Real-time monitoring and control system for annealing temperature of electrical steel strip

Abstract

The application relates to the technical field of steel production and processing, and discloses an annealing temperature real-time monitoring and regulation system for electrical steel strips. The system comprises a temperature data acquisition module, which is responsible for collecting temperature and cooling rate data in the annealing process and obtaining expected parameters; a temperature deviation analysis module, which constructs a matrix by calculating a deviation coefficient through a dynamic weighting algorithm; a dynamic regulation module, which generates regulation parameters based on a fuzzy logic control algorithm; an abnormality grading module, which divides annealing abnormality grades; and a real-time feedback module, which transmits the regulation parameters and abnormality identifiers to a control end. The system also has functions of adjusting a rule base according to strip characteristics, optimizing regulation and control by backtracking historical data, and self-adaptively updating abnormality threshold values. Through the cooperative work of the modules, real-time monitoring and accurate regulation of the annealing temperature are realized, the annealing quality and production efficiency of the electrical steel strips are improved, and the cost is reduced.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of steel production and processing, and particularly to an annealing temperature real-time monitoring and control system for electrical steel strips. BACKGROUND

[0002] In the steel production industry, electrical steel is an important soft magnetic alloy material, and its performance has a key impact on the operating efficiency and energy loss of power equipment. Annealing process is one of the core links that determine the performance of electrical steel strips, and the accurate control of annealing temperature is directly related to the crystal structure, magnetic permeability, iron loss and other key performance indicators of electrical steel. However, the temperature control of the current electrical steel strip annealing process faces many challenges.

[0003] Traditional annealing temperature monitoring methods have obvious defects. On the one hand, the layout and precision of temperature sensors limit the collected data from fully and accurately reflecting the temperature distribution of the entire annealing area. For example, in some large annealing furnaces, the number of sensors is limited and unevenly distributed, making it difficult to capture the temperature changes in some areas in time, and blind spots in temperature monitoring may occur, which may cause local temperature to be too high or too low, affecting the consistency of the overall quality of the steel strip. On the other hand, existing monitoring systems often only focus on real-time temperature measurement, lacking effective monitoring and comprehensive analysis of cooling rates. Cooling rate also has an important impact on the organizational structure and performance of electrical steel, and unreasonable cooling rate may cause stress concentration, organizational defects and other problems in the steel strip, reducing product performance and qualification rate.

[0004] The annealing temperature control technology also needs to be improved. The existing control methods are mostly based on fixed experience parameters or simple control algorithms, which are difficult to adapt to complex and variable production conditions. In actual production, factors such as the material, thickness and production speed of electrical steel strips will affect the demand for annealing temperature. For example, steel strips of different thicknesses have different temperature change rates and patterns under the same heating and cooling conditions. Traditional control methods cannot dynamically adjust the heating power and cooling rate according to these real-time changing factors, resulting in a large deviation between the annealing temperature and the expected curve, which seriously affects the stability of product quality.

[0005] The processing mechanism of abnormal situations also has defects. In the annealing process, once the temperature is abnormal, the existing system often cannot quickly and accurately judge the severity of the abnormality and take effective measures in time. This not only may lead to the generation of a large number of unqualified products, increase the production cost, but also may cause damage to the annealing equipment, shorten the service life of the equipment, and affect the continuity and efficiency of production. With the increasing requirements of the electric power industry on the performance of electrical steel, and the increasing attention of steel production enterprises to production efficiency and product quality control, it is particularly urgent to develop a system that can monitor and accurately control the annealing temperature of electrical steel strip in real time. Such a system needs to overcome the defects of traditional technology, achieve comprehensive and accurate monitoring of the annealing temperature and cooling rate, have intelligent and efficient control ability, and have a perfect abnormality processing mechanism to meet the high-quality and high-efficiency requirements of modern steel production. SUMMARY

[0006] The purpose of the present application is to provide an electrical steel strip annealing temperature real-time monitoring and control system to solve the problems raised in the background art.

[0007] To achieve the above-mentioned purpose, the present application provides the following technical solution: an electrical steel strip annealing temperature real-time monitoring and control system, the system comprising:

[0008] a temperature data acquisition module, the temperature data acquisition module being configured to acquire temperature monitoring data and cooling rate monitoring data of each heating zone in the annealing process in real time, and receive a preset annealing temperature expected curve and a cooling rate expected value;

[0009] a temperature deviation analysis module, the temperature deviation analysis module being configured to perform multi-dimensional deviation calculation on the temperature monitoring data, the cooling rate monitoring data, and the annealing temperature expected curve, the cooling rate expected value, by using a dynamic weighting algorithm, and generate a temperature deviation coefficient matrix;

[0010] a dynamic control module, the dynamic control module being configured to generate heating power control parameters and cooling rate compensation parameters according to the temperature deviation coefficient matrix based on a fuzzy logic control algorithm;

