A method and system for evaluating the heating performance uncertainty of a continuous annealing furnace

Abstract

The application discloses a continuous annealing furnace heating performance uncertainty evaluation method and system, and relates to the technical field of heat treatment equipment state evaluation, and comprises the following steps: based on the heating mode of copper strip heat treatment, the heat energy input of the heating system and the heat absorbed by the copper strip per unit time are calculated; based on the resistance change rate of the electric heating element of the heating system and the air-fuel ratio deviation coefficient of the heating system, a system state factor is constructed; based on the heat energy input of the heating system, the heat absorbed by the copper strip per unit time and the system state factor, a heat energy transfer index is constructed; the mean value and the variance of the heat energy transfer index are calculated through the first-order second-moment method; based on the mean value and the variance of the heat energy transfer index, a target normal distribution to which the heat energy transfer index is subjected is constructed; and based on the target normal distribution, the heat energy transfer index is mapped into the probability distribution of a plurality of discrete score sections. The application alleviates the technical problems of possible deviation and even misjudgment existing in the traditional deterministic evaluation method.

Description

Technical Field

[0001] This invention relates to the field of heat treatment equipment condition evaluation technology, and in particular to a method and system for evaluating the uncertain heating performance of a continuous annealing furnace. Background Technology

[0002] Heat treatment is a crucial step in the manufacturing process of metallic materials, especially in the production of copper and its alloy strips. Its main function is to regulate the microstructure and mechanical properties of the material, achieving goals such as grain refinement, stress relief, hardness control, and improved plasticity. For rolled copper strips, there is a certain degree of work hardening and residual stress, which needs to be eliminated through heat treatment processes such as annealing. This process typically requires heating the strip to a set temperature range and holding it for a certain time to promote sufficient recrystallization. Heat treatment parameters (such as heating temperature, heating rate, holding time, and cooling rate) directly affect the final material properties. Improper control can lead to uneven microstructure, performance fluctuations, or a decline in surface quality. Therefore, heat treatment is not only an important process to ensure the stability of the finished copper strip performance, but also a core link affecting the yield and energy efficiency of the entire production line.

[0003] Currently, copper strip heat treatment mainly employs two categories: continuous annealing furnaces (hereinafter referred to as continuous annealing furnaces) and box-type annealing furnaces. Box-type annealing furnaces are suitable for small to medium batch production, offering advantages such as high temperature control accuracy and strong adaptability, but suffer from long batch processing cycles and lower thermal efficiency. In contrast, continuous annealing furnaces are suitable for large-scale continuous production. Their structure typically includes a heating zone, a holding zone, and a cooling zone, enabling rapid heating and uniform annealing during strip movement, resulting in high production efficiency and good product consistency. The heating methods for continuous annealing furnaces are mainly electric heating and gas heating. Electric heating offers fast heating response and high temperature control accuracy, making it suitable for alloys with high temperature control requirements; gas heating has lower operating costs, making it suitable for energy-sensitive large-scale production scenarios. Different furnace types and heating methods face varying technical challenges in actual operation, especially under high-temperature, long-term operation conditions, where the stability and efficiency of the heating system directly affect product quality and energy consumption levels.

[0004] The heating system of the annealing furnace is its core functional unit, and its temperature control capability determines whether the copper strip can reach a uniform and precise heating temperature at the appropriate time. If the temperature control system is slow to respond or the heat source efficiency decreases, it can easily cause localized overheating or underheating, ultimately affecting the grain structure and surface condition of the copper strip. In actual production, electric heating elements may experience resistance aging due to prolonged high-temperature operation, thus affecting power output; gas burners may suffer from incomplete combustion or flame deviation due to carbon buildup, blockage, or nozzle wear, leading to uneven heat distribution. Furthermore, improper control of the air-fuel mixture ratio (air-fuel ratio) can cause increased energy consumption and furnace temperature fluctuations. These phenomena not only manifest as equipment performance degradation but also exhibit significant randomness and uncertainty—such as fluctuations in heat flux density, drift of temperature sensing elements, noise in flow signals, and time-varying nature of operating condition disturbances. These factors work together to make it difficult to accurately characterize the true performance of the heating process using a single deterministic indicator.

