Distributed wire drawing machine operation management method and system

Abstract

The invention belongs to the technical field of wire drawing machines, and particularly relates to a distributed wire drawing machine operation management method and system.The environment parameters of all nodes and current physical state data of materials are collected in real time, the environment parameter deviation value is calculated based on the environment parameter deviation value, and then a material performance fluctuation prediction value is generated; the temperature and humidity adjusting amount of the node is dynamically adjusted according to the prediction result so as to optimize the material yield and physical characteristics, the method can accurately capture the influence of the change of the environmental parameters on the material performance, the dynamic correction coefficient of the environmental parameters can be continuously updated through trend analysis, and the compensation precision and stability are further improved; therefore, the technical problem of unstable material yield caused by fluctuation of environmental parameters in a traditional method is solved, and the production stability and efficiency are remarkably improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of wire drawing machines, and in particular relates to a distributed wire drawing machine operation management method and system. Background Art

[0002] In the wire drawing process, environmental parameters such as temperature and humidity have a crucial impact on material properties. Traditional wire drawing machine operation and management methods typically rely on fixed environmental parameter setpoints and lack the ability to adapt to real-time environmental changes. These traditional methods generally control the temperature and humidity during the production process through preset standard environmental conditions in the hope of achieving optimal material yield and physical properties. However, due to the inevitable fluctuations in conditions such as temperature and humidity in actual production environments, this static control method often fails to ensure continuous and stable production quality, resulting in unstable material yields and substandard physical properties.

[0003] Existing solutions attempt to address these issues by adding environmental monitoring equipment and adjustment mechanisms. However, most of these approaches fail to accurately model and predict the complex relationships between environmental parameters and material states, making it impossible to effectively compensate for fluctuations in material properties caused by environmental changes. This not only limits production efficiency but also increases costs. Summary of the Invention

[0004] The purpose of the present invention is to provide a distributed wire drawing machine operation management method and system, which solves the technical problem of unstable material yield caused by fluctuations in environmental parameters in traditional methods, and significantly improves production stability and efficiency.

[0005] To achieve the above object, the present invention adopts the following technical solution: a distributed wire drawing machine operation management method, comprising the following steps: Collect the real-time environmental parameters of each node of the distributed wire drawing machine and the current physical state data of the material, the environmental parameters include temperature and humidity values; based on the temperature and humidity values, calculate the deviation between the node environmental parameters and the preset benchmark environmental parameters, and generate a material performance fluctuation prediction value according to the deviation and the current physical state data of the material; compare the prediction value with the material performance threshold to determine the temperature adjustment amount and humidity adjustment amount that the node needs to compensate, send the temperature adjustment amount and humidity adjustment amount to the node, and control its cooling system and dehumidification device operating parameters; monitor the material yield and physical property measured value after the node executes compensation, compare the physical property measured value with the preset standard value, calculate the compensation error and update the deviation; perform trend analysis on the compensation error and historical compensation data, generate a dynamic correction coefficient for the environmental parameter and store it in the node local database.

[0006] Preferably, the collection of real-time environmental parameters of each node of the distributed wire drawing machine and current physical state data of the material includes: Setting a sensor group at each node, and acquiring an initial environmental data set through the sensor group, wherein the data set includes temperature and humidity; Calculating a temperature-humidity ratio based on the temperature and humidity, and using the ratio to assess the degree of impact of the current environment on the material; Comparing the ratio with a pre-stored standard temperature and humidity ratio, and adjusting the sampling frequency of the sensor group if the difference exceeds a set threshold; The current physical state data of the material is continuously updated according to the adjusted sampling frequency to ensure that the data reflects the state changes of the material under the latest environmental conditions.

[0007] Preferably, calculating the deviation between the node environmental parameter and a preset reference environmental parameter based on the temperature value and the humidity value includes: Obtaining the temperature and humidity data collected by each node in real time, and calculating the environmental parameters of each node based on the temperature and humidity to form a current environmental parameter set; The current environmental parameter set is compared with the preset reference environmental parameters to calculate the deviation of each node, and the deviation is stored in the node local database.