[0011] an abnormality grading module, the abnormality grading module being configured to divide the current annealing stage into different abnormality grades according to whether the deviation coefficients of each dimension in the temperature deviation coefficient matrix exceed a dynamic threshold value, and output an abnormality grade identifier;

[0012] a real-time feedback module, the real-time feedback module being configured to transmit the heating power control parameters, the cooling rate compensation parameters, and the abnormality grade identifier to an annealing furnace control end;

[0013] The execution steps of the temperature deviation analysis module include:

[0014] Extracting the time series of temperature monitoring data of each heating zone, and matching it with the annealing temperature expectation curve point by point to calculate the temperature time sequence deviation coefficient;

[0015] Comparing the cooling rate monitoring data with the cooling rate expectation value in a sliding window to generate the cooling rate fluctuation coefficient;

[0016] According to the temperature time sequence deviation coefficient and the cooling rate fluctuation coefficient, a temperature deviation coefficient matrix containing a weight factor is constructed.

[0017] Preferably, the execution steps of the dynamic regulation module further include:

[0018] Inputting the temperature deviation coefficient matrix into a fuzzy logic controller, and performing fuzzy processing on the deviation interval through a preset membership function;

[0019] Generating fuzzy outputs of the heating power regulation parameters and the cooling rate compensation parameters based on a fuzzy rule base, and converting them into accurate control instructions through a defuzzification algorithm.

[0020] Preferably, the execution steps of the temperature data acquisition module include:

[0021] Collecting real-time temperature data of each heating zone through a distributed temperature sensor array, and recording a time stamp;

[0022] Obtaining the steel strip conveying speed through a laser speedometer, and calculating the cooling rate monitoring value in combination with the cooling zone thermal imaging data;

[0023] Retrieving the annealing temperature expectation curve and the cooling rate expectation value matched with the current steel strip model from an annealing process database;

[0024] Adding the real-time temperature data and the cooling rate monitoring value to a temperature monitoring data set, and synchronously storing the time stamp information.

[0025] Preferably, the execution steps of the temperature deviation analysis module further include:

[0026] The temperature time sequence deviation coefficient calculation formula is defined as:

[0027]

[0028] wherein, δ T is the temperature time sequence deviation coefficient, T 监测,i is the monitoring temperature at the i-th time point, T 期望,i is the corresponding expectation temperature, w i is a dynamic weight factor within a time window, and N is the number of sampling points;

[0029] The cooling rate fluctuation coefficient calculation formula is defined as:

[0030]

[0031] wherein, δ C is the fluctuation coefficient of cooling rate, C 监测 is the actual monitored cooling rate value, C 期望 is the preset cooling rate expected value, and a is the cooling rate sensitivity correction factor.

[0032] Preferably, the execution steps of the dynamic regulation module further include:

[0033] According to the thickness and material characteristics of the steel strip, the rule base of the fuzzy logic controller is dynamically adjusted, including:

[0034] If the high-temperature zone deviation proportion in the temperature deviation coefficient matrix exceeds 50%, the cooling rate compensation parameter is preferentially increased;

[0035] If the low-temperature zone deviation continues to exceed 3 sampling periods, a stepwise heating power increase strategy is triggered.

[0036] Preferably, the execution steps of the abnormality grading module include:

[0037] According to the deviation coefficients of each heating zone in the temperature deviation coefficient matrix, a regional abnormality index is calculated:

[0038]

[0039] wherein, R j is the abnormality index of the jth heating zone, δ T,j,k is the temperature time series deviation coefficient of the kth monitoring point in the jth heating zone, γ k is the kth weight factor of region j, and M is the number of monitoring points in the region;

[0040] If the regional abnormality index exceeds a preset threshold value, the corresponding region is marked as a first-level abnormality; otherwise, it is marked as a second-level abnormality.

[0041] Preferably, the dynamic regulation module is further connected with a historical data backtracking unit, and the execution steps of the historical data backtracking unit include:

[0042] Retrieving cases with a similarity greater than 80% to the current temperature deviation coefficient matrix from historical annealing records, and extracting the corresponding regulation parameter optimization scheme;

[0043] Weighted fusion of the optimization scheme and the output result of the fuzzy logic controller to generate a final regulation instruction.

[0044] Preferably, the abnormality grading module is further connected with an adaptive threshold updating unit, and the execution steps of the adaptive threshold updating unit include:

[0045] The temperature deviation coefficient distribution of each heating area in the last 24 hours is counted, and the threshold range of the abnormal level division is dynamically adjusted.

[0046] If the abnormality is not triggered in 10 consecutive sampling periods, the threshold sensitivity is reduced by 5%.

[0047] Preferably, the execution steps of the real-time feedback module further include:

[0048] The control parameters and abnormal level identifiers are packaged into a JSON format data packet and transmitted to the annealing furnace control end through an industrial Ethernet.

[0049] The temperature deviation distribution and abnormal level identifiers of each heating area are visualized in the form of a heat map in the control end interface.

