Energy consumption control method and system for oxygen-free copper rod drawing process

Abstract

The present application relates to the technical field of metal processing control, and particularly relates to a method and system for energy consumption control of oxygen-free copper rod up-drawing process, which comprises the following steps: dividing the up-drawing process and defining core energy efficiency parameters of each stage; collecting the core energy efficiency parameters of each stage in real time to generate a core energy consumption parameter set; comparing the core energy consumption parameter set with a preset energy consumption benchmark model to identify abnormal patterns existing in each stage; dynamically allocating and adjusting weights to generate a composite optimization instruction of temperature-speed-flow; correcting energy consumption benchmark model parameters based on historical control data to generate an energy efficiency control rule set that is adaptive to process state changes; and performing real-time dynamic control on energy consumption of each stage according to the updated energy efficiency control rule set and the composite optimization instruction. Through the present application, the energy efficiency imbalance and static rule rigidity problems caused by isolated control of multi-stage parameters in traditional processes are effectively solved, adaptive parameter collaboration in the whole process is realized, ton electricity consumption is significantly reduced, and quality stability is improved.

Description

Technical Field

[0001] This invention relates to the field of metal processing control technology, and in particular to an energy consumption control method and system for the upward drawing process of oxygen-free copper rods. Background Technology

[0002] In the continuous casting process of oxygen-free copper rods, energy consumption control and process stability are key factors restricting production efficiency and product quality. Traditional control methods often adopt a phased independent control strategy, treating parameters of melting, traction, and cooling as isolated variables for management. While this fragmented control mode can achieve local parameter optimization, it ignores the dynamic coupling effect of parameters at each stage. For example, increasing the traction speed will change the flow characteristics of molten copper, which in turn affects the temperature field distribution of the melting furnace. The adjustment of cooling flow rate must be coordinated with the rate of release of residual heat from melting; otherwise, it is easy to cause thermodynamic imbalance. Due to the lack of a cross-stage dynamic compensation mechanism, the existing technology frequently experiences sharp increases in energy consumption and energy efficiency fluctuations.

[0003] Current systems generally rely on static rule bases to adjust process parameters. Their control logic is based on idealized steady-state models and cannot adapt to dynamic conditions such as fluctuations in raw material properties and environmental disturbances. When the purity of raw copper deviates or there are sudden changes in ambient temperature and humidity, the adjustment instructions from the fixed rule base are misaligned with actual needs, leading to accumulated control deviations. For example, when the purity of raw materials decreases, the optimal ratio of melting temperature to cooling flow rate changes nonlinearly, but the static rules still use the preset parameter combination, resulting in a sharp decrease in cooling efficiency and an increase in energy consumption. In addition, the mechanical characteristic drift caused by equipment aging is not included in the control model, further exacerbating the mismatch of process parameters. This rigid control mode not only limits the potential for energy efficiency improvement but also leads to product quality fluctuations, making it difficult to meet the stringent requirements of high-end applications for the surface finish and crystal uniformity of copper rods. Summary of the Invention

[0004] This invention provides an energy consumption control method and system for the oxygen-free copper rod upward drawing process, thereby effectively solving the problems pointed out in the background art.

[0005] To achieve the above objectives, the technical solution adopted by the present invention is as follows: An energy consumption control method for an oxygen-free copper rod up-drawing process includes: The upward drawing process is divided into a melting stage, a traction stage, and a cooling stage, and the core energy efficiency parameters for each stage are defined. The core energy efficiency parameters at each stage are collected in real time and the data is synchronized to generate a set of core energy consumption parameters. The core energy consumption parameter set is compared with the preset energy consumption benchmark model to identify abnormal patterns in each stage; Dynamically allocate and adjust weights based on the type of abnormal mode to generate composite optimization instructions for temperature, speed, and flow rate; Based on historical control data, the parameters of the energy consumption benchmark model are corrected to generate a set of energy efficiency control rules that adapt to changes in process conditions. Based on the updated set of energy efficiency control rules and the composite optimization instructions of temperature-speed-flow, the energy consumption of each stage of the upward extraction process is dynamically controlled in real time.

[0006] Furthermore, the core energy consumption parameter set is compared with a preset energy consumption benchmark model to identify abnormal patterns at each stage, including: The melting temperature gradient data is smoothed using a sliding window, and the proportion of high-frequency harmonic energy in the traction power is extracted through time-frequency transformation. The smoothed temperature gradient is compared with the dynamic threshold baseline, and the high-frequency harmonic energy ratio is matched with the equipment loss feature library to simultaneously calculate the deviation rate of cooling efficiency relative to the historical average. The processed parameter set is input into the energy consumption benchmark model for multi-dimensional comparison. When the comparison result of any dimension exceeds the preset ratio range of the corresponding benchmark value, the anomaly marker of the corresponding stage is triggered.

[0007] Furthermore, after triggering the exception flag for the corresponding stage, it also includes: Control resources are allocated according to priority based on the type of abnormal stage, and process parameter compensation instructions for cross-stage linkage are generated. The effectiveness of the compensation instruction is verified based on the real-time collected energy consumption data. When the energy consumption reduction rate per unit product does not reach the preset threshold, the parameter association weights in the energy efficiency control rule set are reversed. After valid parameter compensation instructions are bound to the current process state characteristics, they are stored in the energy efficiency control rule set.