[0008] Preferably, generating a material property fluctuation prediction value based on the deviation amount and the current physical state data of the material includes: Obtaining a deviation amount and current physical state data of the material, and defining a comprehensive influencing factor based on the deviation amount and the current physical state data of the material; The comprehensive impact factor is quoted and combined with the material performance change rate under similar conditions in historical data to calculate the predicted performance change rate; The predicted performance change rate value is stored and used as a material performance fluctuation prediction value to adjust the operating parameters of each node.

[0009] Preferably, comparing the predicted value with a material property threshold to determine the temperature adjustment amount and humidity adjustment amount required to be compensated for the node includes: Obtaining the predicted value of material property fluctuation and the preset material property threshold, calculating the difference; if the difference is greater than zero, it indicates that environmental parameter adjustment is required; The temperature adjustment amount and the humidity adjustment amount are calculated according to the difference.

[0010] Preferably, sending the temperature adjustment amount and the humidity adjustment amount to the node to control the operating parameters of the cooling system and the dehumidification device thereof includes: Calculate the cooling system energy adjustment value and the dehumidification device efficiency adjustment value based on the temperature adjustment amount and the humidity adjustment amount; The energy adjustment value and the efficiency adjustment value are converted into corresponding control instructions and sent to a control system of the corresponding node, and the control system adjusts the working state of the cooling system and the dehumidification device according to the control instructions.

[0011] Preferably, the material yield and the measured physical property value of the node after compensation are monitored, including: After the adjustment of the environmental parameters at each node is completed, a batch of materials is produced by starting the production line according to the working state of the cooling system and the dehumidification device corresponding to the control instructions, and the number of finished products and the total input are collected to calculate the material yield; The physical property value of the batch of materials is detected to obtain the physical property value, which is compared with the preset standard physical property value; The quality deviation degree is calculated according to the yield and the physical property value, and the deviation degree is recorded as the basis for evaluating the compensation effect.

[0012] Preferably, the measured physical property value is compared with the preset standard value, the compensation error is calculated, and the deviation amount is updated, including: The physical property value and the corresponding standard physical property value are obtained, the physical property deviation is calculated, and the gap between the physical property of the actually produced material and the expected standard is quantified; The compensation error of the current node is comprehensively evaluated by referring to the physical property deviation combined with the quality deviation degree, and the compensation error is obtained by comparing the trend of the physical property deviation and the historical average deviation; According to the compensation error combined with the deviation amount, a new deviation amount is updated.

[0013] Preferably, the compensation error is analyzed in trend with historical compensation data, an environmental parameter dynamic correction coefficient is generated and stored in a node local database, including: The calculated compensation error is obtained, and a set of past historical compensation error values is extracted from the node local database, and the average value of all compensation errors is calculated to measure the trend center of the overall compensation error; The average value and the set of historical compensation error values are referred to to calculate the environmental parameter dynamic correction coefficient; The environmental parameter dynamic correction coefficient is stored in the node local database together with a time stamp as basic data for adjustment, which is used to optimize the calculation of the temperature and humidity adjustment amount in the cycle using the correction coefficient.

[0014] In another aspect, the application provides a distributed wire drawing machine operation management system, including: An environmental data acquisition module is used to acquire real-time environmental parameters and current physical state data of materials of each node of the distributed wire drawing machine; An environmental parameter deviation and performance prediction module, configured to calculate the deviation between the node environmental parameter and a preset reference environmental parameter based on the temperature and humidity values, and generate a material performance fluctuation prediction value based on the deviation and the current physical state data of the material; a compensation measure formulation and execution module, configured to compare the predicted value with a material property threshold, determine a temperature adjustment amount and a humidity adjustment amount required to compensate the node, send the temperature adjustment amount and the humidity adjustment amount to the node, and control the operating parameters of the cooling system and the dehumidification device thereof; a compensation effect monitoring and error calculation module, configured to monitor the material yield and measured values ​​of physical properties after the compensation is performed on the node, compare the measured values ​​of the physical properties with preset standard values, calculate the compensation error, and update the deviation; The trend analysis and dynamic correction module is used to perform trend analysis on the compensation error and historical compensation data, generate dynamic correction coefficients of environmental parameters and store them in the node local database.