[0005] Traditional deterministic evaluation methods often assume accurate measurement parameters and stable operating conditions, thus ignoring the influence of measurement errors, sensor drift, and operational disturbances that are common in real industrial environments. This leads to overly idealized evaluation results that fail to accurately reflect the equipment's operating status and potential risks. In complex systems like continuous annealing furnaces, which are highly coupled and subject to multiple disturbances, failure to quantify these uncertainties can result in biased or even misjudged evaluation conclusions. Summary of the Invention

[0006] To address the aforementioned technical problems in the existing technology, embodiments of the present invention provide a method and system for evaluating the uncertain heating performance of a continuous annealing furnace. The technical solution is as follows: On the one hand, a method for evaluating the uncertainty of heating performance of a continuous annealing furnace is provided. The method includes: calculating the heat input of the heating system and the heat absorbed by the copper strip per unit time based on the heating method of copper strip heat treatment; constructing a system state factor based on the resistance change rate of the electric heating element of the heating system and the air-fuel ratio deviation coefficient of the heating system; constructing a heat transfer index based on the heat input of the heating system, the heat absorbed by the copper strip per unit time, and the system state factor; calculating the mean and variance of the heat transfer index using the first and second moment methods based on the measurement uncertainty of the heat transfer index; constructing a target normal distribution that the heat transfer index follows based on the mean and variance of the heat transfer index; and mapping the heat transfer index to a probability distribution of multiple discrete scoring segments based on the target normal distribution.

[0007] Optionally, the heating method includes electric heating; the calculation formula for the heat energy input of the heating system includes:

[0008] In the formula, U(t) and I(t) are the voltage and current of the heating system, respectively, R(t) is the resistance of the electric heating element of the heating system, and t represents time; The formula for calculating the heat absorbed by the copper strip per unit time includes:

[0009] In the formula, m represents the mass of the copper strip, c represents the specific heat capacity of the copper strip, and T(t) represents the temperature of the copper strip.

[0010] Optionally, the heating method includes gas heating; the calculation formula for the heat energy input of the heating system includes:

[0011] In the formula, V gas (t) represents the gas consumption of the heating system per unit time, H gas The value of the gas is represented by 't', and time is represented by 't'. The formula for calculating the heat absorbed by the copper strip per unit time includes:

[0012] In the formula, m represents the mass of the copper strip, c represents the specific heat capacity of the copper strip, and T(t) represents the temperature of the copper strip.

[0013] Optionally, the mathematical expression for the system state factor includes:

[0014] In the formula, The system state factor is... and Here, R(t) is the weighting coefficient, R(t) is the resistance of the electric heating element in the heating system, and t represents time. The air-fuel ratio deviation coefficient of the heating system.

[0015] Optionally, the mathematical expression for the heat transfer index includes:

[0016] In the formula, Let Q be the heat transfer index. input (t) represents the thermal energy input of the heating system, Q mat (t) represents the heat absorbed by the copper strip per unit time. , which is the system state factor.

[0017] Optionally, the probability distribution of the plurality of discrete scoring segments includes:

[0018] In the formula, S represents a discrete scoring segment, k represents the number of the discrete scoring segment, and Pr represents the probability distribution. This indicates the heat transfer index. To map the interval of the heat transfer index to the k-th discrete score segment, Let be the standard normal cumulative function of the heat transfer index. and These are the mean and standard deviation of the heat transfer index, respectively.

[0019] On the other hand, a continuous annealing furnace heating performance uncertainty evaluation system is also provided, for implementing the continuous annealing furnace heating performance uncertainty evaluation method provided in the embodiments of the present invention; the system includes: a first calculation module, a first construction module, a second construction module, a second calculation module, a third construction module, and an evaluation module; wherein, the first calculation module is used to calculate the heat energy input of the heating system and the heat absorbed by the copper strip per unit time based on the heating method of copper strip heat treatment; the first construction module is used to construct the system based on the resistance change rate of the electric heating element of the heating system and the air-fuel ratio deviation coefficient of the heating system. The system comprises: a state factor; a second construction module, used to construct a heat transfer index based on the heat input of the heating system, the heat absorbed by the copper strip per unit time, and the system state factor; a second calculation module, used to calculate the mean and variance of the heat transfer index using the first and second moment methods based on the measurement uncertainty of the heat transfer index; a third construction module, used to construct a target normal distribution that the heat transfer index follows based on the mean and variance of the heat transfer index; and an evaluation module, used to map the heat transfer index into a probability distribution of multiple discrete scoring segments based on the target normal distribution.

