter data is not greater than the processing parameter data evaluation index, a repairable angle steel is generated, and processing test results are output based on the scrapped angle steel and the repairable angle steel.

[0033] Furthermore, in the control method of intelligent equipment for multiple angle steel production lines, the angle steel during processing is marked according to the processing and inspection results, specifically including:

[0034] If the processing and inspection results indicate that the angle steel is to be scrapped, then the scrapped angle steel will be marked and centrally processed.

[0035] The number of angle steels scrapped within a preset time period is statistically analyzed, and a threshold for the number of angle steels scrapped is set. The result is then determined whether the number of angle steels scrapped within the preset time period is greater than the threshold for the number of angle steels scrapped.

[0036] If the number of scrapped angle steels within the preset time exceeds the threshold number of scrapped angle steels, the intelligent equipment of the multiple angle steel merging production lines will be controlled to stop working.

[0037] When the number of scrapped angle steels within the preset time is not greater than the threshold number of scrapped angle steels, the intelligent equipment of the multiple angle steel merging production lines will maintain normal operation.

[0038] This invention addresses the shortcomings of the prior art and has the following beneficial effects:

[0039] This invention integrates real-time data from the feeding, conveying, processing, and unloading modules into an IoT platform, acquiring real-time processing data through the IoT platform. A processing anomaly identification model is constructed based on multi-scale processing parameter features. This model identifies anomalies in the processing parameter data of each processing step. Through anomaly identification, the angle steel in the processing process is inspected and marked according to the inspection results. This invention combines IoT technology with adversarial neural networks, combining historical index factors corresponding to each processing parameter and training the network to identify processing anomalies caused by parameter coupling. It can quickly and accurately locate areas of abnormal processing parameters, preventing processing from continuing due to anomalies and reducing the production of defective products. Attached Figure Description

[0040] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other embodiments can be obtained from these drawings without creative effort.

[0041] Figure 1 A schematic diagram of the intelligent equipment for merging multiple angle steel production lines is shown.

[0042] Figure 2 A flowchart illustrating the control method for intelligent equipment in a combined production line of multiple angle steel lines is shown. Detailed Implementation

[0043] To better understand the above-mentioned objectives, features, and advantages of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted that, unless otherwise specified, the embodiments and features described in these embodiments can be combined with each other.

[0044] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and therefore the scope of protection of the invention is not limited to the specific embodiments disclosed below.

[0045] The first aspect of this invention provides an intelligent device for merging multiple angle steel production lines, comprising:

[0046] like Figure 1 As shown, the first aspect of the present invention provides an intelligent device for merging multiple angle steel production lines, comprising:

[0047] The feeding module 10 transfers the angle steel to be processed to the conveying module, and then the conveying module transfers the angle steel to the processing module. After processing by the processing module, the unloading module unloads the processed angle steel.

[0048] Several detection modules 20 are used to detect the angle steel during the processing and mark the angle steel during the processing based on the detection results;

[0049] The IoT platform transmits real-time data from the feeding module 10, conveying module 40, several processing modules 50, and unloading module 30 to the IoT platform, and identifies anomalies in the real-time data through the IoT platform.

[0050] It should be noted that this invention integrates Internet of Things technology and adversarial neural networks, thereby combining historical index factors corresponding to each processing parameter and training them with adversarial neural networks. This enables the identification of processing anomalies caused by the coupling between parameters, and can quickly and accurately locate the areas of abnormal processing parameters. This prevents situations where processing continues due to anomalies, thereby reducing the production of defective products.

[0051] like Figure 2 As shown, a second aspect of the present invention provides a control method for intelligent equipment in a combined production line of multiple angle steel lines, comprising the following steps:

[0052] S102: Integrate the real-time data from the feeding module, conveying module, processing module, and unloading module into the IoT platform, and obtain real-time processing data information through the IoT platform; S104: Build a processing anomaly identification model based on the real-time processing data information;

[0053] S106: Anomalies are identified in the processing parameter data information of each processing flow through the processing anomaly identification model;

[0054] S108: Through anomaly identification, the angle steel in the processing process is inspected, and the angle steel in the processing process is marked according to the inspection results.