[0015] Technical effects and advantages of the present invention: Compared with the prior art, the distributed wire drawing machine operation management method and system proposed in the present invention have the following advantages: The present invention collects the environmental parameters of each node and the current physical state data of the material in real time, and calculates the environmental parameter deviation based on this, thereby generating a material performance fluctuation prediction value, and dynamically adjusts the temperature and humidity adjustment amount of the node according to the prediction result to optimize the material yield and physical properties. This method can not only accurately capture the impact of changes in environmental parameters on material performance, but also continuously update the dynamic correction coefficient of environmental parameters through trend analysis, further improving the compensation accuracy and stability; therefore, it solves the technical problem of unstable material yield caused by environmental parameter fluctuations in traditional methods, and significantly improves production stability and efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 This is a flow chart of the distributed wire drawing machine operation management method of the present invention; Figure 2 This is a block diagram of the distributed wire drawing machine operation management system of the present invention. DETAILED DESCRIPTION

[0017] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. The specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0018] The present invention provides Figure 1 The distributed wire drawing machine operation management method shown in the figure can not only accurately capture the impact of changes in environmental parameters on material properties, but also continuously update the dynamic correction coefficient of environmental parameters through trend analysis to further improve compensation accuracy and stability. The details are as follows:

[0019] In this embodiment, the distributed wire drawing machine operation management method includes the following steps: Step 1: Collect the real-time environmental parameters of each node of the distributed wire drawing machine and the current physical state data of the material; specifically, the following steps are included: A sensor group is set up at each node, and an initial environmental data set D_0 (including temperature T and humidity H) is obtained through the sensor group; Based on D_0, the temperature-humidity ratio R=T / H is calculated, and the R value is used to evaluate the degree of influence of the current environment on the material; this ratio is used to quantify the relationship between temperature and humidity under the current environmental conditions, and to preliminarily evaluate the degree of influence on the material.

[0020] The R value is compared with the pre-stored standard temperature-humidity ratio R_s. If |R-R_s|>Δ (Δ is the set threshold), the sensor group sampling frequency F is adjusted to F*(1+k*|R-R_s|), where k is a proportional constant, to increase the data collection density under abnormal conditions. When it is detected that the environmental conditions deviate significantly from the standard, increasing the sampling frequency can more carefully monitor environmental changes, promptly capture small fluctuations that may affect material properties, and improve response speed and control accuracy.

[0021] The current physical state data S of the material is continuously updated according to the adjusted sampling frequency F to ensure that S reflects the state changes of the material under the latest environmental conditions.

[0022] Step 2: Based on the temperature and humidity values, calculate the deviation between the node environmental parameters and the preset reference environmental parameters; specifically, the following steps are included: Acquire the temperature T and humidity H data collected by each node in real time, the data comes from the sensor group; Based on T and H, the environmental parameter P = T + H for each node is calculated to form the current environmental parameter set P_set, where P represents the environmental parameter, T is temperature, and H is humidity. This formula combines two key environmental factors into a single, easily manageable overall metric through simple addition.

[0023] Referencing the P_set and comparing it to the preset baseline environmental parameter P_base (derived from historical data analysis), the deviation of each node is calculated as ΔP = |P_set-P_base| / P_base*100%, and the degree of deviation is expressed as a percentage. This formula is used to quantify the degree of deviation of the current environment from the ideal state.

[0024] The ΔP value is stored in the node local database so that the ΔP can be referenced in subsequent steps to generate a material property fluctuation prediction value.

[0025] Step 3: Generate a material performance fluctuation prediction value based on the deviation and the current physical state data of the material; specifically comprising the following steps: The environmental parameter deviation ΔP and the material's current physical state data S of each node are extracted from the local database. These data are the basis for evaluating the impact of environmental changes on material performance.

[0026] Based on the ΔP and S, a comprehensive impact factor I = ΔP * S is defined to quantify the degree of influence of environmental parameter deviation on the material state; this factor combines the two aspects of environmental deviation and material response, providing a standard for measuring the intensity of the interaction between the two.

[0027] The predicted performance change rate (V_pred) is calculated by referencing the I value and combining it with the material performance change rate (V_his) under similar conditions in historical data using the formula V_pred = V_his + (I-I_min) / (I_max-I_min)*(V_his-V_min), where V_pred represents the predicted performance change rate, V_his represents the historical material performance change rate, I represents the comprehensive impact factor, I_min and I_max represent the historical minimum and maximum values ​​of the comprehensive impact factor, and V_min represents the minimum performance change rate. This formula uses linear interpolation to predict future performance trends based on the position of the current comprehensive impact factor relative to historical data.

