Cold-rolled strip steel cleaning liquid temperature control system and control method

Abstract

The invention provides a cold-rolled strip steel cleaning liquid temperature control system and method, and relates to the technical field of cold-rolled strip steel cleaning, temperature data and power data of cleaning liquid in different time periods are obtained, and a mapping relational expression of the temperature data and the power data is established; comparing the temperature obtained through the mapping relation with a preset temperature threshold, identifying a temperature abnormal region, performing temperature monitoring on the temperature abnormal region, and determining a temperature fluctuation value; continuously acquiring a fluctuation value of the temperature, and performing abnormal temperature monitoring according to the fluctuation value of the temperature and a fluctuation threshold value to obtain a fluctuation abnormity early warning time period; and a PID algorithm is adopted to adjust the temperature corresponding to the early warning time period.

Description

Technical Field

[0001] The invention relates to the technical field of cold-rolled steel strip cleaning, and in particular to a cold-rolled steel strip cleaning liquid temperature control system and a control method. Background Art

[0002] During the cold-rolled strip cleaning process, the temperature of the cleaning fluid directly affects the surface oil removal effect, the activity of chemical agents and the risk of strip oxidation. Different steel grades (such as high-strength steel, silicon steel) and specifications (thickness 0.3-3.0mm) have significantly different temperature control requirements. The strip cleaning process is a very important part of the cold rolling processing line. After the strip is cold-rolled, rolling oil will remain on its surface, and dust and other pollution will occur during the transportation and temporary storage of the steel coils. If the steel coils are not cleaned properly, defects will occur in the subsequent processes, and the impact on coated substrates is particularly obvious.

[0003] An important part of the cleaning process is electrolytic cleaning, and the main factors affecting the electrolytic cleaning effect are the control of electrolyte concentration and temperature. The electrolyte for cleaning strip steel generally uses a NaOH electrolyte with a concentration of 2%-5%, and the temperature is generally controlled at 40-80°C. The high temperature can enhance the emulsification effect, enhance the degreasing effect, reduce the resistance of the solution, improve the conductivity, and speed up the degreasing process.

[0004] Traditional systems mostly use simple temperature control algorithms, rely on manual experience to set parameters, have a response lag (delay > 1s), and are difficult to cope with dynamic conditions (such as changes in strip speed and ambient temperature fluctuations). In addition, only monitoring the local temperature of the cleaning tank cannot reflect the overall temperature field distribution, which can easily lead to local overheating, causing a large amount of solution to evaporate and increase the burden on the purification equipment. If the local temperature is too low, it will reduce the cleaning effect. Summary of the invention

[0005] In order to solve the above technical problems, the present invention proposes a method for controlling the temperature of a cold-rolled steel strip cleaning liquid, comprising the following steps: S1. Obtain temperature data and power data of the cleaning liquid in different time periods, and establish a mapping relationship between the temperature data and the power data; S2. Compare the temperature obtained by the mapping relationship with a preset temperature threshold, identify the temperature abnormality area, perform temperature monitoring on the temperature abnormality area, and determine the temperature fluctuation value; S3, continuously obtaining temperature fluctuation values, performing abnormal temperature monitoring according to the temperature fluctuation values ​​and fluctuation thresholds, and obtaining an abnormal fluctuation warning time period; S4. Use PID algorithm to adjust the temperature corresponding to the warning time period.

[0006] In a preferred embodiment, in step S1, a mapping relationship between the temperature data and the power data of the system is established: Where T(p) represents the temperature obtained according to the mapping relationship, p represents the power, , and represents the fitting coefficient.

[0007] In a preferred embodiment, in step S1, the fitting coefficient is determined: in, and They represent the power loss and target power of the i-th sampling point respectively, and n represents the total number of sampling points.

[0008] In a preferred embodiment, in step S2, if the temperature obtained by the mapping relationship Not greater than the temperature threshold , the corresponding temperature change area is marked as a normal temperature area; otherwise, the corresponding temperature change area is marked as an abnormal temperature area; The minimum mean square error algorithm is used to correct and monitor the test data corresponding to the temperature abnormality area to obtain the corrected temperature fluctuation value.