[0008] Furthermore, adjustment weights are dynamically allocated based on the type of abnormal mode, generating composite optimization instructions for temperature, speed, and flow rate, including: Adjustment weights are assigned based on the type of abnormal mode, with temperature parameters being the main adjustment term for smelting abnormalities, speed parameters being the main adjustment term for traction abnormalities, and flow rate parameters being the main adjustment term for cooling abnormalities. Based on the adjustment amplitude of the main control term and the dynamic constraint relationship between process parameters, a coordinated compensation amount for temperature, speed, and flow rate is generated. The collaborative compensation amounts are synthesized into composite optimization instructions according to weight ratios. The effectiveness of the instructions is verified by real-time energy consumption data, and the weight allocation strategy is iteratively updated based on the verification results.

[0009] Furthermore, based on historical control data, the parameters of the energy consumption benchmark model are revised to generate a set of energy efficiency control rules that adapt to changes in process conditions, including: Accumulate temperature compensation, speed correction coefficient and flow adjustment amplitude from historical control records, and screen effective control records that meet the preset energy consumption reduction conditions; Extract the correlation patterns between process parameters in the effective control records and update the parameters of the energy consumption benchmark model; Based on the updated energy consumption benchmark model, a subset of energy efficiency control rules related to process state characteristics is generated; The subset of energy efficiency control rules is matched with real-time process status data to generate an optimized parameter set adapted to the current operating conditions.

[0010] Furthermore, based on the updated energy efficiency control rule set and the temperature-speed-flow composite optimization instructions, the energy consumption of each stage of the upward extraction process is dynamically controlled in real time, including: Identify the current process stage and call the corresponding subset of rules from the energy efficiency control rule set; Based on the dynamic constraint relationships in the rule subset and the collaborative compensation amount in the composite optimization instruction, the process parameter adjustment instruction for the current stage is generated. The execution results of parameter adjustment commands are fed back as historical control data to the energy efficiency control rule set, driving the update and iteration of the rule subset.

[0011] Furthermore, the method also includes: When generating the composite optimization instruction, the impact of parameter adjustment on the constraints of each dimension is calculated simultaneously. When any dimension is detected to exceed the safety threshold, a dynamic limiting mechanism for parameter adjustment is automatically triggered. Historical control records that meet multi-dimensional constraints are marked as high-value data, and the decision weight of the high-value data is increased in the energy efficiency control rule set.

[0012] Furthermore, when generating the composite optimization instruction, the impact of parameter adjustments on the constraints of each dimension is calculated simultaneously, including: A multi-dimensional prediction model is established based on the dynamic coupling relationship between process parameters; The temperature compensation amount, speed correction coefficient, and flow rate adjustment amplitude in the composite optimization command are input into the prediction model, and the prediction deviation values ​​of each dimension of the constraint conditions are output. When the prediction deviation exceeds the safety threshold of the corresponding dimension, a dynamic limiting instruction for parameter adjustment is generated.

[0013] An energy consumption control system for an oxygen-free copper rod up-drawing process includes: The core parameter definition module divides the upward drawing process into a melting stage, a traction stage, and a cooling stage, and defines the core energy efficiency parameters for each stage: The core parameter acquisition module collects the core energy efficiency parameters at each stage in real time, synchronizes the data, and generates a core energy consumption parameter set. The abnormal pattern identification module compares the core energy consumption parameter set with the preset energy consumption benchmark model to identify abnormal patterns in each stage. The optimization instruction generation module dynamically allocates and adjusts weights according to the abnormal mode type to generate composite optimization instructions for temperature, speed, and flow rate. The rule set generation module corrects the parameters of the energy consumption benchmark model based on historical control data and generates an energy efficiency control rule set that adapts to changes in process conditions. The real-time energy consumption control module performs real-time dynamic control of energy consumption at each stage of the upward extraction process based on the updated energy efficiency control rule set and the composite optimization instructions of temperature-speed-flow.

[0014] Furthermore, the abnormal pattern recognition module includes: The temperature steady-state extraction unit performs sliding window smoothing on the melting temperature gradient data, and extracts the proportion of high-frequency harmonic energy of the traction power through time-frequency transformation. The threshold feature registration unit compares the smoothed temperature gradient with the dynamic threshold baseline and matches the high-frequency harmonic energy ratio with the equipment loss feature library, and simultaneously calculates the deviation rate of cooling efficiency relative to the historical average. The abnormal stage marking unit inputs the processed parameter set into the energy consumption benchmark model for multi-dimensional comparison. When the comparison result of any dimension exceeds the preset proportion range of the corresponding benchmark value, the abnormal stage is marked.

