CN118357276B - Automatic positioning system and method in cold rolling process - Google Patents

Abstract

The present invention discloses an automatic positioning system and method in a cold rolling process, which belongs to the field of intelligent control, wherein the method includes: establishing an equipment feature set of cold rolling equipment; constructing a real-time measurement database of strip steel; performing deviation evaluation based on the equipment feature set and the real-time measurement database, and generating a window correction factor; establishing a real-time deviation database, which is a deviation data set established by real-time deviation collection through a correction sensor; determining a delay node, which is obtained based on the cold rolling control speed and a pre-collection distance, and the pre-collection distance is the distance between the collection points of the real-time measurement database and the real-time deviation database; configuring a timing control parameter based on the deviation data set, the window correction factor and the delay node; and performing positioning control of the cold rolling equipment through the timing control parameter. The present application solves the technical problem of poor positioning control effect of cold rolling equipment in the prior art, and achieves the technical effect of real-time correction of the cold rolling process and improving the positioning control effect.

Description

Automatic positioning system and method in cold rolling process

Technical Field

The invention relates to the field of intelligent control, and in particular to an automatic positioning system and method in a cold rolling process.

Background Art

Cold rolling technology is increasingly used in industrial production. As an important cold-rolled product, the quality of cold-rolled steel strip directly affects the performance indicators of subsequent processing links and final products. The existing cold rolling process mainly relies on operating experience to adjust the parameters of the cold rolling mill to achieve positioning control of the cold rolling process. However, this positioning control method cannot dynamically correct the deviation in real time according to the characteristics of the equipment and the real-time working conditions, resulting in the difficulty in ensuring the positioning effect of the cold-rolled steel strip.

Summary of the invention

The present application aims to solve the technical problem of poor positioning control effect of cold rolling equipment in the prior art by providing an automatic positioning system and method in a cold rolling process.

In view of the above problems, the present application provides an automatic positioning system and method in a cold rolling process.

The first aspect disclosed in the present application provides an automatic positioning method in a cold rolling process, the method comprising: establishing an equipment feature set of the cold rolling equipment, the equipment feature set being obtained through digital communication with the cold rolling equipment, the equipment feature set comprising equipment free section length, cold rolling control tension, cold rolling control speed, and equipment wear characteristics; constructing a real-time measurement database of the strip, the real-time measurement database being obtained through establishing real-time interactive point collection, the data in the real-time measurement database comprising thickness data and roughness data; performing deviation evaluation based on the equipment feature set and the real-time measurement database, and generating a window correction factor; establishing a real-time deviation database, the real-time deviation database being a deviation data set established by real-time deviation collection by a correction sensor; determining a delay node, the delay node being obtained based on the cold rolling control speed and a pre-collection distance, the pre-collection distance being the collection point distance between the real-time measurement database and the real-time deviation database; configuring timing control parameters based on the deviation data set, the window correction factor, and the delay node; and performing positioning control of the cold rolling equipment through the timing control parameters.

Another aspect disclosed in the present application provides an automatic positioning system in a cold rolling process, the system comprising: an equipment feature establishment module, used to establish an equipment feature set of the cold rolling equipment, the equipment feature set is obtained through digital communication with the cold rolling equipment, the equipment feature set includes equipment free section length, cold rolling control tension, cold rolling control speed, equipment wear characteristics; a real-time measurement data module, used to build a real-time measurement database of the strip, the real-time measurement database is obtained by establishing real-time interaction point collection, the data in the real-time measurement database includes thickness data, roughness data; a window correction factor module, used to perform correction based on the equipment feature set and the real-time measurement database. The invention relates to a method for determining a delay node, wherein the delay node is obtained based on the cold rolling control speed and the pre-collection distance, and the pre-collection distance is the collection point distance between the real-time measurement database and the real-time deviation database; a timing control parameter module is used to configure the timing control parameters according to the deviation data set, the window correction factor and the delay node; and an equipment positioning control module is used to perform positioning control of the cold rolling equipment through the timing control parameters.