[0009] In a preferred embodiment, in step S3, a sliding window algorithm is used to analyze the temperature fluctuation value. The window length of the sliding window algorithm is w, the window step length is s, and the sequence of temperature changes over time is T(t), where t represents the time point, and the temperature fluctuation value in the sliding window is The calculation formula is: in, Represents the average temperature in the kth sliding window, and the calculation formula is: The starting time of the sliding window , where k represents the window number; The calculated fluctuation value With the preset fluctuation threshold For comparison, if Less than or equal to , then the period corresponding to the fluctuation value is marked as the continuous monitoring period; if Greater than , then the period corresponding to the fluctuation value is Mark as warning time period.

[0010] In a preferred embodiment, in step S4, according to the temperature fluctuation value in the kth sliding window Use PID formula to calculate temperature control value : According to the temperature control value , calculate the temperature compensation value C(t): Among them, K p , K i , Kd represents the preset proportional coefficient, integral coefficient and differential coefficient, and A and B represent the preset compensation coefficients.

[0011] The present invention also proposes a cold-rolled steel strip cleaning liquid temperature control system, which is used to implement the above-mentioned cold-rolled steel strip cleaning liquid temperature control method, including: a data acquisition and storage module, a temperature power mapping module, an abnormal temperature detection module, a real-time warning module and an intelligent control module; The data acquisition and storage module is used to acquire and store the temperature data of the cleaning liquid and the power data of the heater in different time periods; The temperature-power mapping module is used to establish a mapping relationship between temperature data and power data; The abnormal temperature detection module is used to compare the temperature obtained through the mapping relationship with the preset temperature threshold to identify the temperature abnormal area; The real-time warning module is used to continuously monitor the temperature fluctuation value, perform abnormal temperature monitoring according to the temperature fluctuation value and the fluctuation threshold, and obtain the abnormal fluctuation warning time period; The intelligent control module is used to adjust the temperature corresponding to the warning time period by using a PID algorithm.

[0012] Compared with the prior art, the present invention has the following beneficial technical effects: By establishing a temperature-power mapping relationship, the system can accurately adjust the power according to the actual required temperature. During the cleaning process, when the temperature is not too high, the system can reduce the power output to avoid energy waste; and when it is necessary to quickly heat up or maintain a high temperature, it can provide sufficient power in time. In the early stage of cleaning, when the temperature needs to rise quickly, the system can increase the power output according to the mapping relationship; in the later stage of cleaning, when the temperature stabilizes, the power can be appropriately reduced to achieve rational use of energy.

[0013] By monitoring and adjusting the temperature in real time, the overheating or overcooling that may occur in traditional control methods is avoided. Overheating not only wastes energy, but may also damage the cleaning equipment and strip; overcooling affects the cleaning effect. This method can accurately control the temperature according to actual conditions and reduce unnecessary energy consumption.

[0014] Stable temperature control can reduce the thermal shock of temperature fluctuations on cleaning equipment and reduce the risk of equipment damage due to frequent temperature changes. By effectively controlling temperature fluctuations, the service life of the equipment is extended and the cost of equipment maintenance and replacement is reduced.

[0015] By continuously monitoring temperature fluctuations and performing abnormal temperature monitoring, temperature anomalies caused by equipment failures can be discovered in a timely manner. The entire temperature control process is automated. From data acquisition, mapping relationship establishment, anomaly monitoring to temperature adjustment, the system can automatically complete a series of operations, reducing manual intervention and improving production efficiency and management levels. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative labor.

[0017] Figure 1 It is a flow chart of the opening control method of the electric regulating valve of the present invention.

[0018] Figure 2 It is a temperature control circuit diagram of the temperature control module of the present invention.

[0019] Figure 3 This is a monitoring control circuit diagram of the temperature control module of the present invention. DETAILED DESCRIPTION

[0020] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0021] In the drawings of the specific embodiments of the present invention, in order to better and more clearly describe the working principles of the various components in the system, the connection relationship of the various parts in the device is shown, which only clearly distinguishes the relative position relationship between the various components, and cannot constitute a limitation on the signal transmission direction, connection sequence and size, dimensions and shape of the components or structures. Example 1

[0022] like Figure 1 FIG. 1 is a flow chart of a method for controlling the temperature of a cold-rolled steel strip cleaning liquid according to the present invention. The method comprises the following steps: S1. Acquire temperature data and power data of the cleaning liquid in different time periods, and establish a mapping relationship between the temperature data and the power data.