[0015] The technical solution of this invention can achieve the following technical effects: It effectively solves the problems of energy efficiency imbalance and static rule rigidity caused by isolated parameter control in multiple stages of traditional processes, realizes adaptive parameter coordination throughout the process, significantly reduces power consumption per ton and improves quality stability. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 A flowchart illustrating an energy consumption control method for an oxygen-free copper rod upward drawing process; Figure 2 A flowchart illustrating the process for identifying anomalous patterns at each stage; Figure 3 A flowchart illustrating the steps following the triggering of an exception flag in the corresponding stage; Figure 4 A flowchart illustrating the process of generating a composite optimization command for temperature, velocity, and flow rate; Figure 5 A flowchart illustrating the process for generating an energy efficiency control rule set that adapts to changes in process conditions; Figure 6 A flowchart illustrating the real-time dynamic control of energy consumption at each stage of the upward drawing process. Figure 7 This is a flowchart illustrating the subsequent optimization steps of an energy consumption control method for an oxygen-free copper rod upward drawing process. Figure 8 A flowchart illustrating the process of synchronously calculating the impact of parameter adjustments on constraints in each dimension. Detailed Implementation

[0018] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0019] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0020] Example 1 like Figure 1 As shown, this invention provides an energy consumption control method for the oxygen-free copper rod upward drawing process, the method comprising: S1: Divide the upward drawing process into a melting stage, a traction stage, and a cooling stage, and define the core energy efficiency parameters for each stage; Specifically, in the smelting stage, the main focus is on temperature control. Key energy efficiency parameters at this stage include smelting temperature, heating power, and heating rate. By monitoring the smelting temperature gradient and the rate of temperature change in real time, the stability and efficiency of the smelting process can be ensured, thereby reducing energy waste and avoiding excessive energy consumption. In the traction stage, the core energy efficiency parameters focus on traction speed, traction power, and mechanical load during traction. The optimization goal at this stage is to improve the stability of the traction speed while ensuring efficient transmission of traction power to avoid affecting overall energy efficiency due to excessive energy loss. By precisely controlling the energy transmission during traction, work efficiency can be significantly improved, and unnecessary energy waste can be reduced. In the cooling stage, key energy efficiency parameters include cooling water flow rate, cooling temperature, and cooling rate. By optimizing the cooling flow rate and temperature gradient, the cooling effect of the copper rod can be effectively controlled, avoiding energy waste caused by uneven or excessive cooling. Energy efficiency control at this stage plays a crucial role in ensuring the quality of the copper rod and reducing energy loss.

[0021] S2: Collect core energy efficiency parameters at each stage in real time, synchronize the data, and generate a set of core energy consumption parameters; Specifically, by collecting core energy efficiency parameters (such as temperature, speed, power, and flow rate) in real time during the smelting, traction, and cooling stages, and processing these data synchronously, it is possible to accurately grasp the operating status of the process at any time. The synchronization of these data can eliminate the impact that may be caused by time differences between different stages, ensuring the consistency and accuracy of the overall data. The energy efficiency information of each stage is summarized into a unified parameter set, which serves as the basis for subsequent energy efficiency assessment and optimization decisions. This parameter set not only provides the necessary data support for comparing and diagnosing abnormal patterns in the process, but also provides real-time feedback for the dynamic adjustment of the energy efficiency control model, thereby achieving more precise energy efficiency management and regulation.

[0022] S3: Compare the core energy consumption parameter set with the preset energy consumption benchmark model to identify abnormal patterns in each stage; Specifically, by comparing real-time collected energy efficiency data with a preset benchmark model, deviations or anomalies in the process can be detected in a timely manner. This comparative analysis helps identify potential energy efficiency problems in the process, providing a basis for subsequent adjustments and optimizations. Comparing the core energy consumption parameter set with the preset energy consumption benchmark model can identify which stages' energy consumption indicators exceed the normal range or differ significantly from historical data. These deviations may be caused by factors such as equipment failure, improper process parameter settings, or changes in the external environment. By identifying these abnormal patterns, problems can be detected in advance, and measures can be taken to prevent further energy efficiency losses or equipment damage. For example, if parameters such as melting temperature, traction power, and cooling flow rate deviate from the benchmark value at each stage, it may indicate an abnormality in the current process state. Through this comparative analysis, the problematic stage can be accurately located, providing specific directions for subsequent optimization and adjustments, ensuring the stable and efficient operation of the process.

[0023] S4: Dynamically allocate and adjust weights according to the abnormal mode type to generate composite optimization instructions for temperature, speed and flow rate; Specifically, each anomaly mode may affect different process parameters. For example, anomalies in the smelting stage typically manifest as improper temperature control, while those in the traction stage may manifest as unstable traction speed or power. By dynamically allocating adjustment weights according to the anomaly mode type, it can be ensured that adjustments to each parameter are focused on the most critical process stages, thereby efficiently and accurately solving energy efficiency problems. The generated temperature-speed-flow composite optimization command is based on this weight allocation, comprehensively considering the dynamic relationships and constraints between various parameters. The generated optimization command can coordinately adjust these three process parameters to achieve the best energy efficiency control effect. In this way, not only can the energy efficiency of each stage of the process be optimized, but also maximum energy savings and process efficiency improvements can be achieved while ensuring product quality.

[0024] S5: Based on historical control data, correct the parameters of the energy consumption benchmark model and generate a set of energy efficiency control rules that adapt to changes in process conditions; Specifically, during the production process, the process status may change due to equipment aging, changes in raw materials, environmental factors, etc. Therefore, it is necessary to regularly revise the parameters of the energy consumption benchmark model to adapt to different process statuses. The core function of this step is to use historical control data as feedback information, compare the energy efficiency performance under different process statuses, and revise the key parameters in the model. Through such revisions, the energy consumption benchmark model can better reflect the actual needs of the current process and avoid energy efficiency instability or over-adjustment due to inappropriate parameter settings.