[0028] The V_pred value is stored and used as a prediction value for material performance fluctuations in subsequent steps to adjust the operating parameters of each node. This step ensures the traceability and availability of all relevant data, supporting continuous optimization of the system.

[0029] Step 4: Compare the predicted value with the material performance threshold to determine the temperature adjustment amount and humidity adjustment amount required to compensate for the node; specifically, the following steps are included: Extract the material performance fluctuation prediction value V_pred from the system database and obtain the preset material performance threshold V_thres. These data are the basis for determining whether environmental parameters need to be adjusted.

[0030] According to V_pred and V_thres, the difference D_V = V_pred-V_thres is calculated, and if D_V is greater than 0, it indicates that the environmental parameter adjustment is needed to improve the material performance; this formula is used to evaluate whether the change of material performance under the current environment is within an acceptable range.

[0031] According to the D_V value, the temperature adjustment amount T_adj and the humidity adjustment amount H_adj are calculated using the formulas T_adj = k_T*D_V and H_adj = k_H*D_V, respectively, where k_T is the temperature adjustment coefficient and k_H is the humidity adjustment coefficient, which are used to quantify the degree of environmental parameter adjustment needed; these two formulas are used to determine the specific environmental parameter adjustment range needed to optimize material performance.

[0032] The T_adj and H_adj values are sent to the corresponding node to guide its cooling system and dehumidification device to operate according to the T_adj and H_adj values, in order to achieve the purpose of optimizing material performance.

[0033] Step five: send the temperature adjustment amount and humidity adjustment amount to the node to control its cooling system and dehumidification device operating parameters; specifically including the following steps:

[0034] Extract the calculated temperature adjustment amount T_adj and humidity adjustment amount H_adj from the system database. These data will be used to guide the specific adjustment of the cooling system and dehumidification device; According to the T_adj and H_adj values, the cooling system energy adjustment value E_T and the dehumidification device efficiency adjustment value E_H are calculated by the formulas E_T = T_adj*P_max / 100 and E_H = H_adj*Q_max / 100, where E_T represents the energy value that needs to be adjusted for the cooling system, E_H represents the efficiency value that needs to be adjusted for the dehumidification device, T_adj is the temperature adjustment amount, H_adj is the humidity adjustment amount, P_max is the maximum power output of the cooling system, and Q_max is the maximum efficiency output of the dehumidification device. These two formulas are used to convert the temperature and humidity adjustment amounts into specific device adjustment instructions; Convert the E_T and E_H values into corresponding control instructions C_T and C_H, and send them to the control system of the corresponding node, which adjusts the working state of the cooling system and dehumidification device according to the C_T and C_H; Monitor the adjusted cooling system output power P_out and dehumidification device actual efficiency Q_out to ensure that P_out = E_T and Q_out = E_H, in order to verify whether the environmental parameter adjustment is performed as expected. Through the actual monitoring of the adjusted parameters, the effectiveness of the environmental adjustment measures is confirmed, and possible deviations are discovered and corrected in a timely manner, ensuring that the production environment continuously meets the requirements.

[0035] Step 6: Monitoring the material yield and measured values ​​of physical properties after the compensation is performed on the node; specifically comprising the following steps: After the environmental parameters are adjusted at each node, the production line is started and a batch of materials is produced according to the working status of the cooling system and dehumidification device corresponding to the control instructions C_T and C_H; Collect the number of finished products N_fin and the total input N_in for a batch of materials and calculate the material yield rate Y = (N_fin / N_in) * 100%. This represents the finished product output efficiency as a percentage. Y represents the yield rate, N_fin is the number of finished products, and N_in is the total input. This formula is used to evaluate the actual output efficiency of materials during the production process.

[0036] Physical property tests are performed on the batch of materials to obtain physical property values ​​M_meas (such as strength, toughness, etc.), and compared with preset standard physical property values ​​M_std. By directly comparing the actual material properties produced with the ideal standard, it is possible to quickly determine whether the product quality meets the standard and the direction for further improvement.

[0037] Based on the yield rate Y and the physical property value M_meas, the quality deviation D_qual is calculated using the formula D_qual = |M_meas-M_std| + L*(100-Y), where D_qual represents the quality deviation, M_meas is the measured physical property value, M_std is the standard physical property value, L is the yield impact factor, and Y is the yield rate. This formula comprehensively considers the degree to which the material physical properties deviate from the standard and the impact of the yield rate on the overall quality. The D_qual value is recorded as the basis for evaluating the compensation effect.