[0023] In the cold-rolled strip production process, precise control of the cleaning fluid temperature has a key impact on product surface quality and production efficiency. This step focuses on the dynamic mapping relationship between the cleaning fluid temperature and the heater power.

[0024] The collected cleaning fluid temperature data (dependent variable) and heater power data (independent variable) are cleaned, outliers are removed, and they are aligned and matched according to the time dimension to form a structured data set.

[0025] According to the structured data set, a quadratic polynomial model is selected as the mathematical expression of the mapping relationship, which can capture the nonlinear relationship between temperature and power.

[0026] Specifically, the mapping relationship between the system temperature data and the heater power data is: Where T(p) represents the temperature obtained according to the mapping relationship, p represents the power, , and represents the fitting coefficient.

[0027] It should be noted that in the above formula, although the temperature data and power data belong to different physical quantities, there is no need to pay attention to the difference in units, only the values. When establishing the mapping relationship, the mathematical model focuses on the relationship between the variable values, rather than whether the units are consistent. The unit difference does not affect the calculation of the fitting coefficient.

[0028] In a preferred embodiment, a smooth curve of the mapping relationship is constructed with a piecewise cubic polynomial to ensure the continuity of the first-order and second-order derivatives, which is suitable for the physical process of continuous change of temperature with power and can more naturally reflect the data trend. In addition, in the case of uneven data errors, weights are assigned to different data points (such as high weights for points with small errors), minimizing the weighted square sum of errors and optimizing the fit of the fitting results to the key data, which is suitable for scenarios with inconsistent temperature or power measurement accuracy.

[0029] In a preferred embodiment, a quadratic polynomial model is fitted by the least squares method to describe the power loss at the i-th sampling point. With target power Specifically, the formula determines the three fitting coefficients of the model by minimizing the sum of squared errors between the predicted values ​​and the actual observed values. , and .

[0030] The fitting coefficients are determined by the following model: in, and Represent the power loss and target power of the i-th sampling point respectively, and n represents the total number of sampling points. The above model establishes a quadratic nonlinear relationship between power loss and target power, which can capture the impact of power change on loss.

[0031] By minimizing the sum of squared errors, the optimal fitting coefficients are obtained. , and , make the model prediction value as close as possible to the actual observed value, use the fitting model to predict the power loss under different target powers, or analyze the trend of loss changing with power.

[0032] S2, the temperature obtained by the mapping relationship of step S1 With preset temperature threshold Make comparisons, identify temperature abnormality areas, conduct temperature monitoring on temperature abnormality areas, and determine temperature fluctuation values.

[0033] like , then the corresponding temperature change area is marked as the normal temperature area; if , the corresponding temperature change area is marked as the temperature abnormal area.

[0034] The minimum mean square error algorithm is used to correct the test data corresponding to the temperature abnormality area, obtain the corrected data, and determine the temperature fluctuation value.

[0035] Table 1 Process simulation of temperature monitoring and correction (for illustration only, not representing actual working values) Through this table, you can clearly see the entire process of temperature monitoring and correction, including how to identify abnormal areas, correct data, and calculate fluctuation values.

[0036] For areas with abnormal temperature, further temperature monitoring can be carried out to determine the temperature fluctuation value.

[0037] Preferably, the minimum mean square error algorithm is used to correct the test data in the temperature abnormality area to obtain the corrected data and determine the temperature fluctuation value.

[0038] S3. Continuously monitor the temperature fluctuation value, perform abnormal temperature monitoring according to the temperature fluctuation value and a preset fluctuation threshold, and obtain an abnormal fluctuation warning time period.

[0039] First, the sliding window algorithm is used to analyze the temperature fluctuation value. The window length of the sliding window algorithm is w, the window step is s, and the sequence of temperature changes over time is T(t), where t represents the time point.

[0040] Temperature fluctuations within the sliding window The calculation formula is: in, Represents the average temperature in the kth sliding window, and the calculation formula is: The starting time of the sliding window for: , where k represents the window number.

[0041] The calculated fluctuation value With the preset fluctuation threshold For comparison. Less than or equal to , then the period corresponding to the fluctuation value is Mark as continuous monitoring period; if Greater than , then the period corresponding to the fluctuation value is Mark as warning time period.

