A crane intelligent magnetic separation method, device and system

Abstract

The application belongs to the technical field of crane application, and specifically provides a crane intelligent magnetic separation method, device and system. The method comprises the following steps: collecting relevant information of a steel plate and control information of a crane PLC; storing the collected information after processing; matching the processed data with control model library data; if the matching is successful, outputting an instruction to the crane PLC control system according to the optimal magnetic force of the matched steel plate in the control model library and the working number of electromagnets to complete the feeding and discharging and position moving of the steel plate; if the matching fails, analyzing the correlation of different data types of the collected data, obtaining a mathematical model of the steel plate specification and the electromagnetic force coupling of electromagnets through iterative evolution; and storing the optimal magnetic force of the steel plate and the working number of electromagnets obtained through a simulation experiment into the control model library. The modelized magnetic force control realizes the suction and release of multi-specification thin steel plates in multiple working modes, and solves the problem of restricting the improvement of production efficiency.

Description

Technical Field

[0001] This invention relates to the field of crane application technology, specifically to a crane intelligent magnetic separation method, device, and system. Background Technology

[0002] With the booming development of the steel industry, the demand for cranes has been increasing, promoting innovation and progress in crane technology. However, due to the steel industry's increasingly higher requirements for the efficiency of steel transportation and the continuous upgrading of hoisting needs, people's requirements for hoisting automation, intelligence, and precision are also constantly increasing, making the improvement of various performance indicators a difficult problem to be solved.

[0003] In recent years, with the increasing demand for steel plates, the existing electromagnetic control systems for cranes can no longer meet production needs. The main problems are as follows:

[0004] 1. Long adjustment time for lifting electromagnets

[0005] Currently, electromagnetic cranes have limited magnetic force selection for steel plate attraction and release operations, typically offering only five settings: 20%-40%-60%-80%-100%, to accommodate steel plates of varying thicknesses. Due to the limited magnetic force adjustment, electromagnets can easily attract and lift 2-3 plates during the lifting of thinner plates. Furthermore, since loading and unloading are remotely controlled, operators cannot promptly detect multiple attracted plates, leading to issues such as discrepancies between the actual plate number and the actual plate being lifted, and sometimes even the safety hazard of plates suddenly falling. If operators do detect the problem in time, they can adjust the crane's magnetic force, the gap between the electromagnet and the steel plate, and repeatedly adjust the attraction and release of the plates through demagnetization, enhancement, and repositioning until successful lifting is achieved. This significantly impacts the crane's steel plate handling speed, reduces production pace, increases labor intensity, and generates substantial noise pollution.

[0006] 2. Monotonous work mode

[0007] Depending on the actual needs on site and the requirement for high production efficiency, it is sometimes necessary to simultaneously attract multiple steel plates and then release them separately. This requires linear or precise adjustment of the crane's electromagnet settings to meet production demands. Summary of the Invention

[0008] To address the issues of long adjustment time and limited operating modes of lifting electromagnets, this invention provides an intelligent magnetic separation method, device, and system for cranes. This method utilizes big data technology and other auxiliary equipment to construct a mathematical model coupling steel plate specifications with magnetic force, enabling precise separation and lifting of steel plates of different sizes and thicknesses, thereby improving the operating efficiency of lifting equipment.

[0009] In a first aspect, the present invention provides an intelligent magnetic separation method for cranes, comprising the following steps:

[0010] Collect relevant information about the steel plate and control information from the crane's PLC;

[0011] The collected information is processed and then stored;

[0012] The processed data is then matched with data from the control model library.

[0013] If the match is successful, the system outputs and sends instructions to the crane PLC control system based on the optimal magnetic force and electromagnet working quantity of the steel plate matched in the control model library to complete the loading, unloading and position movement of the steel plate.

[0014] If the matching fails, the correlation between different types of data collected is analyzed, and a mathematical model of the coupling between steel plate specifications and electromagnet magnetic force is obtained through iterative evolution.

[0015] The optimal magnetic force of the steel plate and the number of electromagnets working were obtained through simulation experiments and stored in the control model library.