[0025] S6: Based on the updated energy efficiency control rule set and the composite optimization instructions of temperature-speed-flow, the energy consumption of each stage of the upward extraction process is dynamically controlled in real time.

[0026] Specifically, real-time dynamic control ensures that process parameters such as temperature, speed, and flow rate remain within optimal ranges under different process conditions, effectively reducing unnecessary energy waste. The updated energy efficiency control rule set, combined with composite optimization instructions, ensures that energy efficiency adjustments at each stage of the process are precise and timely. This real-time feedback-based control method can quickly respond to sudden changes or anomalies in the process, dynamically adjust process parameters, avoid energy waste, and optimize the overall process flow.

[0027] This invention effectively solves the problems of energy efficiency imbalance and static rule rigidity caused by isolated parameter control in multi-stage processes in traditional processes, and achieves adaptive parameter coordination throughout the entire process, significantly reducing power consumption per ton and improving quality stability.

[0028] As a preferred embodiment of the above, such as Figure 2 As shown, in step S3, the core energy consumption parameter set is compared with the preset energy consumption benchmark model to identify abnormal patterns in each stage, including: S31: Perform sliding window smoothing on the melting temperature gradient data, and extract the proportion of high-frequency harmonic energy of traction power through time-frequency transformation; S32: Compare the smoothed temperature gradient with the dynamic threshold baseline, match the proportion of high-frequency harmonic energy with the equipment loss feature library, and simultaneously calculate the deviation rate of cooling efficiency relative to the historical average. S33: Input the processed parameter set into the energy consumption benchmark model for multi-dimensional comparison. When the comparison result of any dimension exceeds the preset ratio range of the corresponding benchmark value, the anomaly marker of the corresponding stage is triggered.

[0029] Specifically, in step S31, the melting temperature gradient data is first smoothed using a sliding window. The purpose of this smoothing process is to remove high-frequency noise from the temperature data by calculating the average value within a certain range around the data points, making the data smoother and more stable. This helps to eliminate external fluctuations or measurement errors and obtain a more accurate temperature change trend. Next, the proportion of high-frequency harmonic energy in the traction power is extracted using time-frequency transformation technology. Time-frequency transformation is a method of converting a signal from the time domain to the frequency domain. In this way, the high-frequency components in the traction power (usually fluctuations caused by unstable equipment operation or malfunctions) can be identified, thereby effectively quantifying the impact of these fluctuations on energy efficiency. In step S32, the smoothed temperature gradient data is first compared with the dynamic threshold baseline. The dynamic threshold baseline is a reference value set based on historical data and process requirements, and is usually adjusted as process conditions change. The purpose of comparing the temperature gradient with the baseline is to check whether the current temperature change exceeds the allowable range. If it exceeds the predetermined range, it may indicate a process anomaly. Next, the proportion of high-frequency harmonic energy in the traction power is matched with the equipment loss feature library. The equipment loss feature library contains the energy efficiency characteristics of the equipment during normal operation. Comparing the proportion of high-frequency harmonic energy with the features in the library can help identify whether there is equipment loss or anomaly. Finally, the deviation rate of cooling efficiency from the historical average is calculated. By comparing the deviation of the current cooling efficiency with the historical data, the effectiveness of the cooling process can be evaluated, and whether there is energy waste due to insufficient or uneven cooling. In step S33, the processed parameter set (including the smoothed temperature gradient, the proportion of high-frequency harmonic energy in traction power, and the cooling efficiency deviation) is input into the energy consumption benchmark model for multi-dimensional comparison. The energy consumption benchmark model is a model built from historical data and empirical values ​​to predict energy efficiency parameters under normal conditions. Through multi-dimensional comparison, the system compares the current value of each parameter with the benchmark value. If the comparison result of any dimension exceeds the preset ratio range, it indicates that there is an abnormality in the energy efficiency at this stage, which may require further optimization or adjustment. The triggered abnormality flag will indicate which part of the process has a problem, providing a basis for subsequent adjustments.

[0030] As a preferred embodiment of the above, such as Figure 3 As shown, after the exception flag for the corresponding stage is triggered, the following also applies: A10: Allocate control resources according to priority based on the type of abnormal stage, and generate process parameter compensation instructions for cross-stage linkage; A20: Verify the effectiveness of the compensation instruction based on real-time collected energy consumption data. When the energy consumption reduction rate per unit product does not reach the preset threshold, reverse the parameter association weights in the energy efficiency control rule set. A30: After binding the verified and valid parameter compensation instructions with the current process state characteristics, store them in the energy efficiency control rule set.