[0038] Step 7: Compare the measured value of the physical property with the preset standard value, calculate the compensation error and update the deviation; specifically including the following steps: The measured values ​​of the physical properties of the current batch of materials, M_meas, are obtained from the testing equipment, and the corresponding standard physical property values, M_std, are extracted from the database. These data are the basis for evaluating production quality.

[0039] The physical property deviation (E_M) = M_meas - M_std is used to quantify the difference between the actual physical properties of the material produced and the expected standard. E_M represents the physical property deviation, M_meas is the measured value of the physical property, and M_std is the standard physical property value. This formula directly reflects the difference between actual production results and the expected target.

[0040] The physical property deviation E_M is referenced and combined with the quality deviation D_qual, and the compensation error Comp_E of the current node is calculated using the formula Comp_E=E_M+D_qual / K, where K is the quality deviation weight factor, which is used to adjust the influence of the quality deviation on the compensation error. This formula comprehensively considers both physical properties and overall quality deviation to fully evaluate compensation needs.

[0041] According to the compensation error Comp_E and the deviation ΔP, the new deviation ΔP_new is updated using the formula ΔP_new=ΔP+Comp_E*W_p, where W_p is the physical property weight factor used to adjust the degree of influence of the environmental parameter deviation on the physical property difference. This formula dynamically adjusts the environmental parameter deviation by introducing the compensation error to gradually approach the optimal production conditions.

[0042] The new deviation ΔP_new is stored in the node local database and used as the basic data for subsequent cyclic adjustments to guide the calculation of the next round of temperature and humidity adjustment amounts, ensuring that the optimal production conditions are gradually approached.

[0043] Step 8: Perform trend analysis on the compensation error and historical compensation data, generate dynamic correction coefficients for environmental parameters and store them in the node local database; specifically, the following steps are included: Obtain the compensation error E_M and extract the historical compensation error value set E_hist={E_1,E_2,...,E_N} for the past N times from the node local database; these data will be used for trend analysis to evaluate the overall compensation effect.

[0044] Calculate the average value of all compensation errors (Avg_E = (E_M + Σ_(i=1)^NE_i) / (N+1)) to measure the trend center of the overall compensation error. E_M is the current compensation error, and Σ_(i=1)^NE_i represents the sum of the past N historical compensation errors. This formula is used to determine the overall level of compensation error.

[0045] The Avg_E and E_hist are referenced to calculate the dynamic correction coefficient C_adj of the environmental parameters through the formula C_adj=1+((E_M-Avg_E) / Avg_E)*M, where M is the correction intensity factor used to adjust the degree of influence of the current error on the overall adjustment; this formula dynamically adjusts the correction intensity of the environmental parameters according to the degree of deviation of the current error from the average error.

[0046] The C_adj value is stored together with the timestamp in the node local database as basic data for reference in subsequent adjustments, so that the C_adj value can be used to optimize the calculation of the temperature and humidity adjustment amount in the next cycle.

[0047] In another aspect, the present application proposes a distributed wire drawing machine operation management system, as shown, comprising: Figure 2 An environmental data acquisition module for acquiring real-time environmental parameters and current physical state data of materials of each node of the distributed wire drawing machine. An environmental parameter deviation and performance prediction module for calculating the deviation of the node environmental parameters from the preset reference environmental parameters based on the temperature and humidity values, and generating a material performance fluctuation prediction value based on the deviation and the current physical state data of the materials. A compensation measure formulation and execution module for comparing the prediction value with the material performance threshold, determining the temperature adjustment amount and humidity adjustment amount required by the node, sending the temperature adjustment amount and humidity adjustment amount to the node, and controlling the operation parameters of the cooling system and dehumidification device. A compensation effect monitoring and error calculation module for monitoring the material yield and physical property measured value of the node after executing the compensation, comparing the physical property measured value with the preset standard value, calculating the compensation error and updating the deviation. A trend analysis and dynamic correction module for performing trend analysis on the compensation error and historical compensation data, generating an environmental parameter dynamic correction coefficient and storing it in the local database of the node. In addition, each of the above modules is also used to implement other steps of the above-mentioned distributed wire drawing machine operation management method when executed, as shown in the following example:

[0048] In modern industrial production, wire drawing machines are widely used in metal processing, plastic product manufacturing and other industries. Its core task is to draw raw materials (such as metal wires or plastic particles) through a mold to produce finished products of the required specifications. However, due to the influence of environmental temperature and humidity fluctuations and material state changes, etc., the wire drawing process often has problems such as decreased yield and unstable physical properties. In order to solve these problems, the present application proposes a distributed wire drawing machine operation management method, which collects data from each node in real time and dynamically adjusts equipment parameters to ensure stable production process and improve finished product quality.

