A core silicon steel sheet shearing task allocation method and system

Abstract

This application belongs to the field of transformer manufacturing technology and discloses a method and system for allocating shearing tasks for core silicon steel sheets. The method includes: acquiring real-time operating status parameters of multiple shearing units; acquiring local physical characteristic parameters of the silicon steel sheets to be processed; acquiring quality constraint indicators of the currently allocated shearing tasks and quantifying them into task strictness scores; performing multi-dimensional correlation analysis based on the real-time operating status parameters, the local physical characteristic parameters, and the task strictness scores to generate potential processing risk assessment values ​​for the silicon steel sheets to be processed and the currently allocated shearing tasks for each shearing unit; and executing task allocation decisions based on the potential processing risk assessment values ​​corresponding to each shearing unit. This achieves precise matching between tasks and unit capabilities, significantly improving shearing quality and production efficiency.

Description

Technical Field

[0001] This application relates to the field of transformer manufacturing technology, and more specifically, to a method and system for allocating tasks for shearing silicon steel sheets in a core. Background Technology

[0002] In modern industrial production, especially in the manufacturing of core components such as transformers or motor cores, the precise shearing of silicon steel sheets is a crucial process. Factories typically deploy multiple shearing units to handle various shearing tasks. To ensure high efficiency, high-quality products, and optimal resource utilization, an intelligent task allocation system is essential. This system aims to allocate shearing tasks based on the capacity of each unit and the specific requirements of each order. However, dynamic changes in equipment performance and raw material characteristics often pose challenges to traditional static task allocation methods, making them difficult to effectively address.

[0003] In existing technologies, production scheduling centers typically formulate detailed shearing plans based on received production orders, allocating shearing tasks for silicon steel sheets of different specifications, grades, and sizes to multiple shearing units within the workshop. The allocation is primarily based on the nominal technical parameters of each unit, such as rated shearing speed, processing accuracy level, maximum supported material width and thickness, etc., combined with order priority and delivery date, to generate theoretically optimal work orders through a planning system. For example, for orders with extremely high precision requirements, the system will prioritize assigning them to the unit with the highest nominal precision; for large-volume, ordinary orders, they will be assigned to the unit with the fastest rated speed, aiming to maximize overall production efficiency.

[0004] However, this allocation method based on static, idealized parameters has significant problems in actual continuous production processes. Equipment conditions are not static; for example, shearing machine blades wear down with use, and transmission systems may develop gaps, causing changes in actual operating accuracy and cut quality (such as burr height). Traditional allocation systems are unaware of these subtle, dynamic changes in equipment "health," treating machines with varying performance as identical resources. This information lag and bias directly leads to resource misallocation. High-precision tasks may be assigned to degraded machines, resulting in defective products and wasted raw materials. Conversely, machines at their peak performance may be assigned to routine tasks with low precision requirements, wasting their superior processing capabilities.

[0005] Furthermore, even if the silicon steel sheet coils delivered from upstream of the production line pass the incoming inspection, their microscopic physical properties may exhibit subtle local fluctuations, such as slight variations in material hardness and thickness at different locations within the same coil. When such coils are assigned to units in poor condition, encountering areas of material with higher hardness can cause the worn cutting edges to chip instantly, leading to a sharp decline in the quality of subsequent products and even unplanned equipment shutdowns. However, assigning this task to units in good condition can better handle material fluctuations. Therefore, existing task allocation methods based on static parameters cannot comprehensively consider the real-time performance status of the shearing unit and the local characteristics of the material to be processed, resulting in a significant reduction in the overall controllability and economic efficiency of the production process.

[0006] To address the aforementioned issues, existing technologies urgently need improvement. Summary of the Invention

[0007] The purpose of this application is to provide a method and system for allocating shearing tasks for iron-core silicon steel sheets, which aims to solve the problem that the existing task allocation method based on static parameters cannot take into account the real-time performance status of the shearing unit and the local characteristics of the material to be processed in a real-time and comprehensive manner, resulting in a significant reduction in the overall controllability and economic benefits of the production process.

[0008] In a first aspect, this application provides a method for assigning shearing tasks of iron-core silicon steel sheets, the method comprising the following steps: A1. Obtain real-time operating status parameters of multiple shearing units; the real-time operating status parameters include at least load characteristic data reflecting the degree of tool wear, and precision characteristic data reflecting the motion deviation of the transmission system; A2. Obtain the local physical property parameters of the silicon steel sheet to be processed; the local physical property parameters include at least the vibration response characteristics reflecting the fluctuation of material hardness and the dimensional deviation characteristics reflecting the uniformity of material thickness; A3. Obtain the quality constraint indicators of the current shearing task to be assigned, and quantify them as a task strictness score; A4. Based on the real-time operating status parameters, the local physical characteristic parameters, and the task severity score, perform multi-dimensional correlation analysis to generate a potential processing risk assessment value for the silicon steel sheet to be processed and the current shearing task to be assigned for each shearing unit; A5. Based on the potential processing risk assessment value corresponding to each shearing unit, execute the task allocation decision.

[0009] Secondly, this application provides a task allocation system for shearing silicon steel sheets with iron cores, the system comprising: The status acquisition module is used to acquire real-time operating status parameters of multiple shearing units; the real-time operating status parameters include at least load characteristic data reflecting the degree of tool wear and precision characteristic data reflecting the motion deviation of the transmission system. The feature acquisition module is used to acquire local physical property parameters of the silicon steel sheet to be processed; the local physical property parameters include at least vibration response characteristics reflecting the hardness fluctuation of the material and dimensional deviation characteristics reflecting the thickness uniformity of the material. The stringency assessment module is used to obtain the quality constraint indicators of the current shearing task to be assigned and quantify them as a task stringency score. The risk assessment module is used to perform multi-dimensional correlation analysis based on the real-time operating status parameters, the local physical characteristic parameters, and the task severity score, and generate a potential processing risk assessment value for the silicon steel sheet to be processed and the current shearing task to be assigned for each shearing unit. The allocation decision module is used to execute task allocation decisions based on the potential processing risk assessment values ​​corresponding to each of the shearing units.