[0055] It should be noted that this invention integrates Internet of Things (IoT) technology and adversarial neural networks (ANNs). By combining historical index factors corresponding to various processing parameters and training the ANN, it can identify processing anomalies caused by the coupling between parameters. It can quickly and accurately locate areas of abnormal processing parameters, thus preventing processing from continuing due to anomalies and reducing the production of defective products. Real-time processing data includes equipment processing parameter data (motor speed during drilling, motor speed during milling, feed rate, etc.) and angle steel drawing parameters (processing index influencing factors, such as roughness, hole positioning accuracy, hole size, hole processing accuracy, etc.).

[0056] Furthermore, in the control method of intelligent equipment for multiple angle steel combined production lines, real-time data from the feeding module, conveying module, processing module, and unloading module are integrated into an IoT platform. Real-time processing data information is obtained through the IoT platform, specifically:

[0057] The file size information of real-time data from the feeding module, conveying module, processing module, and unloading module is obtained, and several information transmission paths are constructed. Several information transmission paths are randomly selected for information transmission, and the transmission paths are tested based on the file size information of the real-time data.

[0058] By conducting information transmission tests, real-time information transmission rate information is obtained, and a preset information transmission rate threshold is obtained to determine whether the real-time information transmission rate information is greater than the preset information transmission rate threshold.

[0059] When the real-time information transmission rate is greater than the preset information transmission rate threshold, information is transmitted according to the current information transmission paths.

[0060] When the real-time information transmission rate is not greater than the preset information transmission rate threshold, several information transmission paths are reset to transmit information, the data is integrated into the Internet of Things (IoT) platform, and real-time processing data information is obtained through the IoT platform.

[0061] It should be noted that the information transmission channels include Bluetooth transmission, Wi-Fi transmission, wired transmission, etc. When the real-time information transmission rate is greater than the preset information transmission rate threshold, it means that the data can be quickly transmitted to the IoT platform. When the real-time information transmission rate is not greater than the preset information transmission rate threshold, several information transmission channels are reset for information transmission, the data is integrated into the IoT platform, and real-time processed data information is obtained through the IoT platform, enabling rapid data reading.

[0062] This method may also include:

[0063] Obtain the data size information of the data to be identified generated by the current processing production line, and obtain the real-time data identification capability feature data of the IoT platform, and determine whether the data size information of the data to be identified generated by the current processing production line is greater than the real-time data identification capability feature data of the IoT platform;

[0064] When the size of the data to be identified generated by the current processing production line is greater than the real-time data identification capability feature data of the IoT platform, the data to be identified generated by the current processing production line is configured according to the real-time data identification capability feature data of the IoT platform.

[0065] By configuring the IoT platform, it can identify data with the largest data size and perform data identification according to the order of timestamps.

[0066] When the size of the data to be identified generated by the current processing production line is not greater than the real-time data identification capability feature data of the IoT platform, the quantity of the data to be identified generated by the current processing production line remains unchanged.

[0067] It should be noted that due to their limited data processing capabilities, IoT platforms have limited real-time data recognition capabilities. These capabilities are characterized by features such as the size of data that can be recognized per unit time and the quantity of data that can be recognized within a predetermined time period. This method allows for the configuration of the maximum data size that the IoT platform can recognize based on its data recognition capabilities, ensuring normal platform operation and preventing platform crashes.

[0068] In addition, real-time data identification capability feature data of the IoT platform is obtained, specifically including:

[0069] By acquiring data recognition capability feature data of IoT platform service terminals in various working environments through big data, and building a data recognition capability feature prediction model based on deep neural networks;

[0070] The data identification capability feature data of the IoT platform service terminal under various working environments are input into the data identification capability feature prediction model for training, so as to obtain a data identification capability feature prediction model that meets the expectations.