[0049] Step 1: Real-time monitoring of environmental parameters and material state

[0050] 1.1 Data acquisition Install a sensor group on each key node of the wire drawing machine to collect temperature T and humidity H data.

[0051] Suppose the current collected temperature T=30℃ and humidity H=60%RH at a certain node; the initial data set D_0={T=30,H=60}.

[0052] ​1.2 Calculation of temperature-humidity ratio The temperature-humidity ratio R = T / H is calculated: R = 30 / 60 = 0.5.

[0053] 1.3 Comparison with standard value The pre-stored standard temperature-humidity ratio R_s = 0.45, and the allowed deviation Δ = 0.05 is set.

[0054] |R-R_s| = |0.5-0.45| = 0.05, which is exactly equal to Δ, indicating that the environment is slightly deviated.

[0055] If it is greater than Δ, increase the sampling frequency F: F_new = F*(1+k*|R-R_s|).

[0056] Assuming the original sampling frequency F = 1 Hz, and the proportionality constant k = 0.1: F_new = 1*(1+0.1*0.05) = 1.005 Hz.

[0057] 1.4 Continuously update material state According to the updated frequency, continuously collect data and update the current physical state S of the material (such as the ductility, hardness, etc. of the material), for example: the current material ductility S = 85%.

[0058] Step two: Calculate the deviation of environmental parameters 2.1 Calculate the comprehensive index P of the environment, P = T + H = 30 + 60 = 90.

[0059] 2.2 Compare with the reference value, set the reference environmental parameter P_base = 85, and calculate the deviation percentage: ΔP = |P-P_base| / P_base*100%; ΔP = |90-85| / 85*100 = 5.88%; This value indicates that the current environment deviates from the ideal state by about 5.88%.

[0060] Step three: Predict material performance fluctuation 3.1 Comprehensive influence factor I, I = ΔP*S = 5.88%*85 = 4.998.

[0061] 3.2 Performance change rate prediction V_pred, assuming the historical average minimum influence factor I_min = 1, and the maximum influence factor I_max = 10; the historical performance change rate V_his = 5%, and the minimum performance change rate V_min = 1%.

[0062] Use the interpolation formula: V_pred = V_his + (I-I_min) / (I_max-I_min)*(V_his-V_min) V_pred=5%+(4.998-1) / (10-1)*(5%-1%) =5%+(3.998 / 9)*4%=5%+1.78%=6.78%.

[0063] This means that material properties are expected to decrease by 6.78% and require adjustment.

[0064] Step 4: Determine the compensation adjustment amount 4.1 Performance Threshold Comparison Set the material property threshold V_thres to 6% and the current predicted value V_pred to 6.78%. The difference D_V is 6.78% - 6% = 0.78%.

[0065] 4.2 Calculation of temperature and humidity adjustment Assume that the temperature adjustment coefficient k_T=0.5% and the humidity adjustment coefficient k_H=0.3%: T_adj=k_T*D_V=0.5%*0.78=0.39%; H_adj=k_H*D_V=0.3%*0.78=0.23%; That means the temperature will drop by 0.39% and the humidity will drop by 0.23%.

[0066] Step 5: Execution environment control 5.1 Equipment Energy Adjustment Assume the maximum power of the cooling system P_max = 10kW, and the maximum efficiency of the dehumidification device Q_max = 20L / h: E_T=T_adj*P_max / 100=0.39*10000W / 100=39W; E_H=H_adj*Q_max / 100=0.23*20L / h / 100=0.046L / h.

[0067] These values ​​are converted into control instructions and sent to the corresponding control system to adjust the working status of the cooling system and dehumidifier.

[0068] Step 6: Evaluate the effectiveness of compensation 6.1 Yield Calculation A batch of raw materials N_in = 100kg, actual output N_fin = 92kg: Y=(N_fin / N_in)*100%=92%.