[0031] Specifically, firstly, in step A10, the system allocates control resources according to the identified anomaly type (e.g., anomalies in the melting, traction, or cooling stages) in priority order. Different anomaly types are prioritized based on their impact on the overall process flow, with priority given to anomalies that have a greater impact on energy efficiency. For example, temperature anomalies in the melting stage may have a significant impact on the entire process, so the melting stage will be allocated higher control resources. Based on the allocated priority, the system generates cross-stage linkage process parameter compensation instructions to ensure that parameter adjustments in each process stage can cooperate and coordinate to achieve overall optimization. For example, anomalies in the traction process may need to be compensated for by adjusting the melting temperature or cooling flow rate to maintain the balance of the entire process. Next, in step A20, the effectiveness of the generated compensation instructions is verified by real-time collected energy consumption data, the energy consumption reduction rate per unit product is calculated, and the compensation is checked. The system determines whether the compensation instruction successfully reduced the energy consumption per unit of product. If the energy consumption reduction rate does not reach the preset threshold, it indicates that the compensation instruction has failed to effectively solve the problem. In this case, a reverse correction is performed, adjusting the parameter association weights in the energy efficiency control rule set. This involves re-correcting the priority and adjustment range in the rules to provide more accurate compensation instructions for similar future anomalies. In this way, the system continuously optimizes the control strategy to improve the effectiveness of future compensation instructions. Finally, in step A30, the verified and effective parameter compensation instructions are bound to the current process state characteristics. This combines the compensation instructions with real-time parameters such as temperature, speed, and flow rate in the process to ensure that the instructions can accurately adapt to the current production conditions. After binding, the compensation instructions are stored in the energy efficiency control rule set and become part of the rule set. In this way, when similar anomalies occur in the future, a rapid response can be made based on the stored instructions, thereby achieving more efficient and accurate energy efficiency control.

[0032] As a preferred embodiment of the above, such as Figure 4 As shown, step S4 involves dynamically allocating adjustment weights based on the abnormal mode type to generate a composite optimization instruction for temperature, speed, and flow rate, including: S41: Assign adjustment weights based on the abnormal mode type, where smelting abnormality is mainly adjusted by temperature parameter, traction abnormality is mainly adjusted by speed parameter, and cooling abnormality is mainly adjusted by flow rate parameter. S42: Generate the coordinated compensation amount of temperature, speed, and flow rate based on the adjustment amplitude of the main control term and the dynamic constraint relationship between process parameters; S43: Combine the collaborative compensation amounts into composite optimization instructions according to the weight ratio, verify the effectiveness of the instructions through real-time energy consumption data, and iteratively update the weight allocation strategy based on the verification results.

[0033] Specifically, firstly, in step S41, adjustment weights are assigned to each process parameter according to different anomaly modes. Specifically, when anomalies occur in the melting stage, the temperature parameter is given a higher adjustment weight because temperature control is crucial during melting. When anomalies occur in the traction stage, the speed parameter becomes the primary adjustment term because traction speed has a significant impact on energy efficiency. And when anomalies occur in the cooling stage, the flow rate parameter is the main adjustment target because cooling flow rate directly determines the cooling effect and energy efficiency optimization. Thus, by classifying anomalies at different stages, it can be ensured that the core issues at each stage are addressed first. Next, in step S42, based on the adjustment magnitude of the primary adjustment term and the dynamic constraint relationships between each process parameter, a process is generated... The coordinated compensation of temperature, speed, and flow rate is crucial. Adjustments to the melting temperature may affect the traction speed, and changes in the traction speed may affect the cooling flow rate. Therefore, the interdependence between various process parameters needs to be considered. Based on these constraints, the coordinated compensation ensures that adjustments are coordinated, preventing imbalances in other parameters caused by adjusting a single parameter, thus optimizing the overall energy efficiency control. Finally, in step S43, these coordinated compensation amounts are synthesized into a composite optimization command for temperature, speed, and flow rate according to a pre-set weight ratio. The effectiveness of these commands is verified using real-time energy consumption data. If energy consumption is effectively reduced, the command continues to execute; if the energy consumption reduction does not meet expectations, adjustments are made based on the verification results, and the weight allocation strategy is updated.

[0034] As a preferred embodiment of the above, such as Figure 5 As shown, step S5 involves correcting the energy consumption baseline model parameters based on historical control data to generate an energy efficiency control rule set that adapts to changes in process conditions, including: S51: Accumulate temperature compensation, speed correction coefficient and flow adjustment amplitude from historical control records, and filter out effective control records that meet the preset energy consumption reduction conditions; S52: Extract the correlation patterns between process parameters in the effective control records and update the parameters of the energy consumption benchmark model; S53: Based on the updated energy consumption benchmark model, generate a subset of energy efficiency control rules related to process state characteristics; S54: Match a subset of energy efficiency control rules with real-time process status data to generate an optimized parameter set adapted to the current operating conditions.