[0016] As a further limitation of the technical solution of the present invention, the steps of collecting relevant information of the steel plate and control information of the crane PLC include:

[0017] Obtain the tail marking information of the steel plate;

[0018] When the crane starts working, it scans and acquires information about the steel plate's motion environment, including information about the steel plate.

[0019] Collect process information on steel plates and control information from the crane's PLC.

[0020] As a further limitation of the technical solution of the present invention, the steps of outputting and issuing instructions to the crane PLC control system to complete the loading, unloading and position movement of the steel plate according to the optimal magnetic force of the steel plate matched in the control model library and the number of electromagnets include:

[0021] Determine whether the information of the steel plate to be lifted is consistent with the information obtained from the tail markings of the steel plate;

[0022] If the information is consistent, the system outputs and sends instructions to the crane PLC control system based on the optimal magnetic force of the steel plate and the number of electromagnets working according to the matching steel plate in the control model library to complete the loading, unloading and position movement of the steel plate.

[0023] If the information is inconsistent, the error message will be sent to the management system and pushed to the operator for manual confirmation.

[0024] As a further limitation of the technical solution of the present invention, when the crane starts working, the step of scanning and acquiring the steel plate movement environment information, including steel plate information, includes:

[0025] Scan the 2D outline information of all objects during the crane's movement and the 3D data model of the entire site, and upload the scanned information;

[0026] Upon receiving the uploaded data, the system processes the data to obtain the exact location of the plate and the centerline position of the steel plate's length.

[0027] As a further limitation of the technical solution of the present invention, the step of outputting and issuing instructions to the crane PLC control system based on the optimal magnetic force of the steel plate and the number of electromagnets working according to the control model library includes:

[0028] Based on the optimal magnetic force of the steel plate and the number of electromagnets working according to the matching in the control model library, the output command is sent to the crane PLC control system.

[0029] When the centerline of the crane's electromagnetic field coincides with the centerline of the steel plate's length, the crane is controlled to stop, and the electromagnetic field is controlled to execute the lifting and transport instructions for the steel plate based on the received control instructions for the optimal magnetic force of the steel plate and the number of working electromagnets.

[0030] As a further limitation of the technical solution of this invention, the steps of analyzing the correlation between different types of collected data and obtaining a mathematical model of the coupling between steel plate specifications and electromagnet magnetic force through iterative evolution include:

[0031] The consistency of electromagnet performance and the flatness of its bottom plane are inspected.

[0032] Mechanical analysis was conducted on the adsorption of thin and thick plates by electromagnets with different distributions. Conventional electromagnets were used to conduct adsorption and release experiments on the plates. The relationship between the deformation of the plates and the distribution of electromagnets was found through the experiments, and an electromagnet distribution model was established for lifting steel plates of different specifications.

[0033] As a further limitation of the technical solution of the present invention, before the step of analyzing the correlation of different types of collected data and obtaining a mathematical model of the coupling between steel plate specifications and electromagnet magnetic force through iterative evolution, the following steps are included:

[0034] A precise electromagnet current control and power supply control system is established based on the operating principle of crane electromagnets; specifically including:

[0035] The characteristics of current and voltage of different steel plates under different working modes are analyzed. By analyzing the fixed magnet mode, strong magnet mode, magnet adjustment mode and sheet splitting working mode of the electromagnet of the stacking machine and the corresponding working process, theoretical analysis is completed using the inverter principle, PI control system, rectification principle and positive logic non-circulating current control subsystem to determine the precise current control and power supply control system.

[0036] Secondly, the present invention provides an intelligent magnetic separation device for cranes, including a data acquisition module, a processing module, an output module, a mathematical model creation module, and an optimal value acquisition module;

[0037] The data acquisition module is used to collect relevant information about the steel plate and control information from the crane PLC.

[0038] The processing module is used to process and store the collected information; to match the processed data with the control model library data; the device also includes a storage module, which processes the collected information and then inputs it into the storage module for storage.

[0039] The output module is used to output instructions to the crane PLC control system to complete the loading, unloading and position movement of the steel plate if the matching is successful, based on the optimal magnetic force of the steel plate and the number of electromagnets working according to the matching steel plate in the control model library.