[0071] Obtain the working environment information of the current IoT platform service terminal's location, and input the working environment information of the current IoT platform service terminal's location into the expected data recognition capability feature prediction model for prediction.

[0072] By predicting, the real-time data identification capability characteristic data of the IoT platform is obtained, and the real-time data identification capability characteristic data of the IoT platform is output.

[0073] It should be noted that the data recognition capability characteristics of IoT platform service terminals are different under different working environments (such as temperature and humidity). This method can further improve the normal operation speed of the IoT platform and prevent IoT platform crashes, thereby improving the control accuracy of the IoT platform in identifying abnormal parameters.

[0074] Furthermore, in the control method of intelligent equipment in multiple angle steel combined production lines, a processing anomaly identification model is constructed based on real-time processing data information, specifically including:

[0075] A processing anomaly identification model is built based on generative adversarial networks. Processing parameter data information in each processing flow is obtained from real-time processing data information, and multi-scale processing parameter features are constructed based on the processing parameter data information in each processing flow.

[0076] A Bayesian network is introduced, and the influencing factors of the processing parameter index are set. The influencing factors of the processing parameter index are used as dependent variables, and the multi-scale processing parameter features are all treated as independent events. The dependent variables and independent events are input into the Bayesian network.

[0077] A multi-parameter optimization objective function is constructed based on independent events. The multi-parameter optimization objective function is then input into a Bayesian network to obtain the potential relationship between the multi-parameter optimization objective function and the dependent variable.

[0078] The potential relationship between the multi-parameter optimization objective function and the dependent variable is input into the processing anomaly identification model for training, thereby obtaining a processing anomaly identification model that meets expectations.

[0079] It should be noted that the factors affecting the processing parameters include cracking, deformation, and dimensional deviations. The multi-parameter optimization objective function satisfies the following relationship:

[0080] ;

[0081] in, The correlation probability value calculated by Bayes takes the value of 0 or 1, where 0 indicates no correlation and 1 indicates correlation. Represents the i-th dependent variable; This represents the correction factor, with a value of 0.1. This represents the real-time data for the i-th processing parameter.

[0082] It should be noted that when When it is 1, it indicates that the current processing parameter data is consistent with... Irrelevant, meaning the current processing parameters will not produce [results]. The data includes indicators of the situation (such as cracks and dimensional deviations). By continuously storing this type of data and statistically analyzing the indicators that occur under various processing parameters, a dataset is constructed. The dataset corresponding to the potential relationship between the multi-parameter optimization objective function and the dependent variable is then input into the processing anomaly identification model for training, thereby obtaining a processing anomaly identification model that meets the expectations.

[0083] Furthermore, in the control method of intelligent equipment in multiple angle steel combined production lines, anomaly identification is performed on the processing parameter data information in each processing flow through a processing anomaly identification model, specifically including:

[0084] Obtain the processing parameter data information in each processing flow, and input the processing parameter data information in each processing flow into the processing anomaly identification model;

[0085] The generator predicts abnormal parameters based on the processing parameter data in the processing flow and obtains the initial parameter identification results.

[0086] The initial parameter recognition result is input into the discriminator for judgment. The discriminator determines whether to accept the initial parameter recognition result. If accepted, the initial parameter recognition result is output.

[0087] If the initial parameter identification result is the same as the preset parameter identification result, the abnormal parameter identification result will be output. If the initial parameter identification result is not the preset parameter identification result, the normal parameter identification result will be output.

[0088] Furthermore, in the control method of intelligent equipment for multiple angle steel production lines, anomaly identification is used to detect and process the angle steel during processing. Specifically:

[0089] By identifying anomalies, abnormal processing parameter data is obtained, and an evaluation index for processing parameter data is set to determine whether the abnormal processing parameter data exceeds the evaluation index.

[0090] When abnormal processing parameter data exceeds the processing parameter data evaluation index, the corresponding angle steel will be scrapped.