[0020] On the other hand, an electronic device is also provided, including: a memory, a processor, and a computer program stored on the memory and running on the processor, wherein the processor executes the computer program to implement the method provided in the embodiments of the present invention.

[0021] On the other hand, a computer-readable storage medium is also provided, wherein program code is stored in the computer-readable storage medium, and the program code can be called by a processor to execute the method provided in the embodiments of the present invention.

[0022] This invention provides a method and system for evaluating the uncertainties in the heating performance of a continuous annealing furnace. It couples the endothermic response of the copper strip with the input energy of the heat source to construct a normalized heat transfer index that can be compared across heating methods. Operating status information is used to adjust this index to reflect the impact of equipment health on heat transfer efficiency. Measurement uncertainty is explicitly propagated to the evaluation index, and the scoring results are presented in a probability distribution format with scores in ten-point increments. This simultaneously provides both the most likely score and the expected score, mitigating the technical problems of potential biases or even misjudgments inherent in traditional deterministic evaluation methods. Attached Figure Description

[0023] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0024] Figure 1 This is a flowchart of a method for evaluating the uncertainty of heating performance of a continuous annealing furnace provided in an embodiment of the present invention; Figure 2 This is a schematic diagram of a continuous annealing furnace heating performance uncertainty evaluation system provided in an embodiment of the present invention. Detailed Implementation

[0025] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0026] In embodiments of the present invention, words such as "exemplarily," "for example," etc., are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" in the present invention should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of the word "exemplary" is intended to present the concept in a concrete manner. Furthermore, in embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one.

[0027] Figure 1 This is a flowchart of a method for evaluating the uncertain heating performance of a continuous annealing furnace according to an embodiment of the present invention. Figure 1 As shown, the method specifically includes the following steps: Step S102: Based on the heating method of copper strip heat treatment, calculate the heat energy input of the heating system and the heat absorbed by the copper strip per unit time.

[0028] Step S104: Construct system state factors based on the resistance change rate of the electric heating element of the heating system and the air-fuel ratio deviation coefficient of the heating system.

[0029] Step S106: Construct the heat transfer index based on the heat input of the heating system, the heat absorbed by the copper strip per unit time, and the system state factor.

[0030] Step S108: Based on the measurement uncertainty of the heat transfer index, calculate the mean and variance of the heat transfer index using the first and second moment methods.

[0031] Step S110: Based on the mean and variance of the heat transfer index, construct the target normal distribution that the heat transfer index follows.

[0032] Step S112: Based on the target normal distribution, the heat transfer index is mapped to the probability distribution of multiple discrete scoring segments.

[0033] Specifically, the heating methods in step S102 include electric heating and gas heating.

[0034] In electric heating, energy input is primarily achieved through the Joule heating process via an electric heating element. Under a constant power setting P0, fluctuations in the actual output of voltage U(t) and current I(t) indicate a dynamic change in the resistance of the electric heating element, which can be expressed as:

[0035] According to Joule's law, the formula for calculating the heat energy input of a heating system includes:

[0036] In the formula, U(t) and I(t) are the voltage and current of the heating system, respectively, R(t) is the resistance of the electric heating element of the heating system, and t represents time; The formula for calculating the heat absorbed by a copper strip per unit time includes:

[0037] In the formula, m represents the mass of the copper strip, c represents the specific heat capacity of the copper strip, and T(t) represents the temperature of the copper strip.

[0038] Based on the ratio of input to output heat, the instantaneous heating efficiency can be defined as:

[0039] like A significant decrease in resistance without a corresponding increase in the heating rate of the copper strip may indicate abnormal resistance, increased heat loss, or decreased power transfer efficiency.