[0035] Specifically, in step S51, it is first necessary to accumulate data such as temperature compensation, speed correction coefficient, and flow rate adjustment amplitude from historical control records. These historical records reflect the adjustment of various parameters under different process conditions. Next, effective control records that meet the preset energy consumption reduction conditions are selected. Effective control records refer to those adjustments that can significantly improve energy efficiency, and these records provide reliable data support for subsequent correction of the benchmark model. In step S52, the correlation patterns between process parameters are extracted from the selected effective control records. This step helps to identify which parameter adjustments can effectively affect energy efficiency by analyzing the relationships between different parameters. For example, temperature and speed adjustments may have a direct impact on traction power and cooling efficiency. By extracting these patterns, a basis can be provided for updating the energy consumption benchmark model, thereby optimizing the model parameters to better reflect the energy efficiency changes in the actual process. Then, in step S53, based on the updated energy consumption benchmark model, a subset of energy efficiency control rules related to process state characteristics is generated. These rule subsets reflect how to achieve optimal energy efficiency by adjusting specific process parameters (such as temperature, speed, and flow rate) under different process states. The updated model generates more accurate and adaptable control rules. Finally, in step S54, the generated subset of energy efficiency control rules is matched with real-time process status data. The purpose of this step is to select the most suitable set of optimized parameters for adjustment based on the current process status. Real-time process status data includes parameters such as current temperature, speed, and flow rate. By comparing this data with the subset of control rules, an optimized parameter set adapted to the current operating conditions is generated and applied to actual production to ensure maximum energy efficiency.

[0036] As a preferred embodiment of the above, such as Figure 6 As shown, in step S6, based on the updated energy efficiency control rule set and the composite optimization instructions of temperature-speed-flow rate, the energy consumption of each stage of the upward extraction process is dynamically controlled in real time, including: S61: Identify the current process stage and call the corresponding subset of rules from the energy efficiency control rule set; S62: Based on the dynamic constraint relationships in the rule subset and the collaborative compensation amount in the composite optimization instructions, generate the process parameter adjustment instructions for the current stage; S63: The execution results of parameter adjustment commands are fed back to the energy efficiency control rule set as historical control data, driving the update and iteration of the rule subset.

[0037] Specifically, in step S61, it is first necessary to identify the current process stage, which could be the melting stage, traction stage, or cooling stage. Once the current process stage is determined, the rule subset corresponding to that stage is retrieved from the energy efficiency control rule set. These rule subsets contain energy efficiency control strategies for specific process stages, providing specific operational guidance for adjusting process parameters. Next, in step S62, based on the dynamic constraint relationships in the rule subsets and the collaborative compensation amount in the composite optimization instructions, process parameter adjustment instructions for the current stage are generated. Dynamic constraint relationships exist between parameters (such as temperature, speed, and flow rate) in each process stage. These relationships determine the adjustment range and priority of each parameter. Based on the collaborative compensation amount in the composite optimization instructions, the system generates specific adjustment instructions. These instructions ensure that each parameter remains coordinated during the adjustment process, thereby optimizing energy efficiency. Finally, in step S63, the execution results of the parameter adjustment instructions are fed back to the energy efficiency control rule set as historical control data. The purpose of this step is to use the execution results as feedback to ensure that the rule set can be updated and optimized according to the actual operating conditions. Through this feedback mechanism, the energy efficiency control rule set will be continuously iterated and updated, so that when similar process conditions occur in the future, process parameters can be adjusted more accurately to improve energy efficiency.

[0038] As a preferred embodiment of the above, such as Figure 7 As shown, the method also includes: B10: When generating composite optimization instructions, simultaneously calculate the impact of parameter adjustments on constraints in each dimension; B20: When any dimension is detected to exceed the safety threshold, a dynamic limiting mechanism for parameter adjustment is automatically triggered; B30: Mark historical control records that meet multi-dimensional constraints as high-value data and increase the decision weight of high-value data in the energy efficiency control rule set.

[0039] Specifically, in step B10, when generating composite optimization instructions, it is necessary to simultaneously calculate the impact values ​​of parameter adjustments on constraints in each dimension. The purpose of this step is to evaluate the impact of adjustments to process parameters such as temperature, speed, and flow rate on other process parameters and overall energy efficiency, ensuring that each adjustment is within the safe range of each dimension. By calculating these impact values, a more precise adjustment scheme can be provided for subsequent control instructions. Then, in step B20, when it is detected that the adjustment of any dimension exceeds the safe threshold, a dynamic limiting mechanism for parameter adjustment amplitude will be automatically triggered. The purpose of this mechanism is to ensure that during the adjustment process, any change in a process parameter will not excessively affect other parameters or cause process instability. By setting dynamic limits, the adjustment range can be flexibly adjusted according to the current process status and the relationship between parameters, thereby avoiding the adverse consequences of over-adjustment and ensuring the safe and stable operation of the process. Finally, in step B30, historical control records that meet multi-dimensional constraints are marked as high-value data. These data represent control records that can effectively improve energy efficiency in actual process operations and have strong reference value. Marking these high-value data will increase their decision weight in the energy efficiency control rule set. This means that when encountering similar process conditions in the future, these high-value data will be referenced first, thereby making more accurate parameter adjustments and improving the accuracy and stability of energy efficiency control.

[0040] As a preferred embodiment of the above, such as Figure 8 As shown, in step B10, when generating the composite optimization instruction, the impact of parameter adjustments on the constraints of each dimension is calculated simultaneously, including: B11: Establish a multi-dimensional prediction model based on the dynamic coupling relationship between process parameters; B12: Input the temperature compensation amount, speed correction coefficient and flow adjustment amplitude in the composite optimization command into the prediction model, and output the prediction deviation value of each dimension of constraint conditions; B13: When the prediction deviation exceeds the safety threshold of the corresponding dimension, a dynamic limit instruction for parameter adjustment is generated.