One or more technical solutions provided in this application have at least the following technical effects or advantages:

The invention adopts the method of establishing an equipment feature set for cold rolling equipment to comprehensively describe the equipment status; constructing a real-time measurement database for strip steel to obtain real-time processing quality data of strip steel; performing deviation evaluation based on the equipment feature set and the real-time measurement database to generate a window correction factor for evaluating the current deviation status of the strip steel; establishing a real-time deviation database based on a deviation data set established by real-time deviation acquisition using a correction sensor to monitor the real-time deviation value of the strip steel; obtaining delay nodes based on the cold rolling control speed and the pre-acquisition distance, taking into account the corresponding relationship between the time delay between data acquisition and execution of positioning; configuring timing control parameters based on the deviation data set, the window correction factor and the delay node to realize the formulation of control strategies; executing positioning control of cold rolling equipment through timing control parameters to realize the technical solution of automatic and precise positioning of the cold rolling process, solving the technical problem of poor positioning control effect of cold rolling equipment in the prior art, and achieving the technical effect of real-time correction of the cold rolling process and improving the positioning control effect.

The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG1 is a schematic flow chart of an automatic positioning method in a cold rolling process provided by an embodiment of the present application;

FIG2 is a schematic diagram of a flow chart of generating a window correction factor in an automatic positioning method in a cold rolling process provided by an embodiment of the present application;

FIG3 is a schematic structural diagram of an automatic positioning system in a cold rolling process provided in an embodiment of the present application.

Explanation of the accompanying drawings: equipment characteristic establishment module 11, real-time measurement data module 12, window correction factor module 13, real-time deviation data module 14, delay node determination module 15, timing control parameter module 16, equipment positioning control module 17.

DETAILED DESCRIPTION

The overall idea of the technical solution provided by this application is as follows:

The embodiment of the present application provides an automatic positioning system and method in a cold rolling process. First, an equipment feature set describing the status of the cold rolling equipment is established, and a real-time measurement database reflecting the processing quality of the strip is constructed to obtain multidimensional process data related to the cold rolling process. Then, a deviation evaluation is performed based on the equipment feature set and the real-time measurement database, and a window correction factor is generated. At the same time, the real-time deviation value of the strip is monitored in real time to obtain a deviation data set. Determine the delay node that takes into account speed and distance factors to accurately grasp the characteristics of the cold rolling process. Then, the deviation data set, window correction factor and delay node are used to configure the timing control parameters to realize the formulation of the process control strategy. Finally, the positioning correction control is performed in a closed loop through the timing control parameters to complete the automatic and precise positioning of the cold rolling process.

After introducing the basic principles of the present application, various non-limiting implementation methods of the present application will be specifically described below in conjunction with the drawings in the specification.

Embodiment 1

As shown in FIG1 , an embodiment of the present application provides an automatic positioning method in a cold rolling process, the method comprising:

Establishing a device feature set of the cold rolling equipment, wherein the device feature set is obtained through digital communication with the cold rolling equipment, and the device feature set includes a free section length of the equipment, a cold rolling control tension, a cold rolling control speed, and a wear feature of the equipment;

In the embodiment of the present application, first, a digital interface of the cold rolling equipment is obtained, and the digital interface is used to digitally communicate with the cold rolling equipment to obtain equipment status data, wherein the digital interface is a data interaction interface for industrial sites such as a field bus interface and an industrial Ethernet interface. Then, a digital communication connection is established with the cold rolling equipment through the digital interface, and an equipment feature data acquisition instruction is sent. The equipment feature set returned by the cold rolling equipment is received, and the equipment feature set contains dynamic equipment feature data such as the free section length, cold rolling control tension, and cold rolling control speed of the cold rolling equipment. At the same time, the monitoring data of the wear detection device of the cold rolling equipment is obtained. The wear detection device is set at the wear-prone parts such as the supercharging mechanism and the transmission system of the cold rolling equipment to detect the wear state in real time. According to the monitoring data of the wear detection device, the equipment wear feature parameters are extracted. Finally, the obtained free section length, cold rolling control tension, cold rolling control speed and equipment wear feature set are collected together to construct an equipment feature set database of the cold rolling equipment, which is used as the overall state feature of the cold rolling equipment during operation, and provides equipment state constraints for subsequent positioning control.