[0010] Beneficial Effects: This application provides a method and system for allocating shearing tasks for silicon steel sheets with iron cores. By acquiring real-time operating status parameters of multiple shearing units and local physical characteristic parameters of the silicon steel sheets to be processed, and combining this with a task strictness score quantified from the quality constraint index of the currently assigned shearing task, multi-dimensional correlation analysis is performed to generate a potential processing risk assessment value for each shearing unit, targeting a specific silicon steel sheet and shearing task. Finally, task allocation decisions are made based on these assessment values. This method overcomes the limitations of existing technologies that allocate tasks based on static, idealized parameters, and can comprehensively consider the dynamic changes in equipment performance and the local characteristic fluctuations of raw materials in real time. By introducing potential processing risk assessment values, this application can effectively avoid assigning high-precision tasks to units with degraded performance, reducing product defects and raw material waste. Simultaneously, it can fully utilize high-performance units to handle more challenging tasks, thereby optimizing resource allocation, improving overall production efficiency and product quality, and significantly enhancing the controllability and economic benefits of the production process. Attached Figure Description

[0011] Figure 1 A flowchart illustrating a method for allocating shearing tasks for silicon steel sheets with iron cores, as provided in this application.

[0012] Figure 2 This is a schematic diagram of a core silicon steel sheet shearing task allocation system provided in this application.

[0013] Labeling Explanation: 1. Status Acquisition Module; 2. Feature Acquisition Module; 3. Strictness Assessment Module; 4. Risk Assessment Module; 5. Allocation Decision Module. Detailed Implementation

[0014] 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 the embodiments. The components of the embodiments of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely represents selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0015] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0016] Please refer to Figure 1 A method for assigning shearing tasks of silicon steel sheets with iron cores, as described in some embodiments of this application, includes the following steps: A1. Obtain real-time operating status parameters of multiple shearing units; the real-time operating status parameters include at least load characteristic data reflecting the degree of tool wear, and precision characteristic data reflecting the motion deviation of the transmission system; A2. Obtain the local physical property parameters of the silicon steel sheet to be processed; the local physical property parameters include at least the vibration response characteristics reflecting the fluctuation of material hardness and the dimensional deviation characteristics reflecting the uniformity of material thickness; A3. Obtain the quality constraint indicators of the current shearing task to be assigned, and quantify them as a task strictness score; A4. Based on the real-time operating status parameters, the local physical characteristic parameters, and the task severity score, perform multi-dimensional correlation analysis to generate a potential processing risk assessment value for the silicon steel sheet to be processed and the current shearing task to be assigned for each shearing unit; A5. Based on the potential processing risk assessment value corresponding to each shearing unit, execute the task allocation decision.

[0017] This application acquires real-time operating status parameters of the shearing unit and local physical property parameters of the silicon steel sheet to be processed, and combines these with task quality constraint indicators to conduct multi-dimensional correlation analysis. This generates a potential processing risk assessment value for each shearing unit, and based on this, makes task allocation decisions. This dynamic and comprehensive assessment mechanism effectively avoids the drawbacks of traditional static allocation methods, achieves precise matching between tasks and unit capabilities, and significantly improves shearing quality and production efficiency.

[0018] A "shearing unit" refers to a collection of equipment used for shearing silicon steel sheets. It typically includes a feeding mechanism, a shearing mechanism (such as scissors, dies, etc.), a transmission system, a control system, and related sensors. A production workshop may have multiple shearing units that are similar in function but may differ in actual operation and performance.

[0019] "Real-time operating status parameters" refer to data collected in real time by sensors or monitoring systems during the operation of the shearing unit, reflecting the current working status of the equipment. These parameters are dynamically changing and can reflect the "health status" and performance of the equipment.

[0020] "Load characteristic data" is a type of real-time operating status parameter used to characterize the wear level of the shearing unit's blades. During the shearing process, the blades bear different loads, and their wear directly affects the shearing quality and equipment lifespan.

[0021] "Accuracy characteristic data" is another type of real-time operating status parameter used to characterize the motion deviation of the shearing unit's transmission system. The accuracy of the transmission system is directly related to the dimensional accuracy and cut quality of the shearing process.

[0022] "Local physical property parameters" refer to the differences in physical properties that may exist in different locations of the silicon steel sheet to be processed. Even silicon steel sheets from the same batch may have slight fluctuations in their internal structure and surface properties.

[0023] "Vibration response characteristics" are a type of local physical property parameter used to reflect the hardness fluctuations of a material. Materials with different hardness will produce different vibration responses when sheared.

[0024] "Dimensional deviation characteristics" are another type of local physical property parameter used to reflect the uniformity of material thickness. Non-uniformity in material thickness can affect the stability of the shearing process and the flatness of the final product.

[0025] "Quality constraint indicators" refer to the specific requirements of the shearing task for the quality of the final product, such as shearing accuracy requirements, burr height requirements, and material flatness requirements. These indicators are important standards for measuring the quality of the shearing task.

[0026] The "Task Strictness Score" is a quantified numerical value of quality constraint indicators, used to comprehensively evaluate the difficulty of the shearing task and the performance requirements of the equipment. The higher the score, the more stringent the requirements of the shearing unit.

[0027] "Potential processing risk assessment value" is one of the core concepts of this application. It is a comprehensive indicator used to quantify the risks that a shearing unit may face when processing a specific silicon steel sheet and a specific task. The higher the risk value, the lower the probability that the unit will complete the task and meet the quality requirements.

[0028] The implementation environment of this application is typically a smart manufacturing workshop, in which multiple shearing units are deployed and equipped with various sensors, data acquisition systems, data processing units, and task allocation decision-making systems. These systems work together to achieve real-time data acquisition, analysis, and decision-making.

[0029] The core of the iron core silicon steel sheet shearing task allocation method proposed in this application lies in achieving intelligent matching between tasks and units through real-time analysis of multi-source heterogeneous data.

[0030] In step A1, it is necessary to acquire multiple real-time operating status parameters of the shearing unit. These parameters are crucial for evaluating the current performance of the unit. For example, current sensors can be installed on the drive motor of the shearing unit to collect the motor's current waveform in real time. By analyzing these current waveforms, characteristics related to tool wear can be identified, such as the distortion rate or harmonic content of the current waveform. When tool wear intensifies, the shearing resistance increases, causing changes in the motor current waveform. As a preferred implementation, force sensors can also be installed on the shearing tool to directly measure the shearing force. As the tool wears, the shearing force changes, thus reflecting the degree of tool wear. Furthermore, to obtain accuracy characteristic data reflecting the motion deviation of the transmission system, displacement sensors or encoders can be installed on the transmission components of the shearing unit to monitor the actual motion trajectory of the tool in real time. By comparing the actual motion trajectory with a preset ideal motion trajectory, the motion deviation can be calculated, thereby evaluating the accuracy of the transmission system. For example, under no-load conditions, the tool can be allowed to perform a preset reciprocating motion, and its displacement feedback data can be recorded. By analyzing the deviation of these data from the ideal trajectory, the accuracy characteristics of the transmission system can be obtained.