[0050] Preferably, the present application further includes an electronic device, which comprises:

[0051] A memory for storing the control program of the electrical steel strip annealing temperature real-time monitoring and control system as described in the present application;

[0052] A processor for executing the control program to realize real-time monitoring and control of the electrical steel strip annealing temperature.

[0053] Compared with the prior art, the present application has the following advantages:

[0054] The electrical steel strip annealing temperature real-time monitoring and control system of the present application has many significant advantages. In terms of data acquisition, the temperature data acquisition module collects real-time temperature data of each heating area through a distributed temperature sensor array and records the time stamp, obtains cooling rate monitoring values in combination with a laser speedometer and cooling zone thermal imaging data, and simultaneously retrieves matching annealing temperature expected curves and cooling rate expected values from an annealing process database, ensuring that the collected data is comprehensive, accurate and closely related to actual production, providing a solid foundation for subsequent accurate analysis and control.

[0055] The temperature deviation analysis module calculates the temperature time series deviation coefficient and the cooling rate fluctuation coefficient using a dynamic weighting algorithm, constructs a temperature deviation coefficient matrix, and realizes multi-dimensional accurate analysis of the temperature and cooling rate deviation in the annealing process. This comprehensive and detailed deviation calculation method can accurately reflect the difference between the actual annealing situation and the ideal state, providing a key basis for subsequent control, and greatly improving the accuracy and reliability of the analysis compared with the traditional simple comparison method.

[0056] The dynamic regulation module generates heating power regulation parameters and cooling rate compensation parameters according to a temperature deviation coefficient matrix based on a fuzzy logic control algorithm. Firstly, the deviation interval is fuzzified by a preset membership function, then a fuzzy output is generated based on a fuzzy rule base, and finally, an accurate control instruction is converted through a defuzzification algorithm. This process simulates the human thinking mode, fully considers various complex situations, and can flexibly adjust the regulation parameters according to the real-time deviation, so that the annealing temperature is closer to the expected curve, effectively improving the accuracy and stability of temperature regulation. For example, when the thickness and material characteristics of the steel strip change, the fuzzy logic controller rule base can also be dynamically adjusted to further optimize the regulation strategy and ensure the annealing quality.

[0057] The abnormal grading module divides the abnormal levels according to the temperature deviation coefficient matrix, accurately judges the abnormal degree by calculating the regional abnormal index and comparing it with the preset threshold, and outputs the abnormal level identifier. This enables the operator to quickly understand the abnormal situation in the annealing process, take appropriate measures in a timely manner for different levels of abnormality, avoid problem deterioration, reduce the production of unqualified products, and reduce production costs.

[0058] The real-time feedback module encapsulates the regulation parameters and abnormal level identifier into a JSON format data packet and transmits it to the annealing furnace control end through industrial Ethernet, and visualizes it in the form of a heat map on the control end interface. This way, the operator can intuitively and clearly master the temperature deviation distribution and abnormal state of each heating zone, facilitating timely decision-making and operation, and improving the visualization level and management efficiency of the production process. The historical data backtracking unit and the adaptive threshold updating unit in the system further enhance the system performance. The historical data backtracking unit retrieves similar cases, extracts optimization schemes and weights the fuzzy logic controller output results for fusion, providing more references for regulation and improving the regulation effect; the adaptive threshold updating unit dynamically adjusts the abnormal level division threshold according to the temperature deviation coefficient distribution, improving the accuracy and adaptability of abnormal monitoring. BRIEF DESCRIPTION OF DRAWINGS

[0059] Figure 1 A working principle diagram of the electric steel strip annealing temperature real-time monitoring and regulation system described in the present application;

[0060] Figure 2 A flowchart of data acquisition and processing by the temperature data acquisition module;

[0061] Figure 3 A flowchart of the deviation-based regulation strategy of the dynamic regulation module;

[0062] Figure 4 A flowchart of the historical data backtracking optimization of the dynamic regulation module. DETAILED DESCRIPTION

[0063] With reference to the drawings of the embodiments of the present application, the technical solutions in the embodiments of the present application will be described clearly and completely. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by a person of ordinary skill in the art without creative work are within the protection scope of the present application.

[0064] Please refer to Figures 1-4 The present application provides an annealing temperature real-time monitoring and control system for electrical steel strip, and the overall implementation scheme is as follows:

[0065] The temperature data acquisition module plays a basic role in data collection in the whole system. It collects the temperature monitoring data and cooling rate monitoring data of each heating zone in the annealing process in real time through specific equipment and methods. Specifically, when collecting temperature data, a distributed temperature sensor array will be used to accurately obtain real-time temperature data of each heating zone and record accurate timestamps for each data, so as to facilitate subsequent analysis. In terms of cooling rate monitoring data, a laser speed meter is used to obtain the conveying speed of the steel strip, and then the cooling rate monitoring value is calculated based on the thermal imaging data of the cooling zone. At the same time, the module will retrieve the annealing temperature expected curve and cooling rate expected value matched with the current steel strip model from the annealing process database, add all collected real-time temperature data and cooling rate monitoring values to the temperature monitoring data set, and store the timestamp information synchronously, so as to provide comprehensive and accurate data support for subsequent analysis and control.