[0040] The mathematical model creation module is used to analyze the correlation of different types of data collected if the matching fails, and to obtain a mathematical model of the coupling between steel plate specifications and electromagnet magnetic force through iterative evolution.

[0041] The optimal value acquisition module is used to obtain the optimal magnetic force of the steel plate and the number of electromagnets working through simulation experiments and store them in the control model library.

[0042] Thirdly, the technical solution of the present invention also provides an intelligent magnetic separation system for cranes, including a server, a crane PLC control system, and a data acquisition module;

[0043] The crane's PLC control system and data acquisition module are connected to the server respectively;

[0044] The data acquisition module is used to read the tail marking information of the steel plate and upload it to the server; scan the 2D outline information of all objects during the crane's movement and the 3D data model of the entire site, and upload the scanned information to the server; it is also used to collect the process information of the steel plate and the control information of the crane PLC and upload it to the server.

[0045] The server analyzes the correlation of different types of collected data, and obtains a mathematical model of the coupling between steel plate specifications and electromagnet magnetic force through iterative evolution. The optimal magnetic force of the steel plate and the number of working electromagnets are obtained through simulation experiments and stored in the control model library. When the server receives the information uploaded by the acquisition module, it compares and calculates with the data in the control model library, provides the current control system for lifting the steel plate, and sends instructions to the crane PLC control system to complete the loading, unloading and position movement of the steel plate.

[0046] As a further limitation of the technical solution of the present invention, the acquisition module includes a machine vision device and a laser contour scanning sensor installed at the position of the crane beam.

[0047] The machine vision device and the laser contour scanning sensor are respectively connected to the server;

[0048] A machine vision device is used to read the marking information at the tail of the steel plate and upload it to the server;

[0049] The laser contour scanning sensor is used to scan the 2D contour information of all objects during the crane's movement, as well as the 3D data model of the entire site, and upload the scanned information to the server.

[0050] As a further limitation of the technical solution of the present invention, after the server receives the information uploaded by the acquisition module, it compares and calculates with the data in the control model library to provide the current control system for lifting the steel plate, and sends instructions to the crane PLC control system to complete the loading, unloading and position movement of the steel plate, the steps include:

[0051] The received scanned information is processed and analyzed to obtain the exact location of the plate and the centerline of the steel plate. Simultaneously, the steel plate marking information scanned by the machine vision device is compared with the length, width, weight, and coordinate position information of the steel plate to be lifted, as instructed by the server. If the information matches, the information uploaded by the acquisition module is compared and calculated with the data in the control model library to determine the current steel plate lifting control system. Instructions are then sent to the crane's PLC control system to control the crane to move slowly. When the centerline of the crane's electromagnetic field coincides with the centerline of the steel plate, the crane stops, and the electromagnet executes the lifting and transport instruction for the steel plate. If the information is inconsistent, a prompt message is sent to the operator for manual confirmation.

[0052] As can be seen from the above technical solutions, the present invention has the following advantages:

[0053] 1. This invention constructs a mathematical model of the specifications of magnetic steel plates and magnetic coupling, which can achieve personalized control in different scenarios through simple adjustments.

[0054] 2. Compared with traditional cranes, this invention introduces data algorithms to establish a classification model corresponding to the magnetic force of steel plates, and integrates information technologies such as the Internet of Things and data processing with steel plate process information and electromagnet control information, which is an integrated application of information technology.

[0055] 3. Develop an intelligent electromagnet magnetic separation system to make the application of electromagnetism more scientific and precise. Through model-based magnetic force control, it can achieve rapid and stable attraction and release of thin steel plates of various specifications in multiple working modes, solving the existing problems that restrict the improvement of production efficiency.

[0056] Furthermore, the design principle of this invention is reliable, the structure is simple, and it has a very wide range of application prospects.

[0057] Therefore, it is evident that the present invention has outstanding substantive features and significant progress compared with the prior art, and the beneficial effects of its implementation are also obvious. Attached Figure Description

[0058] 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, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0059] Figure 1 This is a schematic flowchart of a method according to an embodiment of the present invention.

[0060] Figure 2 This is a schematic block diagram of an apparatus according to an embodiment of the present invention.