[0031] In step A2, it is necessary to obtain the local physical property parameters of the silicon steel sheet to be processed. These parameters help to assess the material's influence on the shearing process. For example, to obtain vibration response characteristics reflecting material hardness fluctuations, the silicon steel sheet can be scanned using non-contact sensors (such as laser vibrometers or ultrasonic sensors) to collect its vibration signals under specific excitation. By performing spectral analysis on these vibration signals, the intensity of specific frequency components related to material hardness can be extracted as vibration response characteristics. As a preferred embodiment, multiple micro piezoelectric sensor arrays are set on the surface of the silicon steel sheet. When the silicon steel sheet passes through, the sensor array senses the minute vibrations of the material. By analyzing the amplitude and frequency distribution of these vibration signals, the local hardness changes of the material can be inferred. To obtain dimensional deviation characteristics reflecting the uniformity of material thickness, the silicon steel sheet can be continuously scanned using non-contact thickness sensors (such as eddy current sensors or laser thickness gauges) before entering the shearing zone to obtain instantaneous measurements of its local thickness. By performing statistical analysis on these measurements, such as calculating the standard deviation or maximum deviation of the local thickness, dimensional deviation characteristics can be obtained.

[0032] In step A3, it is necessary to obtain the quality constraint indicators for the currently assigned shearing task and quantify them into a task rigor score. For example, for a shearing task, its quality constraint indicators may include shearing accuracy requirements (e.g., ±0.05mm), burr height requirements (e.g., less than 0.02mm), and material flatness requirements (e.g., warpage less than 0.1mm / m). These indicators can be obtained through manual input, extraction from the production order database, or communication with the customer. Once these quality constraint indicators are obtained, they need to be quantified into a task rigor score. For example, a scoring standard can be set for each quality constraint indicator; for example, the higher the shearing accuracy requirement, the higher the corresponding score; the lower the burr height requirement, the higher the score. Then, the weighted sum of the rigor scores for each quality constraint indicator is used to obtain the final task rigor score. For example, different weights can be assigned to shearing accuracy, burr height, and material flatness based on experience or historical data, and then the individual scores are multiplied by their respective weights and summed to obtain the total task rigor score.

[0033] In step A4, a multidimensional correlation analysis is performed based on real-time operating status parameters, local physical characteristic parameters, and task severity scores to generate a potential processing risk assessment value for each shearing unit, targeting the silicon steel sheet to be processed and the currently assigned shearing task. This step is the core intelligent analysis component of this application. For example, a multidimensional risk assessment model can be established, taking load characteristic data of tool wear, accuracy characteristic data of transmission system motion deviation, vibration response characteristics of material hardness fluctuation, dimensional deviation characteristics of material thickness uniformity, and task severity score as inputs. The model can employ machine learning algorithms (such as support vector machines, neural networks, or decision trees) or rule-based expert systems. For instance, when tool wear is severe, transmission system accuracy decreases, material hardness fluctuates greatly, and the task severity score is high, the model will output a high potential processing risk assessment value. Conversely, when the unit is in good condition, material properties are stable, and task requirements are not high, the risk assessment value will be lower.

[0034] In step A5, task allocation decisions are made based on the potential processing risk assessment values ​​corresponding to each shearing unit. For example, a preset risk threshold can be set. First, the potential processing risk assessment values ​​of each shearing unit are compared with the preset risk threshold. If only one or no shearing unit has a potential processing risk assessment value lower than the threshold, the shearing unit with the lowest risk assessment value is identified as the target shearing unit. If multiple shearing units have potential processing risk assessment values ​​lower than the threshold, further decision-making mechanisms are needed. For example, a dynamic energy efficiency-quality ratio assessment can be introduced. For these candidate shearing units with risk values ​​lower than the threshold, their real-time power consumption data is obtained, and combined with real-time operating status parameters, local physical characteristic parameters, and task strictness scores, the expected pass rate of each candidate unit for the current task is assessed. Then, the dynamic energy efficiency-quality ratio is calculated based on the real-time power consumption data and the expected pass rate, and the candidate shearing unit with the lowest energy efficiency-quality ratio is identified as the target shearing unit. Finally, the currently assigned shearing task is assigned to the target shearing unit.

[0035] The iron-core silicon steel sheet shearing task allocation method proposed in this application constructs a multi-dimensional potential processing risk assessment model by introducing real-time operating status parameters, local physical property parameters, and task severity scores, thereby realizing intelligent allocation of shearing tasks.

[0036] Specifically, traditional methods rely solely on the static nominal parameters of the unit for task allocation, failing to detect dynamic changes in equipment performance and fluctuations in the local characteristics of raw materials. For example, a shearing unit with a nominal high precision may have a significantly lower actual processing capacity than its nominal value if its cutting tools are severely worn or its transmission system malfunctions. Assigning high-precision tasks to it in this situation would easily lead to product defects. This application, by acquiring load characteristic data reflecting the degree of cutting tool wear and precision characteristic data reflecting the motion deviation of the transmission system in real time, can dynamically assess the actual "health status" and performance level of the unit, avoiding resource misallocation caused by changes in equipment status.

[0037] Furthermore, traditional methods often overlook the local physical property differences inherent in silicon steel sheets. Even within the same batch of materials, slight fluctuations may exist in properties such as hardness and thickness. When these fluctuations are superimposed on a decline in unit performance, they can easily lead to shearing quality problems or even equipment failure. This application, by acquiring vibration response characteristics reflecting material hardness fluctuations and dimensional deviation characteristics reflecting material thickness uniformity, can predict the potential impact of materials on the shearing process in advance. This allows material properties to be taken into account during task allocation, further reducing processing risks.

[0038] Furthermore, this application quantifies the quality constraints of the shearing task into a task rigor score, clearly expressing the difficulty of the task and the requirements for the equipment. By conducting a multi-dimensional correlation analysis between the unit's real-time performance, the local properties of the material, and the task rigor, this application can generate a comprehensive potential processing risk assessment value for each unit. This assessment value intuitively reflects the degree of matching and potential risks of a specific unit when handling specific materials and specific tasks.

[0039] Ultimately, task allocation decisions are made based on these potential processing risk assessments, ensuring that tasks are assigned to the most suitable shearing units. This not only significantly improves product yield and reduces scrap and rework, but also effectively extends the lifespan of tools and equipment, reducing maintenance costs. For example, for a high-sustainability task, the system prioritizes the unit with the lowest potential processing risk assessment value—that is, the unit currently in optimal condition and best suited for the task. Conversely, for a low-sustainability task, the system may select a unit with higher energy efficiency while still meeting quality requirements, thereby maximizing production efficiency and economic benefits.

[0040] In summary, this application overcomes the limitations of task allocation based on static parameters in existing technologies by using a real-time, dynamic, and multi-dimensional evaluation mechanism. It achieves precise matching between shearing tasks and unit capabilities, significantly improving the level of intelligence, product quality, and overall economic benefits of iron core silicon steel sheet shearing production.