[0040] For annealing furnaces heated by natural gas, the energy input is provided by the combustion reaction. Let the gas consumption per unit time of the heating system be... The calorific value of gas is The formula for calculating the heat energy input of the heating system includes:

[0041] The formula for calculating the heat absorbed by a copper strip per unit time includes:

[0042] In the formula, m represents the mass of the copper strip, c represents the specific heat capacity of the copper strip, and T(t) represents the temperature of the copper strip.

[0043] The instantaneous heating efficiency of a gas heating system is defined as:

[0044] Considering the effect of air-fuel ratio on combustion efficiency, let the ideal air-fuel ratio be... The measured value is The air-fuel ratio deviation coefficient is defined as follows:

[0045] when When the value is too high, it often leads to unstable combustion, fluctuating furnace temperature, or decreased energy efficiency, and should be used as an early warning indicator of the operating status.

[0046] Specifically, the mathematical expressions for the system state factors include:

[0047] In the formula, For system state factors, and is a weighting coefficient used to control the contribution of resistance fluctuation rate and air-fuel ratio deviation coefficient to the overall thermal efficiency. R(t) is the resistance of the electric heating element of the heating system.

[0048] Specifically, the mathematical expression for the heat transfer index includes:

[0049] In the formula, Q is the heat transfer index. input (t) represents the thermal energy input of the heating system, Q mat (t) represents the heat absorbed by the copper strip per unit time. This is the system state factor. It should be noted that when the heating method is electric heating, Q... input (t)=P input (t).

[0050] The embodiments of the present invention provide The indicators can be used for real-time monitoring, trend analysis, and horizontal comparison of the operating performance and energy efficiency of different heat source systems.

[0051] In actual industrial operation, the measurement data of heat treatment equipment (such as current, voltage, gas flow rate, and temperature rise rate) are affected by factors such as sensor accuracy, sampling frequency, and environmental interference, thus introducing non-negligible measurement uncertainties. If these uncertainties are not quantitatively described and propagated, the evaluation results may be biased or even misjudged. Therefore, this invention introduces uncertainty quantification technology to address the heat transfer index. Uncertainty assessment is performed to improve the reliability of the evaluation results.

[0052] For each measurement Specify measurement variance Error propagation is performed using the first and second moment methods, with approximate assumptions. If the perturbation can be linearized, then...

[0053] Different measurements can be considered independent, and the covariance term is 0.

[0054] For ease of implementation, this invention provides partial derivatives for commonly used terms (for electric heating, only partial derivatives are provided for gas heating). Substituting the values ​​will yield similar results): For electric heating: remember .but

[0055] The partial derivative is (substituting the value at the estimation point):

[0056] in,

[0057] For gas heating, remember ,but

[0058] And about The same as above.

[0059] Substituting these partial derivative values The linearized perturbation is used to obtain the variance. mean Point-based estimated heat transfer index .

[0060] Then continuous variables The probability distribution mapped to discrete fractions: Selected set of fractions and define the corresponding Segmentation threshold, setting finite physical boundaries And divide evenly:

[0061] in, , Use the 99th percentile of history to avoid extreme stretching.

[0062] Assumption The uncertainty makes it approximately follow a normal distribution:

[0063] The probability distributions of multiple discrete rating segments include:

[0064] In the formula, S represents a discrete scoring segment, k represents the number of the discrete scoring segment, and Pr represents the probability distribution. Indicates the heat transfer index, To map the interval of the heat transfer index to the k-th discrete score segment, It is the standard normal cumulative function of the heat transfer index. and These are the mean and standard deviation of the heat transfer index, respectively.

[0065] The method for evaluating the uncertainty of heating performance of a continuous annealing furnace provided in this embodiment of the invention can be summarized and implemented using the algorithm shown in Table 1:

[0066] This invention uses actual production data to systematically verify the heating performance of a continuous annealing furnace.

[0067] (1) Experimental data and parameter settings: The experimental data were obtained from the continuous annealing furnace operation records of a copper strip manufacturer from July to August 2025. This annealing furnace was an electrically heated type with a rated power of 800kW, processing copper strips with a thickness of 0.8–1.5 mm and a width of 800–1200 mm. The data acquisition system recorded voltage, current, and copper strip temperature parameters in real time, with a sampling interval of 5 seconds.