Constructing a real-time measurement database of the strip steel, wherein the real-time measurement database is obtained by establishing real-time interaction point collection, and the data in the real-time measurement database includes thickness data and roughness data;

Furthermore, the embodiment of the present application also includes:

Acquire width data of the steel strip, and use the width data as a width constraint;

Collect raw material data of the strip steel, establish a raw material database, analyze the uniformity of the transverse thickness of the strip steel in the raw material database, and generate uniform thickness constraints;

Analyze the uniformity of the transverse roughness of the strip steel in the raw material database and generate a uniformity constraint for the roughness;

The layout density of the real-time interaction points is configured through the width constraint, the thickness uniformity constraint and the roughness uniformity constraint, and the real-time measurement database is established according to the layout density configuration result.

Furthermore, the embodiment of the present application also includes:

Taking the center point of the steel strip as the symmetry point, a symmetry sampling point list is established, wherein each symmetry point in the symmetry sampling point list is provided with an associated mapping;

Execute scheme matching of the symmetrical sampling point list through the arrangement density configuration result, and distribute acquisition sensors based on the scheme matching result, wherein the acquisition sensors include thickness sensors and roughness sensors;

Data is collected through the thickness sensor and the roughness sensor, and the real-time measurement database is established through corresponding association mapping.

In a preferred embodiment, first, a width measuring instrument is set at the entrance of the cold rolling equipment, and the real-time width data of the strip is obtained through the width measuring instrument, and the obtained width data is used as the width constraint condition for the subsequent real-time interaction point layout. Secondly, a number of thickness measurement sensors are set at equal intervals along the horizontal direction of the strip steel raw material. When the strip steel raw material passes through a number of thickness measurement sensors, the strip steel raw material is subjected to a horizontal multi-point thickness scan to obtain the thickness data of the strip steel at different horizontal positions, and the collected multi-point thickness data are summarized to construct a raw material database of the strip steel raw material. Thirdly, the uniformity of the thickness data at each horizontal position is analyzed to determine whether the thickness data distribution meets the uniformity requirement, and the uniform thickness constraint condition of the strip steel raw material is generated. At the same time, a number of roughness measurement sensors are set to analyze the roughness data in the raw material database, determine the uniformity of the horizontal roughness of the raw material strip steel, and generate a uniform roughness constraint condition.

Then, the generated width constraint, thickness uniformity constraint and roughness uniformity constraint are read; within the width range of the strip, the thickness uniformity constraint and the roughness uniformity constraint are comprehensively considered, and the real-time interactive measurement points are reasonably arranged in density to obtain the arrangement density configuration result. For example, a higher density of measurement points can be set in areas where the thickness and roughness change dramatically, while a lower density of measurement points can be set in areas where the changes are relatively uniform.

Subsequently, the center point of the strip surface is taken as the symmetry base point, and multiple symmetric sampling points are established within the width of the strip. Each symmetric sampling point is mapped with the center point to establish a symmetric sampling point list of the strip. Next, the generated layout density configuration result is read, which contains the optimized measurement point density distribution scheme for each area of the cold rolling zone of the strip; the constructed symmetric sampling point list is loaded, and based on the layout density configuration result, the appropriate symmetric sampling point is matched and selected according to the measurement accuracy requirements, signal transmission distance and other factors. At the same time, thickness sensors and roughness sensors are set at the selected symmetric sampling point positions to complete the distribution of the acquisition sensors. After that, the strip passes through the set thickness sensors and roughness sensors in turn during the cold rolling process. The thickness sensors and roughness sensors collect the corresponding thickness data and roughness data in real time when the strip passes, and transmit them to the data acquisition card in real time. The data acquisition card associates the thickness data and roughness data with the center point of the strip according to the preset association mapping relationship in the symmetric sampling point list to build a real-time measurement database for the strip.

Performing deviation evaluation based on the device feature set and the real-time measurement database to generate a window correction factor;

Further, as shown in FIG2 , this step includes:

Configure the distance impact factor of each mapping, wherein the distance impact factor is set with the center point as the distance zero point;

Calculating the thickness difference and roughness difference of each mapping, performing deviation judgment and calculation through the thickness difference, the roughness difference and the distance influencing factor, and generating a raw material correction factor;

The raw material correction factor is corrected according to the equipment feature set, and a window correction factor is generated according to the correction result.