[0061] Figure 3 This is a schematic diagram of the installation of a machine vision device and a laser contour scanning sensor.

[0062] Figure 4 The system provided in this embodiment of the invention is a schematic diagram of an embodiment of a crane intelligent magnetic separation method. Detailed Implementation

[0063] To enable those skilled in the art to better understand the technical solutions of this invention, the technical solutions of the embodiments of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of this invention.

[0064] Figure 1 This is a schematic flowchart of an intelligent magnetic separation method for cranes provided by an embodiment of the present invention. The method includes the following steps:

[0065] Step 1: Collect relevant information about the steel plate and control information from the crane PLC;

[0066] Specifically, it includes:

[0067] Step 11: Obtain the tail marking information of the steel plate;

[0068] Step 12: When the crane starts working, scan and acquire the motion environment information of the steel plate, including the information of the steel plate; scan out the 2D contour information of all objects in the crane's movement process and the 3D data model of the entire site;

[0069] Step 13: Collect the process information of the steel plate and the control information of the crane PLC; specifically, this includes the process information of the steel plate number, steel type, steel plate size, weight, etc. of the primary and secondary steel plate systems in the production system, as well as the control information of the crane PLC such as current and voltage.

[0070] Step 2: Process and store the collected information; convert the above data into a unified communication standard using the OPCUA protocol and save it to the server database;

[0071] Step 3: Match the processed data with the data in the control model library;

[0072] If the match is successful, proceed to step 6; if the match fails, proceed to step 4.

[0073] Step 4: Analyze the correlation of different types of data collected, and obtain a mathematical model of steel plate specifications and electromagnet magnetic coupling through iterative evolution; the mathematical model includes checking the consistency of electromagnet performance and the flatness of the bottom plane; conduct mechanical analysis when electromagnets with different distributions adsorb thin and thick plates, use conventional electromagnets to conduct plate adsorption and release experiments, and find the law between plate deformation and electromagnet distribution through experiments.

[0074] Step 5: Obtain the optimal magnetic force of the steel plate and the number of electromagnets working through simulation experiments and store them in the control model library;

[0075] Step 6: Based on the optimal magnetic force and electromagnet working quantity of the steel plate matched in the control model library, output and send instructions to the crane PLC control system to complete the loading, unloading, and position movement of the steel plate; specific steps include:

[0076] The received scanned information is processed and analyzed to obtain the exact location of the plate and the centerline of the steel plate. Simultaneously, the steel plate marking information scanned by the machine vision device is compared with the length, width, weight, and coordinate position information of the steel plate to be lifted, as instructed by the server. If the information matches, the information uploaded by the acquisition module is compared and calculated with the data in the control model library to determine the current steel plate lifting control system. Instructions are then sent to the crane's PLC control system to control the crane to move slowly. When the centerline of the crane's electromagnetic field coincides with the centerline of the steel plate, the crane stops, and the electromagnet executes the lifting and transport instruction for the steel plate. If the information is inconsistent, a prompt message is sent to the operator for manual confirmation.

[0077] It should be noted that when the crane starts working, the steps for scanning and acquiring the steel plate's motion environment information, including steel plate information, include:

[0078] Scan the 2D outline information of all objects during the crane's movement and the 3D data model of the entire site, and upload the scanned information;

[0079] Upon receiving the uploaded data, the system processes the data to obtain the exact location of the plate and the centerline position of the steel plate's length.

[0080] Accordingly, the steps for issuing commands to the crane PLC control system based on the optimal magnetic force of the steel plate and the number of electromagnets working according to the matching control model library include:

[0081] Based on the optimal magnetic force of the steel plate and the number of electromagnets working according to the matching in the control model library, the output command is sent to the crane PLC control system.

[0082] When the centerline of the crane's electromagnetic field coincides with the centerline of the steel plate's length, the crane is controlled to stop, and the electromagnetic field is controlled to execute the lifting and transport instructions for the steel plate based on the received control instructions for the optimal magnetic force of the steel plate and the number of working electromagnets.