[0041] Specifically, firstly, in step B11, a multi-dimensional prediction model is established based on the dynamic coupling relationship between process parameters. This model considers the interdependence between process parameters such as temperature, speed, and flow rate, analyzing how changes in these parameters affect changes in other parameters, and their combined impact on overall process efficiency and energy efficiency. The purpose of this step is to provide accurate prediction basis for subsequent adjustments, ensuring that the adjustments of each parameter do not exceed reasonable ranges. Next, in step B12, the temperature compensation, speed correction coefficient, and flow rate adjustment amplitude from the composite optimization command are input into the prediction model. The model calculates these input data and outputs the prediction deviation values ​​for each dimension of the constraints. The value indicates whether changes in process parameters such as temperature, speed, and flow rate during the adjustment process will cause deviations from the constraints in each dimension, and whether such deviations exceed the acceptable range. In this way, the impact of each adjustment on process stability can be understood in advance. Finally, in step B13, when the predicted deviation value exceeds the safety threshold of the corresponding dimension, a dynamic limiting instruction for the parameter adjustment range is generated. If the prediction result shows that the adjustment of a certain dimension will exceed the safe range, the limiting instruction will control the adjustment range to prevent excessive adjustment from having a negative impact on the process or energy efficiency. Through this dynamic limiting mechanism, it can be ensured that the process parameters are always kept within a safe and stable range during complex process adjustments.

[0042] Example 2 Based on the same inventive concept as the energy consumption control method for the oxygen-free copper rod upward drawing process in the foregoing embodiments, the present invention also provides an energy consumption control system for the oxygen-free copper rod upward drawing process, the system comprising: The core parameter definition module divides the upward drawing process into a melting stage, a traction stage, and a cooling stage, and defines the core energy efficiency parameters for each stage: The core parameter acquisition module collects core energy efficiency parameters at each stage in real time, synchronizes the data, and generates a core energy consumption parameter set. The abnormal pattern recognition module compares the core energy consumption parameter set with the preset energy consumption benchmark model to identify abnormal patterns in each stage. The optimization instruction generation module dynamically allocates and adjusts weights according to the abnormal mode type to generate composite optimization instructions for temperature, speed, and flow rate. The rule set generation module corrects the parameters of the energy consumption benchmark model based on historical control data and generates an energy efficiency control rule set that adapts to changes in process conditions. The real-time energy consumption control module dynamically controls the energy consumption of each stage of the upward extraction process based on the updated energy efficiency control rule set and the composite optimization instructions of temperature-speed-flow.

[0043] The control system described above in this invention can effectively realize the energy consumption control method of oxygen-free copper rod upward drawing process, and the technical effects it can achieve are as described in the above embodiments, which will not be repeated here.

[0044] As a preferred embodiment of the above, the abnormal pattern recognition module includes: The temperature steady-state extraction unit performs sliding window smoothing on the melting temperature gradient data, and extracts the proportion of high-frequency harmonic energy of the traction power through time-frequency transformation. The threshold feature registration unit compares the smoothed temperature gradient with the dynamic threshold baseline and matches the proportion of high-frequency harmonic energy with the equipment loss feature library, and simultaneously calculates the deviation rate of cooling efficiency relative to the historical average. The anomaly stage marking unit inputs the processed parameter set into the energy consumption benchmark model for multi-dimensional comparison. When the comparison result of any dimension exceeds the preset ratio range of the corresponding benchmark value, the anomaly marking of the corresponding stage is triggered.

[0045] Similarly, the above-mentioned optimization schemes for the system can also achieve the optimization effects corresponding to the methods in Embodiment 1, which will not be repeated here.

[0046] Although this application has been described in conjunction with specific features and embodiments, it will be apparent that various modifications and combinations can be made therein without departing from the spirit and scope of this application. Accordingly, this specification and drawings are merely exemplary illustrations of the application as defined herein, and are to be considered as covering any and all modifications, variations, combinations, or equivalents within the scope of this application. Obviously, those skilled in the art can make various modifications and variations to this application without departing from the scope of this application. Therefore, if such modifications and variations fall within the scope of this application and its equivalents, this application intends to include such modifications and variations.