[0041] In some implementations, step A1 includes: A101. Collect the real-time current waveform of the shearing unit drive motor, and obtain load characteristic data reflecting the degree of tool wear by analyzing the distortion rate of the real-time current waveform relative to the reference current waveform. A102. Under no-load conditions, acquire displacement feedback data when the shearing unit's blade executes a preset motion trajectory, and obtain precision characteristic data reflecting the motion deviation of the transmission system by calculating the deviation between the displacement feedback data and the preset motion trajectory.

[0042] Among them, acquiring the real-time current waveform of the shear unit drive motor refers to continuously monitoring and acquiring the current of the drive motor during operation through a current sensor. Specifically, a Hall effect current sensor or a current transformer can be used. These sensors can convert the real-time current signal of the motor into a processable electrical signal.

[0043] Among them, analyzing the distortion rate of the real-time current waveform relative to the reference current waveform refers to comparing the acquired real-time current waveform with a pre-set reference current waveform obtained under the tool's healthy state (such as a brand-new state) to quantify the degree of difference between the two. Specifically, Fourier transform can be used to perform spectral analysis on the current waveform to calculate the harmonic content or total harmonic distortion (THD), or waveform features can be extracted through signal processing methods such as wavelet analysis and empirical mode decomposition, and compared with the corresponding features of the reference waveform to obtain the distortion rate.

[0044] Among them, obtaining load characteristic data reflecting the degree of tool wear refers to characterizing the degree of tool wear through the quantification results of the above distortion rate. Specifically, the distortion rate itself can be used as load characteristic data, or the distortion rate can be mapped to a preset tool wear level. For example, the higher the distortion rate, the greater the tool wear level (i.e., at this time, the load characteristic data is the tool wear level).

[0045] Among them, obtaining displacement feedback data when the shearing unit's blade executes a preset motion trajectory under no-load conditions refers to allowing the shearing unit's blade to move along a preset path without shearing silicon steel sheets, and recording the actual position information of the blade through a displacement sensor. Specifically, high-precision displacement measurement equipment such as laser displacement sensors, encoders, or grating rulers can be used to obtain the coordinate data of the blade on the X, Y, and Z axes in real time.

[0046] Among them, calculating the deviation between displacement feedback data and preset motion trajectory refers to comparing the actual displacement feedback data of the tool movement with the ideal preset motion trajectory and quantifying the degree of deviation between the two. Specifically, the deviation can be represented by indicators such as Euclidean distance, root mean square error (RMSE), or maximum absolute error.

[0047] Among them, obtaining the precision feature data reflecting the motion deviation of the transmission system means characterizing the motion precision of the transmission system through the quantification results of the above deviation. Specifically, the deviation itself can be used as the precision feature data, or the deviation value can be mapped to a preset precision level. For example, the smaller the deviation value, the higher the precision level of the transmission system (that is, at this time, the precision feature data is the precision level of the transmission system).

[0048] This solution acquires the tool wear level by collecting real-time current waveforms of the drive motor and obtains tool displacement feedback data under no-load conditions to obtain transmission system motion deviations. These data serve as real-time operating status parameters of the shearing unit, providing a basis for subsequent task allocation decisions. This parameter acquisition method avoids the lag of traditional reliance on experience-based judgment or periodic shutdown inspections, achieving real-time and refined assessment of equipment status. Through these real-time operating status parameters, the actual processing capacity of each shearing unit can be more accurately evaluated. Therefore, during task allocation, high-precision tasks can be assigned to units in excellent condition, while tasks with lower precision requirements can be assigned to units in average condition, avoiding resource misallocation and product defects.

[0049] In some implementations, step A2 includes: A201. Collect vibration signals from the silicon steel sheet to be processed, and perform spectral analysis on the vibration signals to extract vibration response characteristics that reflect material hardness fluctuations; A202. Collect pressure measurement data on the surface of the silicon steel sheet to be processed to extract dimensional deviation characteristics that reflect the uniformity of material thickness.

[0050] The acquisition of vibration signals from the silicon steel sheet to be processed refers to obtaining mechanical vibration information of the silicon steel sheet under specific excitations (such as impact excitation, sinusoidal excitation, etc.) through sensors. Specifically, a multi-point micro-vibration sensor array can be used, arranged across the width of the silicon steel sheet to comprehensively acquire vibration signals from different locations on the sheet. Spectral analysis of the vibration signal involves converting the acquired time-domain vibration signal into a frequency-domain signal to reveal its frequency composition and the intensity of each frequency component. This can be achieved using digital signal processing techniques such as Fast Fourier Transform (FFT). By analyzing the spectral characteristics, the intensity of specific frequency components related to material hardness can be identified. For example, the higher the material hardness, the stronger the vibration response at certain frequencies (such as natural frequencies) under specific excitations. The intensity of these specific frequency components is used as vibration response characteristics to quantify the local fluctuations in material hardness.

[0051] Acquiring pressure measurement data from the surface of a silicon steel sheet to be processed refers to obtaining pressure distribution information on the surface of the silicon steel sheet under specific conditions using sensors. Specifically, a non-contact micro-pressure sensor can be used. This sensor senses the air pressure of the silicon steel sheet to be processed, thereby obtaining an instantaneous measurement of the local thickness of the silicon steel sheet. This instantaneous measurement value is obtained at different local locations on the silicon steel sheet (e.g., multiple local locations arranged along the width direction). For example, by spraying a stable airflow onto the surface of the silicon steel sheet and measuring the gap pressure formed between the airflow and the surface, a correlation exists between the gap pressure and the local thickness. Statistical analysis of the pressure measurement data involves mathematically processing the acquired instantaneous measurements of local thickness to assess its uniformity. Specifically, the standard deviation of the local thickness can be calculated as a dimensional deviation characteristic; a larger standard deviation indicates poorer thickness uniformity.

[0052] The solution presented in this application, through the aforementioned steps, enables more precise and comprehensive acquisition of the local physical property parameters of the silicon steel sheet to be processed. Specifically, the extraction of vibration response characteristics allows the system to identify subtle fluctuations in the material's internal hardness distribution, which is crucial for predicting potential uneven tool wear or abnormal shearing forces during the shearing process. Simultaneously, the acquisition of dimensional deviation characteristics directly quantifies the uniformity of material thickness, which is essential for ensuring the dimensional accuracy of the sheared product and avoiding shearing defects caused by uneven material thickness. Through these detailed physical property parameters, subsequent multidimensional correlation analysis can establish a more accurate processing risk assessment model.