Preferably, this step also includes:

Establishing a window correction network, wherein the window correction network is constructed through big data;

Performing network configuration of the window correction network through the device feature set, and inputting the raw material correction factor into the window correction network;

The window correction factor is generated by processing the original material correction factor based on the window correction network.

In a preferred embodiment, the window correction factor is used to predict and evaluate the potential deviation of the strip in the subsequent cold rolling process in advance, and to compensate for the deviation before it occurs, so as to avoid drastic correction when the deviation is too large and cause control abnormality. First, according to the list of symmetrical sampling points, the distance data from each symmetrical sampling point to the center point of the strip is extracted, and the sampling point farthest from the center point of the strip is selected, and its distance is set to the maximum distance value. Secondly, taking the center point of the strip as the zero distance base point, the distances of other sampling points are mapped to the range of 0-maximum distance value to obtain normalized distance data. At the same time, the corresponding relationship between distance and influencing factor is set according to experience, and the distance influencing factor corresponding to each normalized distance is obtained. Each symmetrical sampling point is associated with the obtained distance influencing factor to complete the configuration of the distance influencing factor.

Then, extract the thickness data and roughness data of each symmetrical sampling point in the real-time measurement database, calculate the difference between the data of each sampling point and the data of the center point, and obtain the thickness difference and roughness difference. Next, read the distance influence factor configured for each sampling point, multiply the thickness difference and roughness difference with their respective distance influence factors, and obtain the normalized deviation data. Subsequently, compare the normalized deviation data with the standard deviation threshold set according to the control accuracy to generate the raw material correction factor, which reflects the raw material deviation status of the strip.

A large amount of historical process data is collected from multiple cold rolling process production lines, including strip raw material parameters, cold rolling process measurement data, equipment characteristic parameters and cold rolling quality data. The collected data is cleaned, labeled and normalized to construct a cold rolling window correction data set. Then, based on the neural network structure of deep learning, a network model for window correction prediction is designed. The network model includes an input layer, multiple hidden layers and an output layer. The constructed cold rolling window correction data set is used to train the network model to obtain a window correction network constructed by big data. Then, according to the value of the parameters in the equipment feature set, the window correction network is adjusted to perform personalized configuration for the current equipment, and the network hyperparameters, such as the learning rate, are optimized to complete the performance optimization of the network. Subsequently, the generated raw material correction factor data is input into the window correction network, and the window correction network model is started for forward propagation calculation. Through multiple feature extraction and combination of the intermediate hidden layer of the neural network, the network outputs the prediction result of the strip deviation. The network output result is post-processed and converted into a standardized window correction factor, which integrates the prediction results of the raw material deviation and the influence of the current equipment parameters on the deviation.

Establishing a real-time deviation database, wherein the real-time deviation database is a deviation data set established by real-time deviation collection by a deviation correction sensor;

In the embodiment of the present application, in order to obtain the real-time deviation information of the strip during the cold rolling process, a correction sensor is arranged at a set number of collection points to realize the measurement and collection of the position of the strip. The correction sensor uses a variety of measurement methods such as image technology and laser technology to detect and transmit the real-time deviation information of the strip.

When the strip enters the detection range of the deviation correction sensor, the deviation correction sensor begins to continuously scan and measure the edge position of the strip, and obtains the deviation data of the strip at each detection point at a high frequency. These deviation data are analyzed and processed to form a deviation data set of the strip in real time, including the lateral deviation information of each detection point of the strip, including deviation direction, deviation distance and other parameters, reflecting the movement trajectory of the strip at different positions during the cold rolling process, providing a basis for achieving precise positioning control.

Determine a delay node, wherein the delay node is obtained based on the cold rolling control speed and a pre-collection distance, wherein the pre-collection distance is a distance between a collection point of the real-time measurement database and the real-time deviation database;

In an embodiment of the present application, after obtaining the real-time measurement database and the real-time deviation database, it is necessary to determine the delay node based on the cold rolling control speed and the pre-collection distance to achieve closed-loop control. Among them, the cold rolling control speed is the cold rolling speed control parameter configured according to the equipment feature set; the pre-collection distance is the distance between the collection point of the real-time measurement database and the collection point of the real-time deviation database. By dividing the pre-collection distance by the cold rolling control speed, the time from the real-time measurement point to the real-time deviation detection point can be obtained, which is the time parameter of the delay node, the time for the strip to move from the real-time measurement point to the real-time deviation detection point, and provides an accurate time reference for subsequent positioning control, thereby improving the real-time and accuracy of the control.

configuring timing control parameters according to the deviation data set, the window correction factor and the delay node;

In the embodiment of the present application, the configuration of the timing control parameters is divided into two parts: pre-control parameters and real-time correction parameters.