[0066] The temperature deviation analysis module receives the data transmitted by the temperature data acquisition module, and uses a dynamic weighting algorithm to calculate the multi-dimensional deviation of the temperature monitoring data and the cooling rate monitoring data from the annealing temperature expected curve and the cooling rate expected value. It first extracts the time sequence of the temperature monitoring data of each heating zone, matches it with the annealing temperature expected curve point by point, and calculates the temperature time sequence deviation coefficient; then it compares the cooling rate monitoring data with the cooling rate expected value in a sliding window, and generates the cooling rate fluctuation coefficient; finally, it constructs a temperature deviation coefficient matrix containing a weight factor according to the two coefficients. This matrix comprehensively reflects the deviation degree of temperature and cooling rate from the expected situation in the annealing process, and provides a key basis for subsequent control and abnormality judgment.

[0067] The dynamic regulation module generates heating power regulation parameters and cooling rate compensation parameters based on a fuzzy logic control algorithm according to the temperature deviation coefficient matrix. It first inputs the temperature deviation coefficient matrix into a fuzzy logic controller, and performs fuzzy processing on the deviation interval through a preset membership function to convert the accurate deviation value into a fuzzy language variable. Then, the fuzzy output of the heating power regulation parameters and the cooling rate compensation parameters is generated based on a fuzzy rule base, and the fuzzy output is converted into an accurate control instruction through a defuzzification algorithm, so that accurate regulation of the heating power and the cooling rate in the annealing process is realized.

[0068] The abnormal grading module divides the current annealing stage into different abnormal grades according to whether the deviation coefficients of each dimension in the temperature deviation coefficient matrix exceed the dynamic threshold. By calculating the regional abnormal index of each heating zone, if the regional abnormal index exceeds the preset threshold, the corresponding region is marked as a first abnormality; otherwise, it is marked as a second abnormality. The abnormal grade mark output by this module helps the operator to discover abnormal conditions in the annealing process in time and take appropriate measures.

[0069] The real-time feedback module is responsible for transmitting the heating power regulation parameters, the cooling rate compensation parameters generated by the dynamic regulation module, and the abnormal grade mark output by the abnormal grading module to the annealing furnace control end. It will package these data into a JSON format data packet for efficient transmission through industrial Ethernet. At the same time, the temperature deviation distribution and abnormal grade mark of each heating zone are visualized in the form of a heat map in the control end interface, so that the operator can intuitively and clearly understand the temperature conditions and abnormal states in the annealing process, and make timely decisions.

[0070] The implementation of the present application will be further described below in conjunction with Examples 1 to 6.

[0071] Example 1:

[0072] In this embodiment, the working processes of the dynamic regulation module and the temperature deviation analysis module are further described in detail.

[0073] When generating the heating power regulation parameters and the cooling rate compensation parameters, the dynamic regulation module inputs the temperature deviation coefficient matrix into the fuzzy logic controller. A plurality of membership functions are preset in the fuzzy logic controller, such as a triangular membership function, a trapezoidal membership function, etc. Taking the temperature deviation as an example, for different deviation ranges, fuzzy processing is performed through the corresponding membership function. Assuming that the temperature deviation is large, under the action of the membership function, the fuzzy language variable after fuzzy processing may be "very large"; and when the deviation is small, the language variable may be "very small".

[0074] The fuzzy output of the heating power regulation parameter and the cooling rate compensation parameter is generated based on a fuzzy rule base. The fuzzy rule base is established according to a large amount of experimental data and actual experience. For example, when the temperature deviation is "very large" and the cooling rate fluctuation is also "relatively large", the fuzzy rule base will give the corresponding fuzzy output of the heating power regulation parameter and the cooling rate compensation parameter. Then, the fuzzy output is converted into an accurate control instruction through a defuzzification algorithm. Common defuzzification algorithms include the gravity center method, the maximum membership degree method, etc. Taking the gravity center method as an example, the accurate value is determined by calculating the gravity center of the area surrounded by the fuzzy set membership function curve and the horizontal coordinate.

[0075] The temperature time sequence deviation coefficient and the cooling rate fluctuation coefficient are calculated by the temperature deviation analysis module, and there are specific calculation formulas. The temperature time sequence deviation coefficient calculation formula is defined as:

[0076]

[0077] wherein, δ T is the temperature time sequence deviation coefficient, which comprehensively reflects the deviation degree of the monitoring temperature and the expected temperature in a period of time. T 监测,i is the monitoring temperature at the i-th time point, which is the actual collected temperature data of each heating zone at a certain time point in the annealing process. T 期望,i is the corresponding expected temperature, which is the temperature value at the i-th time point on the annealing temperature expected curve retrieved from the annealing process database according to the current steel strip model. w i is the dynamic weight factor in the time window, which will be adjusted according to the actual situation, for example, in the key stage of the annealing process, the data weight near the current time point may be larger to highlight the influence of recent temperature changes. N is the sampling point number, which is the sampling number in the selected time window when calculating the temperature time sequence deviation coefficient, and the number of sampling points will affect the accuracy and stability of the calculation result.