[0083] Before analyzing the correlation between different types of collected data and obtaining a mathematical model of the coupling between steel plate specifications and electromagnets through iterative evolution, the following steps are included:

[0084] A precise electromagnet current control and power supply control system is established based on the operating principle of crane electromagnets; specifically including:

[0085] The characteristics of current and voltage of different steel plates under different working modes are analyzed. By analyzing the fixed magnet mode, strong magnet mode, magnet adjustment mode and sheet splitting working mode of the electromagnet of the stacking machine and the corresponding working process, theoretical analysis is completed using the inverter principle, PI control system, rectification principle and positive logic non-circulating current control subsystem to determine the precise current control and power supply control system.

[0086] like Figure 2 As shown, this embodiment of the invention provides an intelligent magnetic separation device for cranes, including a data acquisition module, a processing module, an output module, a mathematical model creation module, and an optimal value acquisition module;

[0087] The data acquisition module is used to collect relevant information about the steel plate and control information from the crane PLC.

[0088] The processing module is used to process and store the collected information; and to match the processed data with the control model library data.

[0089] The output module is used to output instructions to the crane PLC control system to complete the loading, unloading and position movement of the steel plate if the matching is successful, based on the optimal magnetic force of the steel plate and the number of electromagnets working according to the matching steel plate in the control model library.

[0090] The mathematical model creation module is used to analyze the correlation of different types of data collected if the matching fails, and to obtain a mathematical model of the coupling between steel plate specifications and electromagnet magnetic force through iterative evolution.

[0091] The optimal value acquisition module is used to obtain the optimal magnetic force of the steel plate and the number of electromagnets working through simulation experiments and store them in the control model library.

[0092] The acquisition module is used to acquire data from machine vision devices, laser contour scanning data, steel plate process data, and control data such as current and voltage from crane PLCs, and converts the above data into a unified communication standard through the OPCUA protocol and saves it to the server database.

[0093] The device also includes a storage module for storing machine vision device data, laser contour scanning data, steel plate process data, and crane PLC control data such as current and voltage after cleaning, so that they can be retrieved at any time.

[0094] The processing module is used to analyze the data correlations of different data types;

[0095] The mathematical model creation module uses a hybrid simulation-genetic algorithm to optimize and solve the model, and obtains the mathematical model of steel plate specifications and electromagnet magnetic coupling through iterative evolution;

[0096] The output module presents the crane's real-time dynamic HMI interactive screen to the user based on the B / S model, specifically including functions such as historical query, historical analysis, alarm information push, manual intervention, steel plate information, and work order issuance.

[0097] During the development of the aforementioned functional modules, a virtual control platform was established using Qt Creator to simulate the interface, control buttons, and control programs of actual application projects. The main electrical wiring was built using Simulink software. S-functions were used to coordinate the control of crane data, machine vision device data, laser contour scanning data, and steel plate process data, enabling joint simulation. Furthermore, C and C++ languages ​​were used for software design and programming, and the simulation control platform's program was ported to the software system for joint debugging.

[0098] This invention also provides an intelligent magnetic separation system for cranes, including a server, a crane PLC control system, and a data acquisition module;

[0099] The crane's PLC control system and data acquisition module are connected to the server respectively;

[0100] The data acquisition module is used to read the tail marking information of the steel plate and upload it to the server; scan the 2D outline information of all objects during the crane's movement and the 3D data model of the entire site, and upload the scanned information to the server; it is also used to collect the process information of the steel plate and the control information of the crane PLC and upload it to the server.

[0101] The server analyzes the correlation of different types of collected data, and obtains a mathematical model of the coupling between steel plate specifications and electromagnet magnetic force through iterative evolution. The optimal magnetic force of the steel plate and the number of working electromagnets are obtained through simulation experiments and stored in the control model library. When the server receives the information uploaded by the acquisition module, it compares and calculates with the data in the control model library, provides the current control system for lifting the steel plate, and sends instructions to the crane PLC control system to complete the loading, unloading and position movement of the steel plate.

[0102] The acquisition module includes a machine vision device and a laser contour scanning sensor installed at the location of the crane beam;

[0103] The machine vision device and the laser contour scanning sensor are respectively connected to the server;

[0104] A machine vision device is used to read the marking information at the tail of the steel plate and upload it to the server;

[0105] The laser contour scanning sensor is used to scan the 2D contour information of all objects during the crane's movement, as well as the 3D data model of the entire site, and upload the scanned information to the server.