[0053] In some implementations, step A3 includes: A301. Obtain the quality constraint indicators of the shearing task to be assigned; the quality constraint indicators include shearing accuracy requirements, burr height requirements, and material flatness requirements. A302. Determine the corresponding sub-item strictness score based on each quality constraint indicator; A303. The weighted summation of the severity scores of each quality constraint indicator is used to obtain the task severity score.

[0054] Specifically, shearing accuracy requirements refer to the stringent requirements for the dimensional accuracy of the final sheared part, usually measured by the allowable dimensional deviation range; burr height requirements refer to the limitations on the smoothness of the sheared edge, expressed as the maximum allowable burr height; and material flatness requirements focus on the flatness of the silicon steel sheet surface after shearing, defined for example by allowable warpage or waviness. These indicators together constitute a comprehensive evaluation system for the quality of shearing tasks.

[0055] Determining the corresponding sub-item stringency score involves assigning a quantified value to each quality constraint indicator based on its specific requirements. For example, for high-precision shearing tasks, the sub-item stringency score for shearing precision requirements will be higher; for tasks with strict limitations on burr height, the sub-item stringency score for burr height requirements will also be increased accordingly. These scores can be set based on preset rules, industry standards, or expert experience to reflect the importance of each indicator and the difficulty of meeting it.

[0056] Furthermore, weighted summing of the severity scores for each quality constraint indicator involves assigning different weights to the severity scores of different quality constraint indicators based on their relative importance in the overall task quality, and then summing or averaging these weights to obtain a unified score that comprehensively reflects the task's severity level. For example, if shearing accuracy is the most critical aspect of a specific task, its corresponding severity score will be given a higher weight. This weighted summing ensures that the task severity score objectively and accurately reflects the overall quality requirements of the shearing task to be assigned.

[0057] This application's solution overcomes the subjectivity and bias of traditional methods in assessing task rigor by refining complex quality constraints into quantifiable sub-items and then weighting and synthesizing them. Specifically, it first obtains quality constraints across multiple dimensions, such as shearing accuracy requirements, burr height requirements, and material flatness requirements, ensuring comprehensive coverage of task quality requirements. Second, by assigning corresponding rigor scores to each quality constraint, the quality requirements across different dimensions can be standardized and quantified, facilitating subsequent calculations and comparisons. Finally, these rigor scores are weighted and synthesized, considering not only the absolute rigor of each indicator but also their relative importance in a specific task, thereby generating a comprehensive task rigor score. Therefore, this method provides a more refined, objective, and comprehensive assessment of task rigor, offering a solid data foundation for subsequent shearing task allocation decisions.

[0058] In some implementations, step A4 includes: A401. Calculate the tool health score based on the load characteristic data in the real-time operating status parameters; A402. Calculate the dynamic accuracy index based on the accuracy characteristic data in the real-time operating status parameters; A403. The local quality index of the material is calculated based on the vibration response characteristics and dimensional deviation characteristics in the local physical property parameters. A404. The tool health score, the dynamic accuracy index, the material local quality index, and the task severity score are weighted and fused to obtain the potential machining risk assessment value.

[0059] Specifically, the tool health score is a quantitative assessment of the current wear or aging degree of the shearing unit's tools. This score is typically calculated based on load characteristic data from real-time operating status parameters. A higher tool health score indicates better tool condition and lower wear. For example, the tool health score can be calculated using a preset mapping function or lookup table to convert load characteristic data (such as distortion rate) into a score between 0 and 100, where 100 represents the tool in optimal health and 0 represents complete tool failure.

[0060] The dynamic accuracy index can be understood as the accuracy with which the shearing unit's transmission system maintains a preset motion trajectory during dynamic operation. This index is calculated based on accuracy characteristic data from real-time operating parameters. A lower dynamic accuracy index indicates smaller motion deviation in the transmission system and higher shearing accuracy. The calculation of the dynamic accuracy index involves standardizing accuracy characteristic data (such as the deviation between the displacement feedback data and the preset motion trajectory) to an index ranging from 0 to 100, where 0 represents the best accuracy and 100 represents the worst accuracy.

[0061] In practical applications, the local quality index of a material is specifically a quantitative indicator of the quality of physical properties in a local area of ​​a silicon steel sheet to be processed. This index is calculated by comprehensively considering the vibration response characteristics and dimensional deviation characteristics among the local physical property parameters. For example, the local quality index can be calculated using a weighted average or fuzzy logic reasoning, combining the vibration response characteristics and dimensional deviation characteristics into an index ranging from 0 to 100, where 100 represents the best material quality and 0 represents the worst material quality.

[0062] Further, in step A404, weighted fusion refers to comprehensively calculating multiple independent evaluation indicators (such as tool health score, dynamic accuracy index, material local quality index, and task rigor score) according to preset weight coefficients to obtain a unified comprehensive evaluation value. For example, the potential machining risk assessment value can be calculated using the following formula: Potential machining risk assessment value = C1(100 - tool health score) + C2 dynamic accuracy index + C3(100 - material local quality index) + C4 task rigor score, where C1-C4 are weight coefficients used to adjust the relative importance of each indicator in the final potential machining risk assessment value, which can be set based on actual production experience, historical data analysis, or expert knowledge.

[0063] This application's solution addresses the potential ambiguity and subjectivity of traditional multidimensional correlation analysis by refining and quantifying the key factors influencing shearing task allocation. Specifically, in steps A401 and A402, the real-time operating status of the shearing unit is decomposed into a tool health score and a dynamic accuracy index, objectively reflecting the unit's processing capacity from the two dimensions of tool wear and transmission accuracy, respectively. In step A403, the local physical characteristics of the silicon steel sheet to be processed are quantified into a material local quality index, enabling a more accurate assessment of the material's impact on the shearing process. Subsequently, in step A404, these quantified indicators, along with the task severity score, are incorporated into a weighted fusion model. This fusion approach allows for flexible adjustment of the importance of various influencing factors, ensuring that the potential processing risk assessment comprehensively, objectively, and quantitatively reflects the potential risks of a specific shearing unit when processing a specific silicon steel sheet and meeting specific task requirements. This provides a solid data foundation and scientific basis for subsequent task allocation decisions.

[0064] In some embodiments described above in this application, a scheme is proposed for making task allocation decisions based on the potential processing risk assessment value corresponding to each shearing unit. However, in actual operation, making decisions solely based on the potential processing risk assessment value may have limitations. For example, when the potential processing risk assessment values ​​of multiple shearing units are all at a low level, how to further select the optimal unit to achieve more efficient and economical production, or how to avoid assigning tasks to units with low risk but still potential hidden dangers, are not addressed in detail in the basic scheme. If these problems are not solved, it may lead to suboptimal task allocation, or even increase potential production risks or resource waste.