Before the strip enters the cold rolling process, the initialization parameters of the cold rolling equipment, including rolling speed, tension, working roll gap, etc., are configured in advance according to the deviation data set and the window correction factor, so as to realize the configuration of pre-control parameters and enable the strip to be quickly positioned in the initial stage of cold rolling. During the cold rolling process, when the strip passes through the real-time deviation detection point, the real-time deviation information of the strip is obtained, and the expected deviation of the strip to the next control execution point is calculated based on the configured delay node, and the control signal of the corresponding correction amount is generated to realize the configuration of the real-time correction parameters. By repeating the real-time detection and correction control, the deviation of the strip during the cold rolling process can be continuously compensated to achieve accurate rolling positioning.

By coordinating the pre-control and real-time correction parameters and configuring the timing control parameters, the positioning speed in the initial stage of cold rolling can be improved, and accurate positioning can be ensured throughout the cold rolling process, thereby improving the automated positioning capability and control quality.

The positioning control of the cold rolling equipment is performed by the timing control parameters.

In an embodiment of the present application, after completing the configuration of the timing control parameters, the timing control parameters are sent to the execution system of the cold rolling equipment. First, the pre-control parameters are sent to adjust the initialization parameters of the cold rolling equipment, including the initialization settings of speed, tension, working roll gap, etc., so that the strip is in an optimized ready state. Then, the real-time correction parameters are sent, and according to the expected deviation of the strip at each control execution point, the execution signal of the compensation control is output to drive the actuator to correct the deviation of the strip. During the cold rolling process, the positioning system monitors the deviation information of the strip in real time, and performs closed-loop control according to the delay nodes and real-time correction parameters to keep the strip in a precise rolling position, thereby realizing the automatic correction of the strip during the entire cold rolling process, so that the strip can obtain the required shape and position accuracy and improve the quality of cold rolling.

Furthermore, the embodiment of the present application also includes:

Establishing correction parameters of the correction equipment, predicting the correction limit based on the correction parameters and the equipment feature set, and setting the correction limit threshold;

Performing correction control evaluation on the window correction factor by using the correction limit threshold;

If the window correction factor cannot meet the correction limit threshold, speed feedback configuration is performed according to the breakthrough ratio of the window correction factor;

The window correction factor is updated according to the speed feedback result, and the timing control parameters are reconfigured according to the update result and the speed feedback result.

In a preferred embodiment, the correction parameters of the correction device are established, and the correction parameters include the speed range of the correction motor, the correction limit position and other parameters. Then, the theoretical maximum correction capacity of the correction device is calculated by combining the correction parameters of the correction device and the device feature set; the correction limit threshold judgment rule is set, for example, 80% of the theoretical maximum correction capacity can be taken as the threshold, and the dynamic threshold under different process conditions is obtained, so as to obtain the correction limit threshold, complete the correction limit prediction, and provide restriction conditions for the subsequent correction control evaluation.

Subsequently, the value of the window correction factor is compared with the correction limit threshold to determine whether the window correction factor exceeds the correction limit threshold, so as to evaluate the correction control. When it is determined that the window correction factor exceeds the correction limit threshold, that is, the window correction factor cannot meet the correction limit threshold, the positioning correction cannot be completed. At this time, the excess ratio of the window correction factor relative to the correction limit threshold is calculated, that is, the degree of correction that can exceed the limit under the current process conditions. Based on this, the speed feedback parameter is set. The speed feedback parameter defines the cold rolling speed reduction value under different excess ratios, and the cold rolling speed reduction value is proportional to the excess ratio. Subsequently, according to the excess ratio of the window correction factor, the speed feedback parameter is queried to obtain the current cold rolling speed value that should be reduced as the speed feedback result, that is, the speed feedback configuration is realized. Then, according to the cold rolling speed value that should be reduced, the window correction factor is updated to obtain the new value of the lateral deviation of the adjusted strip, and then the timing control parameters are reconfigured according to the adjusted window correction factor and the speed feedback result to achieve coordinated optimization of the positioning control scheme.