Claims

1. A method for controlling energy consumption in an oxygen-free copper rod upward drawing process, characterized in that, include: The upward drawing process is divided into a melting stage, a traction stage, and a cooling stage, and the core energy efficiency parameters for each stage are defined. The core energy efficiency parameters at each stage are collected in real time and the data is synchronized to generate a set of core energy consumption parameters. The core energy consumption parameter set is compared with a preset energy consumption benchmark model to identify abnormal patterns in each stage, including: The melting temperature gradient data is smoothed using a sliding window, and the proportion of high-frequency harmonic energy in the traction power is extracted through time-frequency transformation. The smoothed temperature gradient is compared with the dynamic threshold baseline, and the high-frequency harmonic energy ratio is matched with the equipment loss feature library to simultaneously calculate the deviation rate of cooling efficiency relative to the historical average. The processed parameter set is input into the energy consumption benchmark model for multi-dimensional comparison. When the comparison result of any dimension exceeds the preset proportion range of the corresponding benchmark value, the anomaly marker of the corresponding stage is triggered. Dynamically allocate adjustment weights based on the type of abnormal mode, and generate composite optimization instructions for temperature, speed, and flow rate, including: Adjustment weights are assigned based on the type of abnormal mode, with temperature parameters being the main adjustment term for smelting abnormalities, speed parameters being the main adjustment term for traction abnormalities, and flow rate parameters being the main adjustment term for cooling abnormalities. Based on the adjustment amplitude of the main control term and the dynamic constraint relationship between process parameters, a coordinated compensation amount for temperature, speed, and flow rate is generated. The collaborative compensation amounts are combined into a composite optimization instruction according to the weight ratio. The effectiveness of the instruction is verified by real-time energy consumption data, and the weight allocation strategy is iteratively updated based on the verification results. Based on historical control data, the parameters of the energy consumption benchmark model are revised to generate a set of energy efficiency control rules that adapt to changes in process conditions, including: Accumulate temperature compensation, speed correction coefficient and flow adjustment amplitude from historical control records, and screen effective control records that meet the preset energy consumption reduction conditions; Extract the correlation patterns between process parameters in the effective control records and update the parameters of the energy consumption benchmark model; Based on the updated energy consumption benchmark model, a subset of energy efficiency control rules related to process state characteristics is generated; The subset of energy efficiency control rules is matched with real-time process status data to generate an optimized parameter set adapted to the current operating conditions; Based on the updated energy efficiency control rule set and the temperature-speed-flow composite optimization instructions, the energy consumption of each stage of the upward extraction process is dynamically controlled in real time, including: Identify the current process stage and call the corresponding subset of rules from the energy efficiency control rule set; Based on the dynamic constraint relationships in the rule subset and the collaborative compensation amount in the composite optimization instruction, the process parameter adjustment instruction for the current stage is generated. The execution results of parameter adjustment commands are fed back as historical control data to the energy efficiency control rule set, driving the update and iteration of the rule subset.

2. The energy consumption control method for the oxygen-free copper rod upward drawing process according to claim 1, characterized in that, After triggering the exception flag for the corresponding stage, it also includes: Control resources are allocated according to priority based on the type of abnormal stage, and process parameter compensation instructions for cross-stage linkage are generated. The effectiveness of the compensation instruction is verified based on the real-time collected energy consumption data. When the energy consumption reduction rate per unit product does not reach the preset threshold, the parameter association weights in the energy efficiency control rule set are reversed. After valid parameter compensation instructions are bound to the current process state characteristics, they are stored in the energy efficiency control rule set.

3. The energy consumption control method for the oxygen-free copper rod upward drawing process according to claim 1, characterized in that, The method further includes: When generating the composite optimization instruction, the impact of parameter adjustment on the constraints of each dimension is calculated simultaneously. When any dimension is detected to exceed the safety threshold, a dynamic limiting mechanism for parameter adjustment is automatically triggered. Historical control records that meet multi-dimensional constraints are marked as high-value data, and the decision weight of the high-value data is increased in the energy efficiency control rule set.

4. The energy consumption control method for the oxygen-free copper rod upward drawing process according to claim 3, characterized in that, When generating the composite optimization instruction, the impact of parameter adjustments on constraints in each dimension is calculated simultaneously, including: A multi-dimensional prediction model is established based on the dynamic coupling relationship between process parameters; The temperature compensation amount, speed correction coefficient, and flow rate adjustment amplitude in the composite optimization command are input into the prediction model, and the prediction deviation values ​​of each dimension of the constraint conditions are output. When the prediction deviation exceeds the safety threshold of the corresponding dimension, a dynamic limiting instruction for parameter adjustment is generated.

5. An energy consumption control system for an oxygen-free copper rod upward drawing process, employing the energy consumption control method for the oxygen-free copper rod upward drawing process as described in any one of claims 1-4, characterized in that, include: The core parameter definition module divides the upward drawing process into the melting stage, the traction stage, and the cooling stage, and defines the core energy efficiency parameters for each stage. The core parameter acquisition module collects the core energy efficiency parameters at each stage in real time, synchronizes the data, and generates a core energy consumption parameter set. The abnormal pattern recognition module compares the core energy consumption parameter set with a preset energy consumption benchmark model to identify abnormal patterns present in each stage, including: The temperature steady-state extraction unit performs sliding window smoothing on the melting temperature gradient data, and extracts the proportion of high-frequency harmonic energy of the traction power through time-frequency transformation. The threshold feature registration unit compares the smoothed temperature gradient with the dynamic threshold baseline and matches the high-frequency harmonic energy ratio with the equipment loss feature library, and simultaneously calculates the deviation rate of cooling efficiency relative to the historical average. The abnormal stage marking unit inputs the processed parameter set into the energy consumption benchmark model for multi-dimensional comparison. When the comparison result of any dimension exceeds the preset proportion range of the corresponding benchmark value, the abnormal stage is marked. The optimization instruction generation module dynamically allocates and adjusts weights according to the type of abnormal mode, generating composite optimization instructions for temperature, speed, and flow. The rule set generation module corrects the parameters of the energy consumption benchmark model based on historical control data and generates an energy efficiency control rule set that adapts to changes in process conditions. The real-time energy consumption control module performs real-time dynamic control of energy consumption at each stage of the upward extraction process based on the updated energy efficiency control rule set and the composite optimization instructions of temperature-speed-flow.

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