[0068] The basic parameter settings are as follows: Copper strip density 8.96×10 3 kg / m 3 Ideal air-fuel ratio λ0 = 10.5 State factor weights: α = 0.6, β = 0.4 Measurement error settings: Voltage ±1%, Current ±1.5%, Temperature ±0.5%, Mass flow rate ±2%. (2) Calculation and evaluation results: According to the method provided in the embodiments of the present invention, the heat transfer index and its uncertainty propagation results for July and August 2025 are calculated.

[0069] Results in July 2025: Mean heat transfer index

[0070] Standard deviation

[0071] mean of state factors

[0072] Resistance fluctuation

[0073] Results in August 2025: Mean heat transfer index

[0074] Standard deviation

[0075] mean of state factors

[0076] Resistance fluctuation

[0077] The results show that the heat transfer index decreased significantly in August, while uncertainty increased, reflecting a deteriorating trend in equipment performance. The increase in resistance fluctuation indicates that the heating elements may be aging, leading to reduced heat transfer efficiency.

[0078] Continuous indicators Mapped to The scores are discrete, with each 10 points representing a different level. The threshold is set based on the 99th percentile of historical data, with an upper limit... lower limit Assuming Then the probability of each rating level is:

[0079] Mapping the continuous heat transfer index to discrete scores yields the following probability distribution for the scores over two months: Table 2. Probability distribution of heat transfer index scores (%)

[0080] The evaluation results are shown in Table 2. Table 2 shows that the scores in July were mainly concentrated in the range of 80 points and above (87.4% in total), indicating that the equipment was in good condition. However, in August, the probability of high scores decreased significantly, while the probability of low to medium scores increased, especially in the 70-80 point range, which rose from 9.8% to 32.7%, showing a trend of declining equipment performance. This change in score distribution provides a quantitative basis for equipment maintenance decisions.

[0081] As described above, this invention provides a method for evaluating the uncertainties in the heating performance of a continuous annealing furnace. It constructs a unified heat transfer evaluation framework based on three main lines: heat source input power, copper strip endothermic response, and equipment operating state factors. First, the input and absorbed energy are estimated using measured voltage, current, or gas consumption and the copper strip temperature rise rate. Then, the system state factors are used to adjust for operating condition fluctuations, resulting in a normalized heat transfer index for performance characterization. To quantify measurement uncertainties, a first- and second-moment method is used to propagate measurement errors to the mean and variance of the index, improving reliability under nonlinear or skewed conditions. Continuous indicators are divided into pre-defined intervals and mapped to discrete scores, with the output being the probability distribution for each level. This method is unified, reproducible, and feasible for online deployment, providing a quantitative basis for energy efficiency assessment and operation and maintenance decisions.

[0082] Figure 2 This is a schematic diagram of a continuous annealing furnace heating performance uncertainty evaluation system provided by an embodiment of the present invention. Figure 2 As shown, the system includes: a first calculation module 10, a first construction module 20, a second construction module 30, a second calculation module 40, a third construction module 50, and an evaluation module 60.

[0083] Specifically, the first calculation module 10 is used to calculate the heat energy input of the heating system and the heat absorbed by the copper strip per unit time based on the heating method of copper strip heat treatment; The first construction module 20 is used to construct the system state factor based on the resistance change rate of the electric heating element of the heating system and the air-fuel ratio deviation coefficient of the heating system. The second construction module 30 is used to construct the heat transfer index based on the heat energy input of the heating system, the heat absorbed by the copper strip per unit time, and the system state factor. The second calculation module 40 is used to calculate the mean and variance of the heat transfer index based on the measurement uncertainty of the heat transfer index using the first and second moment methods. The third construction module 50 is used to construct the target normal distribution that the heat transfer index follows based on the mean and variance of the heat transfer index. Evaluation module 60 is used to map the heat transfer index into a probability distribution of multiple discrete score segments based on the target normal distribution.

[0084] The present invention also provides an electronic device, including: a memory, a processor, and a computer program stored on the memory and running on the processor, wherein the processor executes the computer program to implement the method provided in the embodiments of the present invention.

[0085] The present invention also provides a computer-readable storage medium storing program code, which can be called by a processor to execute the method provided in the embodiments of the present invention.