[0091] When the abnormal processing parameter data is not greater than the processing parameter data evaluation index, a repairable angle steel is generated, and processing test results are output based on the scrapped angle steel and the repairable angle steel.

[0092] It should be noted that there are two types of anomalies: one is repairable (such as negative dimensional deviation in angle steel), and the other is irreparable (such as cracks). Specifically, when the abnormal processing parameter data exceeds the processing parameter data evaluation index, the corresponding angle steel is scrapped. When the abnormal processing parameter data does not exceed the processing parameter data evaluation index, a repairable angle steel is generated, and processing inspection results are output based on both the scrapped and repairable angle steel.

[0093] Furthermore, in the control method of intelligent equipment for multiple angle steel production lines, the angle steel during processing is marked according to the processing and inspection results, specifically including:

[0094] If the processing and inspection results indicate that the angle steel is to be scrapped, the scrapped angle steel will be marked and then centrally processed.

[0095] The system collects information on the number of angle steels scrapped within a preset time period, sets a threshold for the number of angle steels scrapped, and determines whether the number of angle steels scrapped within the preset time period exceeds the threshold.

[0096] If the number of angle steels to be scrapped within a preset time exceeds the threshold for the number of angle steels to be scrapped, the intelligent equipment of multiple angle steel merging production lines will be controlled to stop working.

[0097] When the number of scrapped angle steels within a preset time is not greater than the threshold number of scrapped angle steels, the intelligent equipment of the multiple angle steel merging production lines will maintain normal operation.

[0098] It should be noted that this method can promptly detect a large number of abnormal angle steels, thereby controlling the operation of intelligent equipment on multiple angle steel merging production lines and improving the rationality of control.

[0099] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods, such as: multiple units or components can be combined, or integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the various components shown or discussed can be through some interfaces, and the indirect coupling or communication connection between devices or units can be electrical, mechanical, or other forms.

[0100] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units. They may be located in one place or distributed across multiple network units. Some or all of the units may be selected to achieve the purpose of this embodiment according to actual needs.

[0101] In addition, in the various embodiments of the present invention, each functional unit can be integrated into one processing unit, or each unit can be a separate unit, or two or more units can be integrated into one unit; the integrated unit can be implemented in hardware or in the form of hardware plus software functional units.

[0102] Those skilled in the art will understand that all or part of the steps of the above method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps of the above method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0103] Alternatively, if the integrated units of this invention are implemented as software functional modules and sold or used as independent products, they can also be stored in a computer-readable storage medium. Based on this understanding, the technical solutions of the embodiments of this invention, or the parts that contribute to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, ROM, RAM, magnetic disks, or optical disks.

[0104] The above are merely specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A control method for intelligent equipment in a combined production line of multiple angle steel lines, characterized in that, Includes the following steps: The real-time data from the feeding module, conveying module, processing module, and unloading module are integrated into the Internet of Things (IoT) platform, and real-time processing data information is obtained through the IoT platform. A processing anomaly identification model is constructed based on the real-time processing data information; The aforementioned processing anomaly identification model is used to identify anomalies in the processing parameter data information of each processing flow. Anomaly identification is used to inspect the angle steel during the processing, and the angle steel is marked according to the inspection results. A processing anomaly identification model is constructed based on the real-time processing data information, specifically including: A processing anomaly identification model is constructed based on generative adversarial networks. Processing parameter data information in each processing flow is obtained from the real-time processing data information. Multi-scale processing parameter features are constructed based on the processing parameter data information in each processing flow. A Bayesian network is introduced, and the influencing factors of the processing parameter index are set. The influencing factors of the processing parameter index are used as dependent variables, and the multi-scale processing parameter features are all used as independent events. The dependent variable and the independent events are input into the Bayesian network. A multi-parameter optimization objective function is constructed based on the independent events. The multi-parameter optimization objective function is then input into a Bayesian network to obtain the potential relationship between the multi-parameter optimization objective function and the dependent variable. The potential relationship between the multi-parameter optimization objective function and the dependent variable is input into the processing anomaly identification model for training, thereby obtaining a processing anomaly identification model that meets the expectations. The intelligent equipment for merging multiple angle steel production lines is characterized by comprising: The feeding module transfers the angle steel to be processed to the conveying module, which then transfers it to the processing module. After processing by the processing module, the unloading module unloads the processed angle steel. The detection module performs processing inspection on the angle steel during the processing and marks the angle steel according to the processing inspection results; The Internet of Things (IoT) platform transmits real-time data from the feeding module, conveying module, processing module, and unloading module to the IoT platform, and identifies anomalies in the real-time data through the IoT platform.