[0078] The cooling rate fluctuation coefficient calculation formula is defined as:

[0079]

[0080] wherein, δ C is the cooling rate fluctuation coefficient, which is used to measure the fluctuation of the actual cooling rate and the expected cooling rate. C 期望 is the actual monitoring cooling rate value, which is obtained by combining the steel strip conveying speed obtained by the laser speedometer with the cooling zone thermal imaging data. C 期望The preset cooling rate expectation value is also retrieved from the annealing process database according to the steel strip model. Alpha is a cooling rate sensitivity correction factor, which is set according to different steel strip materials and annealing process requirements, and is used to adjust the sensitivity of the cooling rate fluctuation coefficient to the actual cooling rate fluctuation. For example, for some steel strip materials with high cooling rate requirements, the value of alpha may be set larger to reflect the fluctuation of the cooling rate more timely. The temperature time deviation coefficient and the cooling rate fluctuation coefficient calculated by these formulas provide accurate data basis for constructing the temperature deviation coefficient matrix.

[0081] Example 2:

[0082] The temperature data acquisition module plays an important role as the data source in the entire system. Its workflow includes multiple specific steps.

[0083] Real-time temperature data of each heating zone is collected by a distributed temperature sensor array. The distributed temperature sensor array is composed of multiple temperature sensors distributed at different positions, which have high precision and high sensitivity, and can accurately measure the temperature at different positions of each heating zone. At the same time of collecting temperature data, a time stamp is recorded for each data, accurate to millisecond level, to ensure that subsequent data analysis can accurately correspond to a specific time point.

[0084] The steel strip conveying speed is obtained by a laser speedometer. The laser speedometer uses the laser Doppler effect to measure the frequency change of the reflected light when laser irradiates the steel strip surface, and calculates the conveying speed of the steel strip. After obtaining the conveying speed of the steel strip, the cooling rate monitoring value is calculated by combining the cooling zone thermal imaging data. The cooling zone thermal imaging data is obtained by a thermal imager, which can capture the temperature distribution of the steel strip surface in the cooling zone. By analyzing the change of temperature with time in the thermal imaging data and the conveying speed of the steel strip, the cooling rate monitoring value is calculated by using a specific algorithm.

[0085] The annealing temperature expectation curve and the cooling rate expectation value matching the current steel strip model are retrieved from the annealing process database. The annealing process database stores a large number of annealing process parameters corresponding to different steel strip models, including annealing temperature expectation curve and cooling rate expectation value. In actual production, according to the current production steel strip model, the system can quickly and accurately retrieve the corresponding parameters from the database.

[0086] The real-time temperature data and cooling rate monitoring values are added to the temperature monitoring dataset, and the timestamp information is stored synchronously. The temperature monitoring dataset is stored in the form of a database, which facilitates data management and query. During storage, the temperature data, cooling rate monitoring values, and timestamp information are stored in association according to a certain format, for example, using the table structure of a relational database, taking the timestamp as the primary key, and storing the temperature data and cooling rate monitoring values as fields. In this way, the subsequent temperature deviation analysis module, dynamic control module, etc. can easily obtain the required data from the temperature monitoring dataset for analysis and processing. Through the work of the temperature data acquisition module, accurate and timely data support is provided for the entire electric steel strip annealing temperature real-time monitoring and control system, ensuring that the system can effectively monitor and control the annealing process.

[0087] Example 3:

[0088] During actual operation, the dynamic control module dynamically adjusts the rule base of the fuzzy logic controller according to the thickness and material properties of the steel strip to achieve more precise control.

[0089] Different thicknesses of steel strips have different requirements for temperature and cooling rate during annealing. For thicker steel strips, the heat capacity is larger, and the temperature rises and falls relatively slowly. When the high-temperature zone deviation proportion in the temperature deviation coefficient matrix exceeds 50%, it indicates that the temperature deviation of the high-temperature zone from the expected temperature is relatively serious. In this case, the cooling rate compensation parameter is preferentially increased. Because the thicker steel strip stays in the high-temperature zone for too long, it may cause problems in the organization and performance of the steel strip. By increasing the cooling rate compensation parameter, the cooling speed of the steel strip in the high-temperature zone can be accelerated, so that it can return to the reasonable temperature range as soon as possible. For example, if the current production is a 5mm-thick electric steel strip, the temperature deviation coefficient matrix shows that the high-temperature zone deviation proportion reaches 60% at a certain moment. The system will automatically increase the cooling rate compensation parameter according to the rule base. Assuming that the original cooling rate compensation parameter is 0, it may be adjusted to 5 (the specific value is determined according to the actual situation and pre-set rules) to accelerate the cooling speed.