[0065] Therefore, in some implementations, step A5 includes: A501. Compare the potential processing risk assessment value corresponding to each shearing unit with the preset risk threshold; A502. If the number of potential processing risk assessment values ​​that do not exceed the preset risk threshold does not exceed one, then the shearing unit with the smallest potential processing risk assessment value is determined as the target shearing unit. A503. If the number of potential processing risk assessment values ​​that do not exceed the preset risk threshold is more than one, then the target shearing unit is determined from the shearing units whose potential processing risk assessment values ​​do not exceed the preset risk threshold through dynamic energy efficiency quality ratio assessment. A504. Assign the currently unassigned shearing task to the target shearing unit.

[0066] Specifically, in step A501, the potential processing risk assessment value corresponding to each shearing unit is compared with a preset risk threshold. This preset risk threshold is a key reference standard, designed to define an acceptable range of risk levels. This threshold can be flexibly set based on various factors such as industry standards, historical data, product quality requirements, and equipment maintenance strategies to ensure that only shearing units with risks within a controllable range are included in subsequent allocation considerations. For example, it can be set that only units with potential processing risk assessment values ​​below a certain value (e.g., 20 points) are considered to meet basic risk requirements.

[0067] Furthermore, in step A502, if, after preliminary screening, the number of shearing units with potential processing risk assessment values ​​not exceeding a preset risk threshold does not exceed one (i.e., only one or none), then the shearing unit with the lowest potential processing risk assessment value is directly identified as the target shearing unit. This simplifies the decision-making process and ensures that, when choices are limited, the task can be assigned to the option with the lowest risk and greatest safety.

[0068] Furthermore, in step A503, if the number of shearing units whose potential processing risk assessment values ​​do not exceed a preset risk threshold exceeds one, it indicates that there are multiple candidate units that meet the basic risk requirements. In this case, this application introduces a dynamic energy efficiency-to-quality ratio evaluation mechanism. This evaluation aims to further screen out the units with the best performance in terms of energy efficiency and expected output quality from these units with controllable risks. This reflects a comprehensive consideration of production efficiency and economic benefits under the premise of meeting risk control requirements.

[0069] Finally, in step A504, the current shearing task to be assigned is assigned to the target shearing unit determined through the above-described hierarchical decision-making process.

[0070] Through the above technical solution, this application can effectively avoid assigning shearing tasks to shearing units with potentially high processing risks, thereby significantly reducing the risk of quality defects or equipment failures during production. Especially when multiple shearing units meet the basic risk requirements, the introduction of dynamic energy efficiency-quality ratio assessment ensures that task allocation not only considers risk but also production efficiency and product quality, thus achieving more refined and intelligent task allocation decisions. This helps improve the overall stability and reliability of the production line, optimize resource allocation, and ultimately enhance the comprehensive benefits of shearing core silicon steel sheets.

[0071] Preferably, step A503 may include: Shearing units whose potential processing risk assessment value does not exceed the preset risk threshold are selected as candidate shearing units, and real-time power consumption data of each candidate shearing unit are obtained. By combining the real-time operating status parameters, the local physical characteristic parameters, and the task severity score, the expected pass rate of each candidate shearing unit for the current shearing task to be assigned is evaluated. The dynamic energy efficiency quality ratio is calculated based on the real-time power consumption data and the expected pass rate, and the candidate shear unit with the lowest dynamic energy efficiency quality ratio is determined as the target shear unit.

[0072] Specifically, when the potential processing risk assessment values ​​of multiple shearing units do not exceed a preset risk threshold, these units are identified as candidate shearing units. For each candidate shearing unit, its real-time power consumption data needs to be obtained. Real-time power consumption data can be understood as the electrical energy consumed by the shearing unit per unit time under its current operating state, reflecting the unit's energy consumption level.

[0073] The expected yield rate refers to the proportion of products that a specific shearing unit is expected to produce that meet quality standards under the current shearing task to be assigned. This expected yield rate is assessed by combining real-time operating status parameters, local physical characteristic parameters, and task rigor scores. For example, these multidimensional parameters can be mapped to the expected yield rate based on a pre-established rule set or machine learning model. This rule set or model can be trained based on historical data to accurately predict the unit's production quality performance under specific conditions.

[0074] In practical applications, the dynamic energy efficiency-to-quality ratio is calculated using real-time energy consumption data and the expected yield rate. For example, real-time energy consumption data can be divided by the product of the expected yield rate and a preset material value coefficient. The preset material value coefficient quantifies the economic value of the processed materials, making the calculation of the energy efficiency-to-quality ratio more meaningful. A lower dynamic energy efficiency-to-quality ratio indicates that the shearing unit consumes less energy, has higher production efficiency, and better overall benefits while ensuring product quality.

[0075] This application's solution addresses the challenge of optimal task allocation when multiple shearing units have manageable risks by introducing real-time energy consumption data and expected yield rates, and calculating a dynamic energy efficiency-quality ratio. Specifically, when the potential processing risk assessment values ​​of multiple shearing units are all below a preset risk threshold, these units are considered to have the basic capability to perform the current task. However, to further optimize resource allocation and improve production efficiency, this solution goes beyond risk assessment alone, directly reflecting the operating costs of each candidate shearing unit by acquiring real-time energy consumption data. Simultaneously, by combining the unit's real-time operating status parameters, the local physical characteristics of the silicon steel sheets to be processed, and the task stringency score, the expected yield rate for the current task is comprehensively evaluated, thereby quantifying its production quality assurance capability. By correlating energy consumption costs with quality output, a dynamic energy efficiency-quality ratio is calculated. This ratio comprehensively measures the unit's overall performance under a specific task, achieving higher yield output with lower energy consumption. Therefore, selecting the shearing unit with the lowest dynamic energy efficiency-quality ratio ensures that energy efficiency and economic benefits are maximized while meeting quality requirements.

[0076] In reality, even if a shearing unit has a low potential processing risk assessment value, if its operating status shows an unstable trend or fluctuation, or if the quality constraints of the currently assigned shearing tasks are very strict, directly assigning tasks to that unit may still lead to substandard processing quality or unexpected downtime. Failure to address these issues may reduce the reliability of task allocation and production efficiency.

[0077] In some preferred embodiments, after step A3 and before step A5, the following step is also included: A6. Based on the status change information of the real-time operating status parameters and the task severity score, select the effective shearing units that can be used for the current shearing task to be assigned from each of the shearing units; In step A5, task allocation decisions are made only for effective shear units.