[0086] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the devices, apparatuses, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0087] In the several embodiments provided by this invention, it should be understood that the disclosed devices, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another device, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0088] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0089] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0090] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0091] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for evaluating the uncertain heating performance of a continuous annealing furnace, characterized in that, The method includes: Based on the heating method of copper strip heat treatment, calculate the heat energy input of the heating system and the heat absorbed by the copper strip per unit time; Based on the resistance change rate of the electric heating element of the heating system and the air-fuel ratio deviation coefficient of the heating system, a system state factor is constructed. A heat transfer index is constructed based on the heat input of the heating system, the heat absorbed by the copper strip per unit time, and the system state factor. Based on the measurement uncertainty of the heat transfer index, the mean and variance of the heat transfer index are calculated by the first and second moment methods. Based on the mean and variance of the heat transfer index, a target normal distribution is constructed to which the heat transfer index follows. Based on the target normal distribution, the heat transfer index is mapped to a probability distribution of multiple discrete scoring segments.

2. The method according to claim 1, characterized in that, The heating method includes electric heating; the calculation formula for the heat energy input of the heating system includes: In the formula, U(t) and I(t) are the voltage and current of the heating system, respectively, R(t) is the resistance of the electric heating element of the heating system, and t represents time; The formula for calculating the heat absorbed by the copper strip per unit time includes: In the formula, m represents the mass of the copper strip, c represents the specific heat capacity of the copper strip, and T(t) represents the temperature of the copper strip.

3. The method according to claim 1, characterized in that, The heating method includes gas heating; the calculation formula for the heat energy input of the heating system includes: In the formula, V gas (t) represents the gas consumption of the heating system per unit time, H gas The value of the gas is represented by 't', and time is represented by 't'. The formula for calculating the heat absorbed by the copper strip per unit time includes: In the formula, m represents the mass of the copper strip, c represents the specific heat capacity of the copper strip, and T(t) represents the temperature of the copper strip.

4. The method according to claim 1, characterized in that, The mathematical expressions for the system state factors include: In the formula, The system state factor is... and Here, R(t) is the weighting coefficient, R(t) is the resistance of the electric heating element in the heating system, and t represents time. The air-fuel ratio deviation coefficient of the heating system.

5. The method according to claim 1, characterized in that, The mathematical expression for the heat transfer index includes: In the formula, Let Q be the heat transfer index. input (t) represents the thermal energy input of the heating system, Q mat (t) represents the heat absorbed by the copper strip per unit time. , which is the system state factor.

6. The method according to claim 1, characterized in that, The probability distributions of the multiple discrete scoring segments include: In the formula, S represents a discrete scoring segment, k represents the number of the discrete scoring segment, and Pr represents the probability distribution. This indicates the heat transfer index. To map the interval of the heat transfer index to the k-th discrete score segment, Let be the standard normal cumulative function of the heat transfer index. and These are the mean and standard deviation of the heat transfer index, respectively.

7. A system for evaluating the uncertain heating performance of a continuous annealing furnace, characterized in that, This system is used to implement the method for evaluating the uncertainty of heating performance of a continuous annealing furnace as described in any one of claims 1-6; the system comprises: a first calculation module, a first construction module, a second construction module, a second calculation module, a third construction module, and an evaluation module; wherein, The first calculation module is used to calculate the heat energy input of the heating system and the heat absorbed by the copper strip per unit time based on the heating method of copper strip heat treatment; The first construction module is used to construct a system state factor based on the resistance change rate of the electric heating element of the heating system and the air-fuel ratio deviation coefficient of the heating system; The second construction module is used to construct a heat transfer index based on the heat energy input of the heating system, the heat absorbed by the copper strip per unit time, and the system state factor; The second calculation module is used to calculate the mean and variance of the heat transfer index based on the measurement uncertainty of the heat transfer index using the first and second moment methods. The third construction module is used to construct a target normal distribution that the heat transfer index follows based on the mean and variance of the heat transfer index. The evaluation module is used to map the heat transfer index into a probability distribution of multiple discrete scoring segments based on the target normal distribution.

8. An electronic device, characterized in that, include: A memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor, when executing the computer program, implements the method as claimed in any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium contains program code that can be invoked by a processor to execute the method as described in any one of claims 1 to 6.

Images