[0106] After the server receives the information uploaded by the acquisition module, it compares and calculates the data with the control model library to provide the current control system for lifting the steel plate. The steps for completing the loading, unloading, and position movement of the steel plate include:

[0107] The received scanned information is processed and analyzed to obtain the exact location of the plate and the centerline of the steel plate. Simultaneously, the steel plate marking information scanned by the machine vision device is compared with the length, width, weight, and coordinate position information of the steel plate to be lifted, as instructed by the server. If the information matches, the information uploaded by the acquisition module is compared and calculated with the data in the control model library to determine the current steel plate lifting control system. Instructions are then sent to the crane's PLC control system to control the crane to move slowly. When the centerline of the crane's electromagnetic field coincides with the centerline of the steel plate, the crane stops, and the electromagnet executes the lifting and transport instruction for the steel plate. If the information is inconsistent, a prompt message is sent to the operator for manual confirmation.

[0108] The present invention provides a method for implementing intelligent magnetic separation in cranes, comprising the following steps:

[0109] S101. Install a machine vision device at the position of the crane beam;

[0110] S102. Install a laser contour scanning sensor at the position of the crane beam;

[0111] S103. Collect steel plate material information and crane weighing information from the production system based on Internet of Things (IoT) communication technology;

[0112] S104. Establish a precise electromagnet current control and power supply control system based on the operating principle of crane electromagnets.

[0113] S105. Set the above data as a unified communication conversion standard and integrate it into the database;

[0114] S106. Establish a mathematical model of the number of electromagnets in operation, the magnitude of magnetic force, and the steady-state lifting of steel plates of different lengths, widths, and thicknesses by combining experimental methods and big data analysis technology.

[0115] S107. Install a PC server on the crane's central control console.

[0116] This invention uses an electromagnetic crane used in a heat treatment workshop for 4300mm thick plates as an example to illustrate a method for intelligent magnetic separation in cranes. Figure 3 As shown, the specific method is as follows:

[0117] In step S201, as Figure 4 As shown, the machine vision device 21 is a commercially available device with supplementary lighting. Its response time is less than 40ms. The device reads the marking information at the tail of the steel plate and uploads it to the PC server.

[0118] In step S202, as Figure 4 As shown, the laser contour scanning sensor 22 is installed at the crane beam position, with a response time of less than 50ms and a measurement accuracy of ±15mm. The laser contour scanning sensor scans the 2D contour information of all objects during the crane's movement and the 3D data model of the entire site. This information is uploaded to the PC server, where dedicated software processes and analyzes the data to obtain the exact position of the plate and the centerline position of the steel plate's length. Then, the steel plate marking information scanned by the machine vision device is compared with the length, width, weight, and coordinate position information of the steel plate to be lifted issued by the PC server. If the information matches, the PC server calculates the specific position the crane should move to and sends it to the crane's PLC system, controlling the crane to move slowly. When the centerline of the crane's electromagnetic field coincides with the centerline of the steel plate's length, the crane stops and controls the electromagnet to execute the lifting and transport command for the steel plate. If the information does not match, the error information is fed back to the PC server and a new command is issued, or it is pushed to the operator for manual confirmation.

[0119] In step S203, the collected information includes primary and secondary system information of the steel plate production system, as well as control information such as current and voltage from the crane PLC. The primary and secondary system information of the steel plate includes key production information such as steel type, length, width, thickness, weight, and date, and other information, ranging from 5000 to 14000*1600 to 4000*5 to 100mm. The crane control information includes control information for transformers, rectifiers, contactors, and other components.

[0120] In step S204, the control system includes analyzing the current and voltage characteristics of different steel plates under different operating modes. This is achieved through analysis of the electromagnet's constant magnetization mode, strong magnetization mode, adjustable magnetization mode, and sheet splitting mode, as well as their operational processes. Based on real-time sensor sampling values, theoretical analysis is performed using inverter principles, PI control systems, rectification principles, and a positive logic non-circulating current control subsystem to determine precise current control and a power supply control system. Preliminary simulations of the control system are conducted using Simulink to identify problems and improve the control system, verifying the correctness of the fundamental analysis.