[0042] The temperature fluctuation characteristics are analyzed by the sliding window algorithm, and the warning time period is identified by combining the preset fluctuation threshold. By quantifying the temperature fluctuation degree and comparing it with the fluctuation threshold, potential abnormal time periods are discovered.

[0043] In order to more intuitively demonstrate the calculation process of the sliding window, the following is a schematic diagram in Table 2 Table 2 Calculation data of sliding window S4. Use PID algorithm to adjust the temperature corresponding to the warning time period and optimize the working state and transient response of the control system under dynamic load.

[0044] Specifically, the temperature control value is calculated using a PID formula, and the temperature compensation value is calculated according to the temperature control value through a preset compensation function.

[0045] The temperature control value and temperature compensation value are calculated by the following formula: According to the temperature fluctuation value in the kth sliding window Use PID formula to calculate temperature control value : in, The term takes into account the accumulation of historical errors and is used to eliminate steady-state errors; The term takes into account the rate of change of the error and is used to suppress rapidly changing interference.

[0046] According to the temperature control value , calculate the temperature compensation value C(t): Among them, K p , K i , K drepresents the preset proportional coefficient, integral coefficient and differential coefficient, and A and B represent the preset compensation coefficients. The above formula uses logarithmic form to convert the temperature control value Converted into temperature compensation value C(t).

[0047] Table 3 shows the calculation process in an exemplary manner. Through this table, we can clearly see the calculation process of the PID controller and the compensation function, as well as how to calculate the final temperature compensation value based on the temperature fluctuation value. Table 3 Calculation process numerical display Example 2 This embodiment proposes a schematic diagram of the structure of a cold-rolled strip cleaning liquid temperature control system, which includes: a data acquisition and storage module, a temperature power mapping module, an abnormal temperature detection module, a real-time warning module and an intelligent control module.

[0048] The data acquisition and storage module is used to obtain and store the temperature data of the cleaning liquid and the power data of the heater in different time periods, including: temperature sensor group, power monitoring unit, and time series database.

[0049] Temperature sensor group: distributed in key areas of the cleaning tank to collect liquid temperature data in real time.

[0050] Power monitoring unit: Connect to the heating / cooling equipment to obtain the current equipment operating power data.

[0051] Time series database: stores temperature data and heater power data in time series, supporting historical data backtracking analysis.

[0052] The temperature-power mapping module is used to establish a mapping relationship between temperature data and power data, and includes a data preprocessing unit, a mapping relationship modeling unit, and a model updating unit.

[0053] Data preprocessing unit: filtering, denoising and normalizing the original data; Mapping relationship modeling unit: Use machine learning algorithms (such as LSTM neural network or regression analysis) to establish a temperature-power dynamic mapping model; Model update unit: Regularly iteratively optimizes mapping model parameters using the latest data.

[0054] The abnormal temperature detection module is used to compare the temperature obtained through the mapping relationship with the preset temperature threshold to identify the temperature abnormal area. It includes: a threshold comparator, a fluctuation analysis unit, and an abnormal area positioning unit.

[0055] Threshold comparator: preset upper / lower temperature thresholds and compare with current temperature in real time.

[0056] Fluctuation analysis unit: calculates temperature fluctuation values ​​based on sliding window algorithm; The real-time warning module is used to continuously monitor the temperature fluctuation value, perform abnormal temperature monitoring according to the temperature fluctuation value and fluctuation threshold, and obtain the abnormal fluctuation warning time period.

[0057] The intelligent control module is used to adjust the temperature corresponding to the warning time period using a PID algorithm.

[0058] In a preferred embodiment, the temperature control system of the present invention also includes a system management module, which is used to coordinate data interaction and task scheduling among modules through a central controller, has a parameter configuration interface, supports threshold setting, control parameter adjustment and other operations, and stores operation records, alarm information and control parameter changes through a log recording system. Example 3

[0059] The zoned constant temperature control method is adopted to reduce the temperature difference in different positions of the tank, reduce the temperature difference fluctuation of each zone, and improve the control accuracy.

[0060] Zoned thermostatic control is a technology that can perform targeted control according to the temperature requirements of different spaces. Different areas have different requirements for temperature control. Zoned control technology can set up multiple independent temperature control areas according to actual needs, which not only improves flexibility but also reduces energy consumption. This technology maintains the set temperature range by precisely controlling the heat source. It is usually equipped with a temperature sensor that can monitor ambient temperature changes in real time and automatically adjust the output.