[0069] 6.2 Physical property testing The measured material strength M_meas = 520MPa, the standard strength M_std = 530MPa: M deviation = |520-530| = 10MPa.

[0070] 6.3 Quality deviation D_qual Assume the yield rate influencing factor L=0.5: D_qual=|M_meas-M_std|+L*(100-Y)=10+0.5*(100-92)=10+4=14.

[0071] Step 7: Dynamically update the deviation 7.1 Compensation Error Comp_E Assume the quality deviation weight K=5: Comp_E=E_M+D_qual / K=10+14 / 5=10+2.8=12.8.

[0072] 7.2 Update Deviation ΔP_new Assume the physical property weight factor W_p=0.2: ΔP_new=ΔP+Comp_E*W_p=5.88+12.8*0.2=5.88+2.56=8.44%.

[0073] Step 8: Trend analysis and correction factor generation The compensation errors of the past five times are: {10, 11, 12, 13, 14}, and the current E_M=12.8.

[0074] Avg_E=(12.8+10+11+12+13+14) / 6=72.8 / 6=12.13.

[0075] Assume the modified intensity factor M=0.1: C_adj=1+((E_M-Avg_E) / Avg_E)*M=1+((12.8-12.13) / 12.13)*0.1 =1+(0.67 / 12.13)*0.1=1+0.0055=1.0055.

[0076] This correction coefficient C_adj is stored in the database and used as a reference for the next round of adjustment.

[0077] This example simulates the complete operation and adjustment process of a wire drawing machine, from environmental perception, data analysis, material property prediction, to final equipment adjustment and feedback optimization, forming a closed-loop control system. This method can be deployed in factory production lines, especially for large-scale, multi-site distributed production equipment. It can significantly improve product quality stability, reduce energy consumption and scrap rates, and has promising industrial applications.

[0078] Finally, it should be noted that the above is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A distributed wire drawing machine operation management method, characterized in that: The following steps are involved: Collect the real-time environmental parameters of each node of the distributed wire drawing machine and the current physical state data of the material. The environmental parameters include temperature and humidity values; Based on the temperature and humidity values, calculating the deviation between the node environmental parameters and the preset reference environmental parameters, and generating a material performance fluctuation prediction value according to the deviation and the current physical state data of the material; Comparing the predicted value with a material property threshold to determine a temperature adjustment amount and a humidity adjustment amount that the node needs to compensate, sending the temperature adjustment amount and the humidity adjustment amount to the node to control operating parameters of its cooling system and dehumidification device; monitoring the material yield and measured values ​​of physical properties after the compensation is performed on the node, comparing the measured values ​​of the physical properties with preset standard values, calculating the compensation error and updating the deviation; The compensation error and historical compensation data are subjected to trend analysis to generate a dynamic correction coefficient of the environmental parameter and store it in the node local database.

2. A distributed wire drawing machine operation management method according to claim 1, characterized in that: The collection of real-time environmental parameters of each node of the distributed wire drawing machine and the current physical state data of the material includes: Setting a sensor group at each node, and acquiring an initial environmental data set through the sensor group, wherein the data set includes temperature and humidity; Calculating a temperature-humidity ratio based on the temperature and humidity, and using the ratio to assess the degree of impact of the current environment on the material; Comparing the ratio with a pre-stored standard temperature and humidity ratio, and adjusting the sampling frequency of the sensor group if the difference exceeds a set threshold; The current physical state data of the material is continuously updated according to the adjusted sampling frequency to ensure that the data reflects the state changes of the material under the latest environmental conditions.

3. A distributed wire drawing machine operation management method according to claim 2, characterized in that: Calculating a deviation between the node environmental parameter and a preset reference environmental parameter based on the temperature value and the humidity value includes: Obtaining the temperature and humidity data collected by each node in real time, and calculating the environmental parameters of each node based on the temperature and humidity to form a current environmental parameter set; The current environmental parameter set is compared with the preset reference environmental parameters to calculate the deviation of each node, and the deviation is stored in the node local database.