Furthermore, the embodiment of the present application also includes:

Configure the feedback cycle of the feedback constraint;

evaluating the positioning results of the cold rolling equipment within the feedback cycle to generate a feedback constraint set;

The generation control and compensation of the window correction factor are performed through the feedback constraint set.

In a preferred embodiment, first, according to the cold rolling process parameters and the needs of positioning control, the time interval of positioning feedback evaluation, that is, the feedback cycle of feedback constraints, is determined. For example, a short feedback cycle can be set for a high-speed cold rolling process to quickly respond to positioning deviations; a longer feedback cycle interval is set for a low-speed cold rolling process. For example, the time range of the feedback cycle is pre-set, such as 20-60 seconds, and then a certain value is selected as the parameter of the feedback cycle according to the specific process and equipment conditions to complete the configuration of the feedback cycle. Within the set feedback cycle time interval, according to the acquisition results of the sensor, the lateral deviation data of the strip at multiple time points in the cold rolling process is obtained, and multiple quality indicators such as positioning deviation, fluctuation frequency, and fluctuation range are comprehensively considered, and the weights and scoring rules of each indicator are set to obtain the evaluation score of the positioning quality. Then, according to the scoring rules set by experts, the positioning quality level is determined, and a feedback constraint set is generated, including positioning deviation constraints, fluctuation constraints, quality levels, etc.

Subsequently, the feedback constraint set is combined with the window correction factor generation process to determine the control compensation scheme. For example, if the feedback result shows that the positioning deviation is too large, the deviation weight in the window correction network can be increased to enhance the correction effect. If the feedback result shows that the positioning fluctuates too fast, the network filtering parameters can be adjusted to suppress the fluctuation frequency, etc., thereby achieving compensation and control for the window correction factor generation, improving the adaptability of positioning, and ensuring the positioning quality.

In summary, the automatic positioning method in a cold rolling process provided by the embodiment of the present application has the following technical effects:

Establish the equipment feature set of the cold rolling equipment. The equipment feature set is obtained through digital communication with the cold rolling equipment, and provides equipment-side information for evaluating the deviation of the strip. Construct a real-time measurement database for the strip. The real-time measurement database is obtained by establishing real-time interactive point collection, and provides strip-side information for deviation evaluation. Deviation evaluation is performed based on the equipment feature set and the real-time measurement database, and the window correction factor is generated to evaluate the current deviation state of the strip. Establish a real-time deviation database. The real-time deviation database is a deviation data set established by real-time deviation collection through the correction sensor, and provides real-time feedback for positioning control. Determine the delay node. The delay node is obtained based on the cold rolling control speed and the pre-collection distance. The pre-collection distance is the distance between the collection point of the real-time measurement database and the real-time deviation database. Considering the time delay from data collection to execution of positioning, it provides a control timing basis. Configure the timing control parameters based on the deviation data set, window correction factor and delay node, and formulate an accurate process control strategy. Perform positioning control of the cold rolling equipment through the timing control parameters, implement automatic and accurate correction positioning, and complete the positioning control of the entire cold rolling process.

Embodiment 2

Based on the same inventive concept as the automatic positioning method in a cold rolling process in the aforementioned embodiment, as shown in FIG3 , the embodiment of the present application provides an automatic positioning system in a cold rolling process, the system comprising:

The equipment feature establishment module 11 is used to establish an equipment feature set of the cold rolling equipment, wherein the equipment feature set is obtained through digital communication with the cold rolling equipment, and the equipment feature set includes equipment free section length, cold rolling control tension, cold rolling control speed, and equipment wear characteristics;

A real-time measurement data module 12 is used to construct a real-time measurement database for the strip steel, wherein the real-time measurement database is obtained by establishing real-time interaction point collection, and the data in the real-time measurement database includes thickness data and roughness data;

A window correction factor module 13, used to perform deviation evaluation based on the device feature set and the real-time measurement database to generate a window correction factor;

A real-time deviation data module 14 is used to establish a real-time deviation database, wherein the real-time deviation database is a deviation data set established by real-time deviation collection by a deviation correction sensor;

A delay node determination module 15 is used to determine a delay node, wherein the delay node is obtained based on the cold rolling control speed and a pre-collection distance, wherein the pre-collection distance is a distance between a collection point of the real-time measurement database and the real-time deviation database;

A timing control parameter module 16, configured to configure timing control parameters according to the deviation data set, the window correction factor and the delay node;

The equipment positioning control module 17 is used to perform positioning control of the cold rolling equipment through the timing control parameters.