2. The control method for intelligent equipment in a multi-angle steel merging production line according to claim 1, characterized in that, The real-time data from the feeding module, conveying module, processing module, and unloading module are integrated into an IoT platform. Real-time processing data information is obtained through this IoT platform, specifically: The file size information of real-time data from the feeding module, conveying module, processing module, and unloading module is obtained, and several information transmission paths are constructed. Several information transmission paths are randomly selected for information transmission, and the transmission paths are tested based on the file size information of the real-time data. By conducting information transmission tests, real-time information transmission rate information is obtained, and a preset information transmission rate threshold is obtained. It is then determined whether the real-time information transmission rate information is greater than the preset information transmission rate threshold. When the real-time information transmission rate is greater than the preset information transmission rate threshold, information is transmitted according to the current information transmission paths. When the real-time information transmission rate is not greater than the preset information transmission rate threshold, several information transmission paths are reset for information transmission, the data is integrated into the Internet of Things (IoT) platform, and real-time processing data information is obtained through the IoT platform.

3. The control method for intelligent equipment in a multi-angle steel merging production line according to claim 1, characterized in that, The aforementioned processing anomaly identification model identifies anomalies in the processing parameter data information of each processing flow, specifically including: Obtain processing parameter data information for each processing step, and input the processing parameter data information for each processing step into the processing anomaly identification model; The generator predicts abnormal parameters based on the processing parameter data in the processing flow and obtains the initial parameter identification results. The initial parameter identification result is input into the discriminator for judgment. The discriminator determines whether to accept the initial parameter identification result. If accepted, the initial parameter identification result is output. If the initial parameter identification result is the preset parameter identification result, an abnormal parameter identification result is output; if the initial parameter identification result is not the preset parameter identification result, a normal parameter identification result is output.

4. The control method for intelligent equipment in a multi-angle steel merging production line according to claim 1, characterized in that, Anomaly identification is used to inspect the angle steel during the processing, specifically: By identifying anomalies, abnormal processing parameter data information is obtained, and a processing parameter data evaluation index is set to determine whether the abnormal processing parameter data information is greater than the processing parameter data evaluation index. When the abnormal processing parameter data exceeds the processing parameter data evaluation index, the corresponding angle steel will be scrapped. When the abnormal processing parameter data is not greater than the processing parameter data evaluation index, a repairable angle steel is generated, and processing test results are output based on the scrapped angle steel and the repairable angle steel.

5. The control method for intelligent equipment in a multi-angle steel merging production line according to claim 1, characterized in that, The angle steel is marked according to the processing and inspection results, specifically including: If the processing and inspection results indicate that the angle steel is to be scrapped, then the scrapped angle steel will be marked and centrally processed. The number of angle steels scrapped within a preset time period is statistically analyzed, and a threshold for the number of angle steels scrapped is set. The result is then determined whether the number of angle steels scrapped within the preset time period is greater than the threshold for the number of angle steels scrapped. If the number of scrapped angle steels within the preset time exceeds the threshold number of scrapped angle steels, the intelligent equipment of the multiple angle steel merging production lines will be controlled to stop working. When the number of scrapped angle steels within the preset time is not greater than the threshold number of scrapped angle steels, the intelligent equipment of the multiple angle steel merging production lines will maintain normal operation.

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