[0121] In step S205, the machine vision device data, laser contour scanning data, steel plate primary and secondary information in the production system, and control information such as current and voltage of the crane PLC belong to different systems. They need to be converted into data in a unified format and stored on the server to facilitate data correlation analysis. The data exchange is carried out through Profibus-DP cable.

[0122] In step S206, the mathematical model includes checking the consistency of electromagnet performance and the flatness of the bottom plane; conducting mechanical analysis when electromagnets with different distributions adsorb thin and thick plates; using conventional electromagnets to conduct plate adsorption and release experiments; finding the relationship between plate deformation and electromagnet distribution through experiments; and establishing an electromagnet distribution model for lifting steel plates of different specifications.

[0123] In step S207, the server integrates and compares the data from steps 201-207, constructs a mathematical model of steel plate specifications and electromagnet magnetic coupling using big data technology, and ultimately obtains a control model library of optimal magnetic force and electromagnet working quantity for steel plates of different specifications. Specifically, after receiving data from the machine vision device, laser contour scanning data, primary and secondary information of the steel plates in the production system, and control information such as current and voltage from the crane PLC, the server compares and calculates this data with the control model library data, provides the current steel plate lifting control system, and issues instructions to the crane PLC control system to complete the loading, unloading, and position movement of the steel plates.

[0124] Although the present invention has been described in detail with reference to the accompanying drawings and preferred embodiments, the invention is not limited thereto. Various equivalent modifications or substitutions can be made to the embodiments of the invention by those skilled in the art without departing from the spirit and essence of the invention, and such modifications or substitutions should all be within the scope of the invention. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the invention should also be covered within the protection scope of the invention. Therefore, the protection scope of the invention should be determined by the scope of the claims.

Claims

1. A method for intelligent sub-magnetic division of a crane, characterized by, It comprises the following steps: Collecting relevant information of the steel plate and control information of the crane PLC; Storing the collected information after processing; Matching the processed data with the control model library data; If the matching is successful, outputting the instruction to the crane PLC control system according to the optimal magnetic force and the number of electromagnetic iron working of the matched steel plate in the control model library to complete the feeding and discharging and position moving of the steel plate; If the matching fails, analyzing the correlation of different data types of the collected data, and obtaining the mathematical model of the steel plate specification and electromagnetic iron magnetic force coupling through iterative evolution; specifically including: testing the consistency of electromagnetic iron performance and the flatness of the bottom plane; analyzing the mechanics of different distribution arrangements of electromagnetic iron adsorbing thin plates and thick plates, conducting plate adsorption and release experiments with conventional electromagnetic iron, finding out the law between plate deformation and electromagnetic iron distribution through experiments, and establishing an electromagnetic iron distribution model under different specifications of steel plates; Obtaining the optimal magnetic force and the number of electromagnetic iron working of the steel plate through simulation experiments and storing them in the control model library.

2. The crane intelligent partial magnetization method according to claim 1, characterized in that, The step of collecting relevant information of the steel plate and control information of the crane PLC comprises: Obtaining the identification information of the tail of the steel plate; When the crane starts working, scanning and obtaining the steel plate movement environment information including the steel plate information; Collecting process information of the steel plate and control information of the crane PLC.

3. The crane intelligent partial magnetization method according to claim 2, characterized in that, The step of outputting the instruction to the crane PLC control system according to the optimal magnetic force and the number of electromagnetic iron working of the matched steel plate in the control model library to complete the feeding and discharging and position moving of the steel plate comprises: Judging whether the information of the steel plate to be lifted is consistent with the obtained identification information of the tail of the steel plate; If the information is consistent, outputting the instruction to the crane PLC control system according to the optimal magnetic force and the number of electromagnetic iron working of the matched steel plate in the control model library to complete the feeding and discharging and position moving of the steel plate; If the information is inconsistent, feeding back the error information to the management end and pushing it to the operator for manual confirmation.