[0078] Specifically, a pre-screening mechanism is introduced before executing the task allocation decision. This mechanism uses the real-time operating status parameters of the shearing unit, such as its operating trend and fluctuation frequency, to assess the unit's current stability. Simultaneously, it combines this with the task severity score of the currently assigned shearing task to comprehensively determine whether each shearing unit is suitable for the task. The status change information can be understood as data reflecting the dynamic trend or abnormal conditions of the shearing unit's operating status over a period of time, aiming to identify potential failure risks or performance degradation trends. The task severity score quantifies the task's requirements for processing quality, aiming to differentiate the difficulty of different tasks and their performance requirements for the unit. In this way, shearing units with unstable operating states or unsuitable for the current task are excluded, resulting in a set of "effective shearing units." Subsequently, the task allocation decision in step A5 will only be made for these selected effective shearing units; that is, only the target shearing unit is determined from the effective shearing units, and the currently assigned shearing task is assigned to that target shearing unit, thereby ensuring the rationality and reliability of task allocation.

[0079] This application's solution introduces a dynamic assessment and screening mechanism for shear unit availability by adding step A6 between steps A3 and A5. In the basic solution, task allocation is mainly based on the potential processing risk assessment value. Although this assessment value integrates multiple factors, it may not be able to capture subtle changes in the unit's operating status or potential instability in real time. For example, a unit's potential processing risk assessment value may be temporarily low, but its real-time operating status parameters may be experiencing rapid drift or abnormal fluctuations, indicating an impending failure or performance degradation. In this case, directly assigning a high-strickenness task may lead to processing failure. Through step A6, the system can dynamically identify shear units with unplanned downtime risks or unstable performance based on real-time operating status parameter changes, such as drift rate or abnormal fluctuation frequency. Simultaneously, combined with task severity scoring, the system will more rigorously screen units for high-strickenness tasks, ensuring that only those units with stable operating status and capable of meeting high requirements are considered. Therefore, shearing units that may not have the highest potential processing risk assessment value but are actually in poor operating condition or unsuitable for the current task will be excluded in advance, thus avoiding the assignment of tasks to unsuitable units.

[0080] In some implementations, step A6 includes: A601. Compare the task rigor score with the preset high difficulty score threshold; A602. If the task rigor score does not exceed the high difficulty score threshold, then all the shearing units are determined as valid shearing units; A603. If the task rigor score exceeds the high difficulty score threshold, then all shearing units are added to the set of viable candidate shearing units, and the following steps are performed for each shearing unit: The drift rate and abnormal fluctuation frequency of the real-time operating status parameters of the current shear unit within a preset time period are obtained as the status change information. If the drift rate or abnormal fluctuation frequency of at least one of the real-time operating status parameters exceeds the corresponding preset warning threshold, it is determined that the current shear unit has an unplanned shutdown risk, and the current shear unit is removed from the set of candidate valid shear units. A604. The remaining shearing units in the set of candidate effective shearing units are determined as effective shearing units.

[0081] Specifically, in step A601, the high difficulty scoring threshold can be understood as a preset boundary that distinguishes between ordinary tasks and high difficulty tasks. This threshold can be set based on actual production experience, historical data analysis, or expert knowledge to ensure that differentiated screening strategies are adopted for tasks of different difficulties.

[0082] Further, in step A603, the drift rate refers to the trend or rate of change of real-time operating status parameters over a period of time. For example, it can be a slow but continuous deterioration trend of load characteristic data on tool wear or accuracy characteristic data on transmission system motion deviation over time. Specifically, it can be represented by calculating the slope using linear regression analysis of the parameter values. Abnormal fluctuation frequency refers to the number or frequency of times real-time operating status parameters exceed the normal range within a preset time period. Specifically, it can be calculated by counting the number of sample points where the parameter values ​​fall outside the preset upper and lower limits, and dividing by the total number of sample points. This status change information can more precisely reflect the dynamic health status and potential failure risks of the shearing unit. The preset warning thresholds are safety limits set for the drift rate and abnormal fluctuation frequency. Once these thresholds are exceeded, it indicates a high risk of unplanned downtime or performance degradation in the shearing unit. The preset warning thresholds can be set based on the equipment manufacturer's recommendations, historical failure data, or maintenance experience.

[0083] This application's solution achieves differentiated screening of shearing units by introducing a comparison between task rigor scoring and a high-difficulty scoring threshold. When the task difficulty is low, all shearing units are considered effective, thereby maximizing equipment utilization. However, when the task difficulty is high, the system activates a more stringent screening mechanism, proactively identifying and eliminating units with potential unplanned downtime risks by analyzing the drift rate and abnormal fluctuation frequency of the shearing units' real-time operating status parameters. It is precisely because of this dynamic and refined risk assessment that high-difficulty tasks can be assigned to the most stable and reliable shearing units, thereby effectively avoiding potential processing risks.

[0084] It should be noted that in step A604, if the set of available effective shearing units is empty, that is, there are no remaining shearing units, an alarm signal is issued and the allocation decision for the currently assigned shearing task is suspended.

[0085] refer to Figure 2 This application provides a task allocation system for shearing silicon steel sheets with iron cores, the system comprising: Status acquisition module 1 is used to acquire real-time operating status parameters of multiple shearing units; the real-time operating status parameters include at least load characteristic data reflecting the degree of tool wear, and precision characteristic data reflecting the motion deviation of the transmission system (for details, please refer to step A1 above). The characteristic acquisition module 2 is used to acquire local physical characteristic parameters of the silicon steel sheet to be processed; the local physical characteristic parameters include at least the vibration response characteristics reflecting the hardness fluctuation of the material and the dimensional deviation characteristics reflecting the thickness uniformity of the material (for details, please refer to step A2 above). The rigor assessment module 3 is used to obtain the quality constraint index of the current shearing task to be assigned and quantify it into a task rigor score (for details, please refer to step A3 above). Risk assessment module 4 is used to perform multi-dimensional correlation analysis based on the real-time operating status parameters, the local physical characteristic parameters, and the task severity score, and generate potential processing risk assessment values ​​for the silicon steel sheet to be processed and the current shearing task to be assigned for each shearing unit (for details, please refer to step A4 above). The allocation decision module 5 is used to perform task allocation decisions based on the potential processing risk assessment value corresponding to each shearing unit (the specific process can be referred to step A5 above).

[0086] In some embodiments, the iron core silicon steel sheet shearing task allocation system further includes: The unit initial screening module is used to select effective shearing units that can be used for the current shearing task to be assigned from each shearing unit based on the status change information of the real-time operating status parameters and the task strictness score (for details, please refer to step A6 above). The task allocation decision module 5 only performs task allocation decisions for effective shear units.