[0090] For the case of low-temperature zone deviation, if the low-temperature zone deviation lasts for more than 3 sampling periods, a step-up strategy of heating power is triggered. This is because the low-temperature zone temperature is too low to affect the annealing effect of the steel strip, resulting in the performance of the steel strip failing to meet the expected standard. The step-up strategy means that the power value is increased by a certain amount each time, gradually increasing the temperature of the low-temperature zone. For example, if each sampling period is set to 10 seconds, and the low-temperature zone deviation exceeds the allowed range for 30 consecutive seconds, the system will start the step-up strategy of heating power. The first time the power is increased by 10kW, and after a period of time, if the low-temperature zone deviation still exists, the power is increased again, and the power value can be adjusted according to the actual situation, such as 15kW for the second time, and so on, until the low-temperature zone temperature returns to a reasonable range. At the same time, during the adjustment of the heating power, the system will monitor the changes in the temperature deviation coefficient matrix in real time, and dynamically adjust the subsequent control strategy according to the new deviation situation, to ensure that the annealing process can be stable and efficient, and the performance of the steel strip can reach the best state.

[0091] Embodiment 4:

[0092] The abnormality grading module is used in the entire system to grade the current annealing stage, and its execution steps have clear calculation logic and judgment criteria.

[0093] According to the deviation coefficients of each heating zone in the temperature deviation coefficient matrix, the regional abnormality index is calculated. The calculation formula is:

[0094]

[0095] wherein R j is the abnormality index of the jth heating subzone, which comprehensively reflects the overall situation of the temperature deviation in the heating subzone. δ T,j,k is the temperature time series deviation coefficient of the kth monitoring point in the jth heating subzone, which is calculated by the temperature deviation analysis module and reflects the deviation degree of the temperature of each monitoring point from the expected temperature. γ k is the kth weight factor of region j, which is set according to the position, importance and other factors of the monitoring point in the heating subzone. For example, for the monitoring point near the center of the steel strip, since its temperature has a greater impact on the overall performance of the steel strip, its weight factor may be set higher; while for the monitoring point at the edge position, the weight factor is relatively low. M is the number of monitoring points in the region, which is determined according to the size of the heating subzone and the monitoring accuracy requirement. Generally speaking, the more the number of monitoring points, the more accurate the regional abnormality index calculated can reflect the temperature deviation in the heating subzone.

[0096] After calculating the area anomaly index, the anomaly level is divided according to the preset threshold. If the area anomaly index exceeds the preset threshold, the corresponding area is marked as a first-level anomaly; otherwise, it is marked as a second-level anomaly. The preset threshold is determined according to a large amount of historical production data and experimental results. For example, after many experiments and data analysis, it is determined that when the area anomaly index is greater than 0.8, the temperature deviation of the area is relatively serious, which will have a greater impact on the annealing quality of the steel strip. At this time, the area is marked as a first-level anomaly. The operator can take appropriate measures in time according to the anomaly level mark. For the first-level anomaly area, the annealing process may need to be stopped immediately, and the equipment needs to be checked and adjusted. For the second-level anomaly area, the heating power or cooling rate can be appropriately adjusted to avoid further deterioration of the abnormal situation and ensure that the annealing process can proceed smoothly to produce electrical steel strips that meet the quality requirements.

[0097] Embodiment 5:

[0098] When the system is running, the historical data backtracking unit connected to the dynamic control module plays a key role. The core task of this unit is to filter out past cases with a similarity greater than 80% to the current temperature deviation coefficient matrix from the huge historical annealing record database. The historical annealing record database stores a large amount of annealing data under different working conditions, which details the changes of various parameters in the annealing process and the final control results.

[0099] The historical data backtracking unit uses a specific similarity calculation algorithm for case retrieval. For example, the Euclidean distance-based similarity algorithm is used to treat the temperature deviation coefficient matrix as a vector in a multi-dimensional space, and the similarity is measured by calculating the Euclidean distance between the current matrix vector and the matrix vector in the historical record. The formula for calculating the Euclidean distance is: where d represents the Euclidean distance between two vectors, x i and y i are the components of the two vectors in the i-th dimension, and n is the dimension of the vector. In the temperature deviation coefficient matrix, n is the number of elements in the matrix. After calculating the Euclidean distance, the distance value is converted to a similarity value through a specific conversion formula. When the similarity is greater than 80%, it is determined that the historical case meets the filtering condition.

[0100] When the historical cases that meet the conditions are filtered out, the historical data backtracking unit extracts the corresponding control parameter optimization scheme of these cases. These optimization schemes are effective control strategies that have been tested and verified many times in the past actual production. For example, in a certain historical case, in response to a similar temperature deviation situation as the current one, the control measures of reducing the heating power by 20% and increasing the cooling rate by 15% were taken, which successfully restored the annealing process to normal and ensured the quality of the steel strip.