4. The crane intelligent partial magnetization method according to claim 3, characterized in that, The step of scanning and obtaining the steel plate movement environment information including the steel plate information when the crane starts working comprises: Scanning the 2D contour information of all objects in the crane walking process and the 3D data model of the entire site and uploading the scanning information; After receiving the uploaded data, processing the received data to obtain the exact position of the plate and the centerline position of the steel plate length.

5. The crane intelligent partial magnetization method according to claim 4, characterized in that, The step of outputting the instruction to the crane PLC control system according to the optimal magnetic force and the number of electromagnetic iron working of the matched steel plate in the control model library comprises: Outputting the instruction to the crane PLC control system according to the optimal magnetic force and the number of electromagnetic iron working of the matched steel plate in the control model library; When the centerline of the electromagnetic iron of the crane coincides with the centerline of the steel plate length, controlling the crane to stop and controlling the electromagnetic iron to execute the lifting and transportation instruction of the steel plate according to the received control instruction of the optimal magnetic force and the number of electromagnetic iron working of the steel plate.

6. The intelligent partial magnetization method of a crane according to claim 5, characterized in that, The step of analyzing the correlation of different data types of the collected data and obtaining the mathematical model of the steel plate specification and electromagnetic iron magnetic force coupling through iterative evolution comprises: Establishing an electromagnetic iron current precise control and power control system based on the operating principle of the crane electromagnetic iron; specifically including: The current-voltage characteristics of different steel plates in working modes are analyzed, and through the analysis of the permanent magnet mode, the strong magnet mode, the magnetic adjustment mode and the separate plate working mode of the electromagnetic iron of the piling plate machine and the corresponding working process, the theoretical analysis is completed by using the inverter principle, the PI control system, the rectifier principle and the positive logic currentless control subsystem to determine the accurate current control and the power control system.

7. A crane intelligence sub-magnetic device, characterized in that, The system comprises a server, a crane PLC control system and an acquisition module. The acquisition module is used for collecting relevant information of the steel plate and control information of the crane PLC. The processing module is used for storing the collected information after processing. The output module is used for outputting instructions to the crane PLC control system to complete the feeding and discharging and position moving of the steel plate if the matching is successful. The mathematical model creation module is used for analyzing the correlation of different data types, obtaining the mathematical model of the steel plate specification and the electromagnetic iron magnetic force coupling through iterative evolution, and testing the performance consistency and the flatness of the bottom plane of the electromagnetic iron. The optimal value acquisition module is used for obtaining the optimal magnetic force of the steel plate and the working number of the electromagnetic iron through simulation experiments and storing them in the control model library.

8. A crane intelligence sub-magnetic system, characterized in that, The system comprises a server, a crane PLC control system and an acquisition module. The crane PLC control system and the acquisition module are connected with the server. The acquisition module is used for reading the identification information of the tail of the steel plate and uploading it to the server. The acquisition module is also used for collecting process information of the steel plate and control information of the crane PLC and uploading them to the server.

9. The intelligent crane segmenting system of claim 8, wherein, The server is used for analyzing the correlation of different data types, obtaining the mathematical model of the steel plate specification and the electromagnetic iron magnetic force coupling through iterative evolution, testing the performance consistency and the flatness of the bottom plane of the electromagnetic iron, performing mechanical analysis of the electromagnetic iron in different distribution arrangements for absorbing thin plates and thick plates, performing plate absorption and release experiments with conventional electromagnetic iron, finding out the law between the plate deformation and the electromagnetic iron distribution, establishing the electromagnetic iron distribution model under different specifications of the steel plate, obtaining the optimal magnetic force of the steel plate and the working number of the electromagnetic iron through simulation experiments and storing them in the control model library, and giving the current control system of the steel plate lifting when the server receives the information uploaded by the acquisition module and compares and calculates the data with the control model library data. The acquisition module comprises a machine vision device and a laser profile scanning sensor installed at the position of the crane girder. The machine vision device and the laser profile scanning sensor are connected with the server. Machine vision device for reading the tail identification information of the steel plate and uploading it to the server; Laser profile scanning sensor for scanning the 2D profile information of all objects in the walking process of the crane and the 3D data model of the entire site, and uploading the scanned information to the server.

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