[0087] The above description is merely an embodiment of this application and is not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A method for allocating shearing tasks for silicon steel sheets with iron cores, characterized in that, The method includes the following steps: A1. Obtain real-time operating status parameters of multiple shearing units; the real-time operating status parameters include at least load characteristic data reflecting the degree of tool wear, and precision characteristic data reflecting the motion deviation of the transmission system; A2. Obtain the local physical property parameters of the silicon steel sheet to be processed; the local physical property parameters include at least the vibration response characteristics reflecting the fluctuation of material hardness and the dimensional deviation characteristics reflecting the uniformity of material thickness; A3. Obtain the quality constraint indicators of the current shearing task to be assigned, and quantify them as a task strictness score; A4. Based on the real-time operating status parameters, the local physical characteristic parameters, and the task severity score, perform multi-dimensional correlation analysis to generate a potential processing risk assessment value for the silicon steel sheet to be processed and the current shearing task to be assigned for each shearing unit; A5. Based on the potential processing risk assessment value corresponding to each shearing unit, execute the task allocation decision.

2. The method for allocating shearing tasks for silicon steel sheets with iron cores according to claim 1, characterized in that, Step A1 includes: A101. Collect the real-time current waveform of the shearing unit drive motor, and obtain load characteristic data reflecting the degree of tool wear by analyzing the distortion rate of the real-time current waveform relative to the reference current waveform. A102. Under no-load conditions, acquire displacement feedback data when the shearing unit's blade executes a preset motion trajectory, and obtain precision characteristic data reflecting the motion deviation of the transmission system by calculating the deviation between the displacement feedback data and the preset motion trajectory.

3. The method for allocating shearing tasks for silicon steel sheets with iron cores according to claim 1, characterized in that, Step A2 includes: A201. Collect vibration signals from the silicon steel sheet to be processed, and perform spectral analysis on the vibration signals to extract vibration response characteristics that reflect material hardness fluctuations; A202. Collect pressure measurement data on the surface of the silicon steel sheet to be processed to extract dimensional deviation characteristics that reflect the uniformity of material thickness.

4. The method for allocating shearing tasks for silicon steel sheets with iron cores according to claim 1, characterized in that, Step A3 includes: A301. Obtain the quality constraint indicators of the shearing task to be assigned; the quality constraint indicators include shearing accuracy requirements, burr height requirements, and material flatness requirements. A302. Determine the corresponding sub-item strictness score based on each quality constraint indicator; A303. The weighted summation of the severity scores of each quality constraint indicator is used to obtain the task severity score.

5. The method for allocating shearing tasks for silicon steel sheets with iron cores according to claim 1, characterized in that, Step A4 includes: A401. Calculate the tool health score based on the load characteristic data in the real-time operating status parameters; A402. Calculate the dynamic accuracy index based on the accuracy characteristic data in the real-time operating status parameters; A403. The local quality index of the material is calculated based on the vibration response characteristics and dimensional deviation characteristics in the local physical property parameters. A404. The tool health score, the dynamic accuracy index, the material local quality index, and the task severity score are weighted and fused to obtain the potential machining risk assessment value.

6. The method for allocating shearing tasks for silicon steel sheets with iron cores according to claim 1, characterized in that, Step A5 includes: A501. Compare the potential processing risk assessment value corresponding to each shearing unit with the preset risk threshold; A502. If the number of potential processing risk assessment values ​​that do not exceed the preset risk threshold does not exceed one, then the shearing unit with the smallest potential processing risk assessment value is determined as the target shearing unit. A503. If the number of potential processing risk assessment values ​​that do not exceed the preset risk threshold is more than one, then the target shearing unit is determined from the shearing units whose potential processing risk assessment values ​​do not exceed the preset risk threshold through dynamic energy efficiency quality ratio assessment. A504. Assign the currently unassigned shearing task to the target shearing unit.

7. The method for allocating shearing tasks for silicon steel sheets with iron cores according to claim 6, characterized in that, Step A503 includes: Shearing units whose potential processing risk assessment value does not exceed the preset risk threshold are selected as candidate shearing units, and real-time power consumption data of each candidate shearing unit are obtained. By combining the real-time operating status parameters, the local physical characteristic parameters, and the task severity score, the expected pass rate of each candidate shearing unit for the current shearing task to be assigned is evaluated. The dynamic energy efficiency quality ratio is calculated based on the real-time power consumption data and the expected pass rate, and the candidate shear unit with the lowest dynamic energy efficiency quality ratio is determined as the target shear unit.

8. The method for allocating shearing tasks for silicon steel sheets with iron cores according to claim 1, characterized in that, The steps following step A3 and before step A5 include: A6. Based on the status change information of the real-time operating status parameters and the task severity score, select the effective shearing units that can be used for the current shearing task to be assigned from each of the shearing units; In step A5, task allocation decisions are made only for effective shear units.

9. The method for allocating shearing tasks for silicon steel sheets with iron cores according to claim 8, characterized in that, Step A6 includes: A601. Compare the task rigor score with the preset high difficulty score threshold; A602. If the task rigor score does not exceed the high difficulty score threshold, then all the shearing units are determined as valid shearing units; A603. If the task rigor score exceeds the high difficulty score threshold, then all shearing units are added to the set of viable candidate shearing units, and the following steps are performed for each shearing unit: The drift rate and abnormal fluctuation frequency of the real-time operating status parameters of the current shear unit within a preset time period are obtained as the status change information. If the drift rate or abnormal fluctuation frequency of at least one of the real-time operating status parameters exceeds the corresponding preset warning threshold, it is determined that the current shear unit has an unplanned shutdown risk, and the current shear unit is removed from the set of candidate valid shear units. A604. The remaining shearing units in the set of candidate effective shearing units are determined as effective shearing units.

10. A task allocation system for shearing silicon steel sheets with iron cores, characterized in that, The system includes: The status acquisition module is used to acquire real-time operating status parameters of multiple shearing units; the real-time operating status parameters include at least load characteristic data reflecting the degree of tool wear and precision characteristic data reflecting the motion deviation of the transmission system. The feature acquisition module is used to acquire local physical property parameters of the silicon steel sheet to be processed; the local physical property parameters include at least vibration response characteristics reflecting the hardness fluctuation of the material and dimensional deviation characteristics reflecting the thickness uniformity of the material. The stringency assessment module is used to obtain the quality constraint indicators of the current shearing task to be assigned and quantify them as a task stringency score. The risk assessment module is used to perform multi-dimensional correlation analysis based on the real-time operating status parameters, the local physical characteristic parameters, and the task severity score, and generate a potential processing risk assessment value for the silicon steel sheet to be processed and the current shearing task to be assigned for each shearing unit. The allocation decision module is used to execute task allocation decisions based on the potential processing risk assessment values ​​corresponding to each of the shearing units.

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