[0101] Next, the historical data backtracking unit will extract the optimization scheme and the output results of the fuzzy logic controller to generate the final control instruction. In the process of weighted fusion, according to the similarity of the current production environment and the historical case and the actual production experience, different weights are assigned to the optimization scheme and the output results of the fuzzy logic controller. Suppose after evaluation, it is considered that the current production environment has a very high similarity with a historical case, the weight of the historical optimization scheme can be set to 0.7, and the weight of the fuzzy logic controller output result is set to 0.3. Through weighted calculation, for example, for the heating power regulation parameter, if the heating power regulation parameter in the historical optimization scheme is P1, the heating power regulation parameter output by the fuzzy logic controller is P2, then the final heating power regulation parameter P = 0.7P1+0.3P2; similarly, the cooling rate compensation parameter is calculated. In this way, the combination of historical experience and modern intelligent algorithms is fully utilized, making the generated control instruction more scientific and reasonable, effectively improving the regulation precision and stability of the system to the annealing process.

[0102] Example 6:

[0103] The adaptive threshold updating unit connected to the anomaly grading module and the real-time feedback module play an indispensable role in the stable operation of the system.

[0104] The adaptive threshold updating unit will regularly statistics the temperature deviation coefficient distribution of each heating zone in the last 24 hours. It first collects all the temperature deviation coefficient data of each heating zone in these 24 hours, which reflects the deviation degree of the actual temperature and the expected temperature of each heating zone at each time. Then, using data analysis methods such as calculating the mean, median, variance and other statistics of the data, to deeply understand the distribution characteristics of the temperature deviation coefficient. If it is found that the variance of the temperature deviation coefficient increases, it means that the fluctuation range of the temperature deviation is expanding, and the stability of the annealing process has decreased. At this time, the adaptive threshold updating unit will appropriately increase the threshold of anomaly grading. For example, the threshold of the first level anomaly is 0.5, after analysis, it is found that the fluctuation of the temperature deviation coefficient increases, the threshold is increased to 0.6, so that the annealing process can be more strictly monitored, and potential abnormal situations can be discovered in time. Conversely, if the variance decreases, it means that the annealing process tends to be stable, then appropriately reduce the threshold.

[0105] In addition, if no anomaly is triggered for 10 consecutive sampling periods, it indicates that the current annealing process is relatively stable, in order to avoid the system being too sensitive and generating too many false alarms, the adaptive threshold updating unit will reduce the threshold sensitivity by 5%. Suppose the threshold of the first level anomaly is 0.6, after reducing 5%, the new threshold becomes 0.6x(1-0.05)=0.57. Through this way of dynamically adjusting the threshold, the system can better adapt to different production conditions, improve the accuracy and reliability of anomaly monitoring.

[0106] The real-time feedback module is responsible for transmitting the control parameters and abnormal level identification to the annealing furnace control end efficiently, and presenting the relevant information in an intuitive way. It first encapsulates the heating power control parameters generated by the dynamic control module, the cooling rate compensation parameters, and the abnormal level identification output by the abnormal grading module in JSON format. JSON format has the characteristics of lightweight, easy to parse and transmit, and is very suitable for data interaction in industrial systems. The encapsulated data is transmitted through industrial Ethernet, which has the characteristics of high speed and stability, and can ensure that the data reaches the annealing furnace control end accurately and timely.

[0107] In the annealing furnace control end interface, the real-time feedback module visualizes the temperature deviation distribution and abnormal level identification of each heating zone in the form of a heat map. The heat map represents the size of the temperature deviation by different colors, for example, red represents the area with larger temperature deviation, green represents the area with smaller temperature deviation, and the depth of the color further reflects the specific value of the deviation. At the same time, the abnormal level identification is directly marked on the corresponding heating zone position, such as "first-level abnormality" or "second-level abnormality". By observing the heat map, the operator can understand the temperature and abnormal state of each heating zone of the entire annealing furnace at a glance, discover problems in time and take corresponding measures, thereby effectively ensuring the smooth progress of the annealed steel strip annealing process and product quality.

[0108] It should be noted that the relational terms such as first and second, and the like, are used merely to distinguish one entity or action from another, without necessarily requiring or implying that the entities or actions are in any way mutually exclusive or in any way in a required sequence. In addition, the terms "comprising", "including" or any other variant thereof are intended to cover non-exclusive inclusion, so that a process, method, article or apparatus including a series of elements includes not only those elements, but also other elements not explicitly listed or inherent to such a process, method, article or apparatus.

[0109] Although the embodiments of the present application have been shown and described, it will be understood by those of ordinary skill in the art that various changes, modifications, substitutions and alterations can be made without departing from the principles and spirit of the present application, and the scope of the present application is defined by the appended claims and their equivalents.