Furthermore, the embodiment of the present application also includes a control parameter updating module, which includes the following execution steps:

Establishing correction parameters of the correction equipment, predicting the correction limit based on the correction parameters and the equipment feature set, and setting the correction limit threshold;

Performing correction control evaluation on the window correction factor by using the correction limit threshold;

If the window correction factor cannot meet the correction limit threshold, speed feedback configuration is performed according to the breakthrough ratio of the window correction factor;

The window correction factor is updated according to the speed feedback result, and the timing control parameters are reconfigured according to the update result and the speed feedback result.

Furthermore, the real-time measurement data module 12 includes the following execution steps:

Acquire width data of the steel strip, and use the width data as a width constraint;

Collect raw material data of the strip steel, establish a raw material database, analyze the uniformity of the transverse thickness of the strip steel in the raw material database, and generate uniform thickness constraints;

Analyze the uniformity of the transverse roughness of the strip steel in the raw material database and generate a uniformity constraint for the roughness;

The layout density of the real-time interaction points is configured through the width constraint, the thickness uniformity constraint and the roughness uniformity constraint, and the real-time measurement database is established according to the layout density configuration result.

Furthermore, the real-time measurement data module 12 further includes the following execution steps:

Taking the center point of the steel strip as the symmetry point, a symmetry sampling point list is established, wherein each symmetry point in the symmetry sampling point list is provided with an associated mapping;

Execute scheme matching of the symmetrical sampling point list through the arrangement density configuration result, and distribute acquisition sensors based on the scheme matching result, wherein the acquisition sensors include thickness sensors and roughness sensors;

Data is collected through the thickness sensor and the roughness sensor, and the real-time measurement database is established through corresponding association mapping.

Furthermore, the window correction factor module 13 includes the following execution steps:

Configure the distance impact factor of each mapping, wherein the distance impact factor is set with the center point as the distance zero point;

Calculating the thickness difference and roughness difference of each mapping, performing deviation judgment and calculation through the thickness difference, the roughness difference and the distance influencing factor, and generating a raw material correction factor;

The raw material correction factor is corrected according to the equipment feature set, and a window correction factor is generated according to the correction result.

Furthermore, the window correction factor module 13 further includes the following execution steps:

Establishing a window correction network, wherein the window correction network is constructed through big data;

Performing network configuration of the window correction network through the device feature set, and inputting the raw material correction factor into the window correction network;

The window correction factor is generated by processing the original material correction factor based on the window correction network.

Furthermore, the embodiment of the present application also includes a control compensation generation module, which includes the following execution steps:

Configure the feedback cycle of the feedback constraint;

evaluating the positioning results of the cold rolling equipment within the feedback cycle to generate a feedback constraint set;

The generation control and compensation of the window correction factor are performed through the feedback constraint set.

Any step of the method described above can be stored as a computer instruction or program in an unlimited computer memory, and can be called and recognized by an unlimited computer processor to implement any method in the embodiments of the present application, without any unnecessary restrictions.

Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application is intended to include these modifications and variations.

Claims (4)