[0061] The temperature control module is sent to the power regulator through a main circuit breaker and a fast fuse. The power regulator controls multiple high-power liquid heaters respectively. Each liquid heater has a separate operating indicator light, which can intuitively see the operating status. At the same time, each heater has a separate circuit breaker, which can realize modular and flexible control; at the same time, it can avoid the failure of a single heater affecting the use of the entire system.

[0062] like Figure 2 The figure shows the temperature control circuit diagram of the temperature control module, which includes two parts: power supply start-up and temperature control instrument.

[0063] The power startup part includes: multiple circuit breakers, leakage detection devices, multiple heaters, and start-stop control devices.

[0064] 1Q in the temperature control circuit diagram is a circuit breaker with leakage protection, which is used to control the on and off of the circuit and automatically cut off the power supply when a fault such as leakage occurs in the circuit. The SL connected below is a leakage detection device.

[0065] Heater H obtains power through circuit breaker 1Q to realize heating function; DX1 and DX2 are grounding terminal blocks, among which DX2 has a protective grounding terminal PE, which is used to connect the grounding line of the equipment to ensure power safety.

[0066] 2Q is another circuit breaker, which cooperates with 1SB stop button, 2SB start button and 1K contactor to form a start-stop control device to realize the start-stop control of the circuit. When the 2SB start button is pressed, the coil of the 1K contactor is energized and attracted, and its normally open contact is closed to keep the circuit conducting; when the 1SB stop button is pressed, the coil of the 1K contactor loses power and the circuit is disconnected.

[0067] 1H is another heater, which realizes heating when 1K contactor is closed and the circuit operates normally. The temperature control instrument part includes: temperature sensor, temperature control instrument and fuse.

[0068] The temperature sensor LW6043 is responsible for collecting temperature signals. It has two sets of inputs (1+, 1-; 2+, 2-) and one set of outputs (1P+, 1P-). Temperature control instrument 2: Receives the temperature signal from the temperature sensor LW6043, monitors and controls the temperature, and controls the external device to adjust the temperature by comparing it with the set temperature.

[0069] When the circuit current of fuse 7F is too large, its fuse will melt and cut off the circuit, protecting the temperature control instrument and other equipment from damage.

[0070] like Figure 3 As shown, it is the monitoring and control circuit diagram of the temperature control module, which is mainly used for the control and monitoring of the heater, including: power input part, voltage monitoring part, current monitoring and control part, control module part and load part.

[0071] In the power input part, L11, L21, and L31 are the incoming lines of the three-phase AC power supply, providing power. The circuit breaker Q plays the role of circuit on-off control and short circuit and overload protection. When an abnormally large current appears in the circuit, the circuit breaker Q will automatically trip and cut off the power supply.

[0072] In the voltage monitoring part, SV is a voltage transformer, which is used to convert high voltage into low voltage in proportion. V1 and V2 are voltmeters. The voltage signal is obtained through the voltage transformer SV, and the two-phase voltage values ​​in the three-phase power supply are displayed in real time to monitor the power supply voltage status.

[0073] In the current monitoring and control part, the main contact of the AC contactor 1KM is used to control the power on and off of the heater. When the contactor coil is energized, the main contact is closed and the current can flow to the heater; when the power is off, the main contact is open and the power supply to the heater is cut off.

[0074] Current transformers A1-A6: Convert large currents into small currents in proportion to facilitate measurement and protection. Used with ammeters to monitor the current in the circuit.

[0075] When the current of fuse 1-3F is too large, the fuse will melt, cut off the circuit and protect the subsequent equipment.

[0076] In the control module part, ST30 is an intelligent temperature control module, which has multiple interfaces, such as I2, O1, O2, X1, M, GND, 1+, 1-, etc. I2 is the current signal input interface, O1 and O2 are control signal output interfaces, which are used to control other devices or feedback status; X1 and M are communication interfaces; GND is the ground terminal; 1+ and 1- are temperature sensor signal inputs, which are used to monitor the actual temperature.

[0077] In the load part, 1 is a zone heater: it contains two 100KW heating units (100KW I and 100KWII), which are connected in a triangle and obtain power from the N line and the three-phase line for heating. These heaters are the loads of the entire circuit, and their working status is regulated by the previous control and monitoring circuits.