Claims

1. An electrical steel strip annealing temperature real-time monitoring and regulation system, characterized in that, The application relates to a temperature deviation analysis and dynamic control method for an annealing process. The application comprises: a temperature data acquisition module for collecting temperature monitoring data and cooling rate monitoring data of each heating zone in real time during an annealing process, and receiving preset annealing temperature expectation curves and cooling rate expectation values; a temperature deviation analysis module for performing multidimensional deviation calculation on the temperature monitoring data, the cooling rate monitoring data, and the annealing temperature expectation curves and the cooling rate expectation values through a dynamic weighting algorithm, and generating a temperature deviation coefficient matrix; a dynamic control module for generating heating power control parameters and cooling rate compensation parameters according to the temperature deviation coefficient matrix based on a fuzzy logic control algorithm; an abnormal grading module for dividing the current annealing stage into different abnormal grades according to whether the deviation coefficients of each dimension in the temperature deviation coefficient matrix exceed a dynamic threshold value, and outputting abnormal grade identifiers; a real-time feedback module for transmitting the heating power control parameters, the cooling rate compensation parameters and the abnormal grade identifiers to an annealing furnace control end; The execution steps of the temperature deviation analysis module comprise: extracting the time sequence of the temperature monitoring data of each heating zone, and performing point-by-point matching with the annealing temperature expectation curves to calculate temperature time sequence deviation coefficients; performing sliding window comparison between the cooling rate monitoring data and the cooling rate expectation values to generate cooling rate fluctuation coefficients; constructing a temperature deviation coefficient matrix containing weight factors according to the temperature time sequence deviation coefficients and the cooling rate fluctuation coefficients; The execution steps of the temperature deviation analysis module further comprise: wherein, is a temperature timing deviation coefficient, is a monitoring temperature at a first time point, is a corresponding desired temperature, is a dynamic weight factor within a time window, is a number of sampling points; defining the temperature time sequence deviation coefficient calculation formula as: wherein, is a cooling rate fluctuation coefficient, is an actual monitored cooling rate value, is a preset cooling rate desired value, is a cooling rate sensitivity correction factor.

2. The system of claim 1, wherein, defining the cooling rate fluctuation coefficient calculation formula as: The execution steps of the dynamic control module further comprise: inputting the temperature deviation coefficient matrix into a fuzzy logic controller, and performing fuzzy processing on the deviation intervals through a preset membership function; 3. The system of claim 1, wherein, generating fuzzy outputs of the heating power control parameters and the cooling rate compensation parameters based on a fuzzy rule base, and converting the fuzzy outputs into accurate control instructions through a defuzzification algorithm. The execution steps of the temperature data acquisition module comprise: collecting real-time temperature data of each heating zone through a distributed temperature sensor array, and recording time stamps; obtaining the steel strip conveying speed through a laser speed measuring instrument, and calculating the cooling rate monitoring value in combination with the cooling zone thermal imaging data; calling the annealing temperature expectation curves and the cooling rate expectation values matched with the current steel strip model from an annealing process database; 4. The system of claim 2, wherein, adding the real-time temperature data and the cooling rate monitoring value to a temperature monitoring data set, and synchronously storing time stamp information. The execution steps of the dynamic control module further comprise: dynamically adjusting the rule base of the fuzzy logic controller according to the thickness and material characteristics of the steel strip, including: if the high-temperature zone deviation proportion in the temperature deviation coefficient matrix exceeds 50%, the cooling rate compensation parameter is preferentially increased; 5. The system of claim 2, wherein, if the low-temperature zone deviation continuously exceeds 3 sampling periods, a stepwise heating power increasing strategy is triggered. The execution steps of the abnormal grading module comprise: calculating the regional abnormal index according to the deviation coefficients of each heating zone in the temperature deviation coefficient matrix: in, For the first Abnormal index of each heating zone, For the first Within the heating zone, the first Temperature time-series deviation coefficient at each monitoring point For the region The Each weighting factor This represents the number of monitoring points within the area. If the regional anomaly index exceeds the preset threshold, the corresponding region is marked as a first-level anomaly; otherwise, it is marked as a second-level anomaly.

6. The system of claim 1, wherein, The dynamic regulation module is further connected with a historical data backtracking unit, and the execution steps of the historical data backtracking unit include: Retrieving cases with a similarity greater than 80% to the current temperature deviation coefficient matrix in the historical annealing records, and extracting the corresponding regulation parameter optimization scheme; The optimization scheme is weighted and fused with the output result of the fuzzy logic controller to generate a final regulation instruction.

7. The system of claim 1, wherein, The anomaly grading module is further connected with an adaptive threshold updating unit, and the execution steps of the adaptive threshold updating unit include: Statistically analyzing the temperature deviation coefficient distribution of each heating zone in the last 24 hours to dynamically adjust the threshold range for anomaly grading; If no anomaly is triggered in the last 10 sampling periods, the threshold sensitivity is reduced by 5%.

8. The system of claim 1, wherein, The execution steps of the real-time feedback module further include: The regulation parameters and anomaly level identification are packaged as a JSON format data packet and transmitted to the annealing furnace control end through industrial Ethernet; The temperature deviation distribution and anomaly level identification of each heating zone are visualized in the form of a heat map on the control end interface.

9. An electronic device, comprising: The system comprises: A memory for storing the control program of the system according to any one of claims 1-8; A processor for executing the control program to realize real-time monitoring and regulation of the annealing temperature of the electrical steel strip.

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