1. An automatic positioning system in a cold rolling process, characterized in that the system comprises: An equipment feature establishment module, the equipment feature establishment module is used to establish an equipment feature set of the cold rolling equipment, the equipment feature set is obtained through digital communication with the cold rolling equipment, and the equipment feature set includes equipment free section length, cold rolling control tension, cold rolling control speed, and equipment wear characteristics; A real-time measurement data module, which is used to construct a real-time measurement database for the strip steel. The real-time measurement database is obtained by establishing real-time interaction point collection. The data in the real-time measurement database includes thickness data and roughness data. A window correction factor module, the window correction factor module is used to perform deviation evaluation based on the device feature set and the real-time measurement database to generate a window correction factor; A real-time deviation data module, wherein the real-time deviation data module is used to establish a real-time deviation database, wherein the real-time deviation database is a deviation data set established by real-time deviation collection by a deviation correction sensor; A delay node determination module, the delay node determination module is used to determine a delay node, the delay node is obtained based on the cold rolling control speed and a pre-collection distance, the pre-collection distance is the distance between the collection points of the real-time measurement database and the real-time deviation database; A timing control parameter module, the timing control parameter module is used to configure timing control parameters according to the deviation data set, the window correction factor and the delay node; An equipment positioning control module, the equipment positioning control module is used to perform positioning control of cold rolling equipment through the timing control parameters; Wherein, the system further comprises: Acquire width data of the steel strip, and use the width data as a width constraint; Collect raw material data of the strip steel, establish a raw material database, analyze the uniformity of the transverse thickness of the strip steel in the raw material database, and generate uniform thickness constraints; Analyze the uniformity of the transverse roughness of the strip steel in the raw material database and generate a uniformity constraint for the roughness; The layout density of the real-time interaction points is configured by using the width constraint, the thickness uniformity constraint and the roughness uniformity constraint, and the real-time measurement database is established according to the layout density configuration result; Wherein, the system further comprises: Taking the center point of the steel strip as the symmetry point, a symmetry sampling point list is established, wherein each symmetry point in the symmetry sampling point list is provided with an associated mapping; Execute scheme matching of the symmetrical sampling point list through the arrangement density configuration result, and distribute acquisition sensors based on the scheme matching result, wherein the acquisition sensors include thickness sensors and roughness sensors; Collecting data through the thickness sensor and the roughness sensor, and establishing the real-time measurement database through corresponding association mapping; Wherein, the system further comprises: Configure the distance impact factor of each mapping, wherein the distance impact factor is set with the center point as the distance zero point; Calculating the thickness difference and roughness difference of each mapping, performing deviation judgment and calculation through the thickness difference, the roughness difference and the distance influencing factor, and generating a raw material correction factor; Performing correction on the raw material correction factor according to the equipment feature set, and generating a window correction factor according to the correction result; Wherein, the system further comprises: Establishing a window correction network, wherein the window correction network is constructed through big data; Performing network configuration of the window correction network through the device feature set, and inputting the raw material correction factor into the window correction network; The window correction factor is generated by processing the original material correction factor based on the window correction network. 2. The system according to claim 1, characterized in that the system further comprises: Establishing correction parameters of the correction equipment, predicting the correction limit based on the correction parameters and the equipment feature set, and setting the correction limit threshold; Performing correction control evaluation on the window correction factor by using the correction limit threshold; If the window correction factor cannot meet the correction limit threshold, speed feedback configuration is performed according to the breakthrough ratio of the window correction factor; The window correction factor is updated according to the speed feedback result, and the timing control parameters are reconfigured according to the update result and the speed feedback result. 3. The system according to claim 1, characterized in that the system further comprises: Configure the feedback cycle of the feedback constraint; evaluating the positioning results of the cold rolling equipment within the feedback cycle to generate a feedback constraint set; The generation control and compensation of the window correction factor are performed through the feedback constraint set. 4. An automatic positioning method in a cold rolling process, characterized in that the method is applied to an automatic positioning system in a cold rolling process according to claims 1-3, and the method comprises: Establishing a device feature set of the cold rolling equipment, wherein the device feature set is obtained through digital communication with the cold rolling equipment, and the device feature set includes a free section length of the equipment, a cold rolling control tension, a cold rolling control speed, and a wear feature of the equipment; Constructing a real-time measurement database of the strip steel, wherein the real-time measurement database is obtained by establishing real-time interaction point collection, and the data in the real-time measurement database includes thickness data and roughness data; Performing deviation evaluation based on the device feature set and the real-time measurement database to generate a window correction factor; Establishing a real-time deviation database, wherein the real-time deviation database is a deviation data set established by real-time deviation collection by a deviation correction sensor; Determine a delay node, wherein the delay node is obtained based on the cold rolling control speed and a pre-collection distance, wherein the pre-collection distance is a distance between a collection point of the real-time measurement database and the real-time deviation database; configuring timing control parameters according to the deviation data set, the window correction factor and the delay node; The positioning control of the cold rolling equipment is performed by the timing control parameters.