[0078] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium or transmitted through the computer-readable storage medium. The computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server or a data center that includes one or more available media integrated. The available medium may be a magnetic medium, (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid state disk (SSD)), etc.

[0079] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed in the present application, and these modifications or replacements should be included in the protection scope of the present application. Therefore, the protection scope of the present application shall be based on the protection scope of the claims.

Claims

1. A method for controlling the temperature of a cold-rolled steel strip cleaning liquid, characterized in that: The steps include: S1. Obtain temperature data and power data of the cleaning liquid in different time periods, and establish a mapping relationship between the temperature data and the power data; S2. Compare the temperature obtained by the mapping relationship with a preset temperature threshold, identify the temperature abnormality area, perform temperature monitoring on the temperature abnormality area, and determine the temperature fluctuation value; S3, continuously obtaining temperature fluctuation values, performing abnormal temperature monitoring according to the temperature fluctuation values ​​and fluctuation thresholds, and obtaining an abnormal fluctuation warning time period; S4. Use PID algorithm to adjust the temperature corresponding to the warning time period.

2. The method for controlling the temperature of the cold-rolled steel strip cleaning liquid according to claim 1, characterized in that: In step S1, a mapping relationship between the temperature data and the power data of the system is established: Where T(p) represents the temperature obtained according to the mapping relationship, p represents the power, , and represents the fitting coefficient.

3. The method for controlling the temperature of the cold-rolled steel strip cleaning liquid according to claim 2, characterized in that: In step S1, the fitting coefficient is determined: in, and They represent the power loss and target power of the i-th sampling point respectively, and n represents the total number of sampling points.

4. The method for controlling the temperature of the cold-rolled steel strip cleaning liquid according to claim 2, characterized in that: In step S2, if the temperature obtained by the mapping relationship Not greater than the temperature threshold , then the corresponding temperature change area is marked as the normal temperature area; Otherwise, the corresponding temperature change area is marked as a temperature abnormality area; The minimum mean square error algorithm is used to correct and monitor the test data corresponding to the temperature abnormality area to obtain the corrected temperature fluctuation value.

5. The method for controlling the temperature of the cold-rolled steel strip cleaning liquid according to claim 4, characterized in that: In step S3, a sliding window algorithm is used to analyze the temperature fluctuation value. The window length of the sliding window algorithm is w, the window step length is s, and the sequence of temperature changes over time is T(t), where t represents the time point and the temperature fluctuation value in the sliding window is The calculation formula is: in, Represents the average temperature in the kth sliding window, and the calculation formula is: The starting time of the sliding window , where k represents the window number; the calculated fluctuation value With the preset fluctuation threshold For comparison, if Less than or equal to , then the period corresponding to the fluctuation value is Mark as continuous monitoring period; if Greater than , then the period corresponding to the fluctuation value is Mark as warning time period.

6. The method for controlling the temperature of the cold-rolled steel strip cleaning liquid according to claim 5, characterized in that: In step S4, according to the temperature fluctuation value in the kth sliding window Use PID formula to calculate temperature control value : According to the temperature control value , calculate the temperature compensation value C(t): Among them, K p , K i , K d represents the preset proportional coefficient, integral coefficient and differential coefficient, and A and B represent the preset compensation coefficients.

7. A cold-rolled steel strip cleaning liquid temperature control system, used to implement the cold-rolled steel strip cleaning liquid temperature control method according to any one of claims 1 to 6, characterized in that: include: Data acquisition and storage module, temperature power mapping module, abnormal temperature detection module, real-time warning module and intelligent control module; The data acquisition and storage module is used to acquire and store the temperature data of the cleaning liquid and the power data of the heater in different time periods; The temperature-power mapping module is used to establish a mapping relationship between temperature data and power data; The abnormal temperature detection module is used to compare the temperature obtained by the mapping relationship with the preset temperature threshold to identify the abnormal temperature area; The real-time warning module is used to continuously monitor the temperature fluctuation value, perform abnormal temperature monitoring according to the temperature fluctuation value and the fluctuation threshold, and obtain the abnormal fluctuation warning time period; The intelligent control module is used to adjust the temperature corresponding to the warning time period by using a PID algorithm.

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