4. A distributed wire drawing machine operation management method according to claim 3, characterized in that: Generating a material property fluctuation prediction value based on the deviation and the current physical state data of the material, including: Obtaining a deviation amount and current physical state data of the material, and defining a comprehensive influencing factor based on the deviation amount and the current physical state data of the material; The comprehensive impact factor is quoted and combined with the material performance change rate under similar conditions in historical data to calculate the predicted performance change rate; The predicted performance change rate value is stored and used as a material performance fluctuation prediction value to adjust the operating parameters of each node.

5. A distributed wire drawing machine operation management method according to claim 4, characterized in that: Comparing the predicted value with a material property threshold to determine a temperature adjustment amount and a humidity adjustment amount required to compensate for the node includes: Obtaining the predicted value of material property fluctuation and the preset material property threshold, calculating the difference; if the difference is greater than zero, it indicates that environmental parameter adjustment is required; The temperature adjustment amount and the humidity adjustment amount are calculated according to the difference.

6. A distributed wire drawing machine operation management method according to claim 5, characterized in that: Sending the temperature adjustment amount and the humidity adjustment amount to the node to control the operating parameters of the cooling system and the dehumidification device thereof, including: Calculate the cooling system energy adjustment value and the dehumidification device efficiency adjustment value based on the temperature adjustment amount and the humidity adjustment amount; The energy adjustment value and the efficiency adjustment value are converted into corresponding control instructions and sent to the control system of the corresponding node. The control system adjusts the working status of the cooling system and the dehumidification device according to the control instructions.

7. A distributed wire drawing machine operation management method according to claim 6, characterized in that: Monitoring the material yield and measured values ​​of physical properties after the compensation is performed on the node, including: After the environmental parameters are adjusted at each node, the production line is started and a batch of materials is produced according to the working status of the cooling system and dehumidification device corresponding to the control instruction. The number of finished products and the total input amount are collected and the material yield rate is calculated; Performing physical property testing on the batch of materials to obtain physical property values, and comparing them with preset standard physical property values; The quality deviation is calculated based on the yield and physical property values, and the deviation is recorded as a basis for evaluating the compensation effect.

8. A distributed wire drawing machine operation management method according to claim 7, characterized in that: Comparing the measured value of the physical property with the preset standard value, calculating the compensation error and updating the deviation amount, including: Obtaining the physical property values ​​and corresponding standard physical property values, and calculating the physical property deviation to quantify the difference between the physical properties of the material actually produced and the expected standard; The physical property deviation is combined with the quality deviation to comprehensively evaluate the compensation error of the current node. The compensation error is obtained by comparing the trend of the physical property deviation with the historical average deviation; A new offset is updated based on the compensation error combined with the offset.

9. A distributed wire drawing machine operation management method according to claim 8, characterized in that: Perform trend analysis on the compensation error and historical compensation data, generate dynamic correction coefficients of environmental parameters and store them in the node local database, including: Obtain the calculated compensation error, extract the past historical compensation error value set from the node local database, calculate the average value of all compensation errors, and use it to measure the trend center of the overall compensation error; Calculate the dynamic correction coefficient of the environmental parameter by quoting the average value and the historical compensation error value set; The dynamic correction coefficient of the environmental parameter is stored together with the timestamp in the node local database as basic data for reference during adjustment, and is used to optimize the calculation of the temperature and humidity adjustment amount using the correction coefficient in the cycle.

10. A distributed wire drawing machine operation management system for implementing the method according to any one of claims 1 to 9, characterized in that: include: Environmental data acquisition module, used to collect real-time environmental parameters of each node of the distributed wire drawing machine and the current physical state data of the material; An environmental parameter deviation and performance prediction module, configured to calculate the deviation between the node environmental parameter and a preset reference environmental parameter based on the temperature and humidity values, and generate a material performance fluctuation prediction value based on the deviation and the current physical state data of the material; a compensation measure formulation and execution module, configured to compare the predicted value with a material property threshold, determine a temperature adjustment amount and a humidity adjustment amount required to compensate the node, send the temperature adjustment amount and the humidity adjustment amount to the node, and control the operating parameters of the cooling system and the dehumidification device thereof; a compensation effect monitoring and error calculation module, configured to monitor the material yield and measured values ​​of physical properties after the compensation is performed on the node, compare the measured values ​​of the physical properties with preset standard values, calculate the compensation error, and update the deviation; The trend analysis and dynamic correction module is used to perform trend analysis on the compensation error and historical compensation data, generate dynamic correction coefficients of environmental parameters and store them in the node local database.

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