Communication control system for copper tube annealing equipment parameters based on real-time characteristics of insulation materials

Abstract

This invention relates to the field of system communication and control technology, and in particular to a communication and control system for copper tube annealing equipment parameters based on the real-time characteristics of insulation materials. The system includes: a monitoring module comprising a temperature sensor array deployed inside and / or on the surface of the insulation material in the annealing furnace, used to collect real-time thermal state characteristic data of the insulation material; and to encapsulate the collected thermal state characteristic data in a communication format to generate a data frame to be transmitted conforming to a preset communication specification; and a control module, communicatively connected to the monitoring module, configured to: receive the data frame to be transmitted, parse to obtain the thermal state characteristic data; calculate the real-time thermal performance index of the insulation material based on the thermal state characteristic data; compare the real-time thermal performance index with a preset performance threshold, and generate control commands based on the comparison result. This system can compensate for temperature deviations caused by fluctuations in the thermal performance of the insulation material in real time, ensuring the consistency of the copper tube annealing process.

Description

Technical Field

[0001] This invention relates to the technical field of system communication control, and in particular to a communication control system for copper tube annealing equipment parameters based on the real-time characteristics of insulation materials. Background Technology

[0002] During the copper tube annealing process, the thermal properties of the insulation material in the annealing furnace will change dynamically with factors such as usage time, ambient humidity, and thermal fatigue. This will cause fluctuations in key parameters such as thermal conductivity and thermal resistance. These fluctuations will directly affect the stability of the temperature field inside the annealing furnace, causing the actual heating temperature to deviate from the process requirement temperature. Consequently, this will lead to unstable copper tube annealing quality issues such as uneven grain size and substandard mechanical properties.

[0003] Existing copper tube annealing equipment control systems typically rely solely on feedback adjustments based on the furnace air temperature, without considering the impact of changes in the thermal properties of the insulation material on the temperature field. This makes it impossible to compensate for temperature deviations caused by fluctuations in the thermal properties of the insulation material in real time, thus hindering the consistency of the copper tube annealing process. Summary of the Invention

[0004] This invention provides a communication control system for copper tube annealing equipment parameters based on the real-time characteristics of the insulation material, which can compensate for temperature deviations caused by fluctuations in the thermal properties of the insulation material in real time and ensure the consistency of the copper tube annealing process. This system can effectively solve the problems in the background art.

[0005] To achieve the above objectives, the present invention provides a communication control system for copper tube annealing equipment parameters based on the real-time characteristics of insulation materials, comprising:

[0006] The monitoring module includes a temperature sensing array deployed inside and / or on the surface of the insulation material of the annealing furnace, used to collect thermal state characteristic data of the insulation material in real time; and to encapsulate the collected thermal state characteristic data in a communication format to generate a data frame to be transmitted that conforms to a preset communication specification.

[0007] The control module, which is communicatively connected to the monitoring module, is configured as follows:

[0008] Receive the data frame to be transmitted and parse it to obtain the thermal state characteristic data;

[0009] Calculate the real-time thermal performance index of the thermal insulation material based on the aforementioned thermal state characteristic data;

[0010] The real-time thermal performance index is compared with a preset performance threshold, and a control command is generated based on the comparison result. The control command is then encapsulated into a command data frame that conforms to a preset communication specification.

[0011] An execution module, which is communicatively connected to the control module, includes a heater and / or a conveying mechanism for the annealing furnace, and is used to receive the instruction data frame, parse the control instruction, and execute it.

[0012] In one possible design, the temperature sensing array is distributed in a three-dimensional grid pattern inside and / or on the surface of the insulation material;

[0013] The monitoring module also includes a humidity sensor and a strain sensor; the thermal state characteristic data includes temperature gradient data, moisture content data, and strain data.

[0014] In one possible design, the monitoring module further includes an edge computing unit configured to dynamically adjust the sampling frequency based on the temperature change rate of the monitoring point and to filter the sampled data.

[0015] In one possible design, the edge computing unit is further configured as follows:

[0016] The first sampling period is used when the rate of temperature change is greater than the first threshold.

[0017] The second sampling period is used when the rate of temperature change is less than or equal to the second threshold.

[0018] A third sampling period is used when the rate of temperature change is between the first and second thresholds.

[0019] In one possible design, the control module is configured as follows:

[0020] When receiving the data frame to be transmitted, it supports parallel reception and automatic identification of multiple industrial communication protocols;

[0021] Perform integrity checks on the received data frames;

[0022] When the verification passes, the header information and feature layer data of the data frame are parsed.

[0023] In one possible design, the control module is further configured as follows:

[0024] The validity of the numerical range of the thermal state characteristic data obtained by analysis is verified.

[0025] Data credibility is screened based on the confidence parameters carried in the thermal state characteristic data;

[0026] Perform logical consistency checks on associated feature data.

[0027] In one possible design, the control module is configured to, when calculating the real-time thermal performance index:

[0028] Based on the physical laws governing the changes in the thermal properties of thermal insulation materials, the influencing factors used to calculate the real-time thermal performance index are determined. These influencing factors include the temperature gradient factor, moisture content factor, strain factor, and corresponding data confidence levels.

[0029] The temperature gradient factor, moisture content factor, and strain factor are standardized respectively.

[0030] The confidence levels were used to calculate the weighted average of each standardized influence factor.

[0031] The weights of each influencing factor are determined based on the working conditions of copper tube annealing.

[0032] The basic thermal performance index is calculated based on the various influencing factors obtained by confidence weighting and the weights of the influencing factors.

[0033] The baseline thermal performance index is corrected using historical comparisons, and the real-time thermal performance index is determined.

[0034] In one possible design, the formula for calculating the basic thermal performance index, which utilizes historical comparisons to correct for errors, is as follows:

[0035]

[0036] Among them, I his I is a historical comparison correction index. base Based on the thermal performance index, I avg The historical average index is defined for a preset time period, and sign is the sign function.

[0037] In one possible design, the formula for calculating the real-time thermal performance index is:

[0038]

[0039] Among them, I final The final real-time thermal performance index is given by , where max is the maximum value function, min is the minimum value function, and K is the operating condition correction coefficient.

[0040] In one possible design, the operating condition correction factor is dynamically adjusted based on the ratio of the annealing furnace heating power to the rated power.

[0041] The technical solution of this invention can achieve the following technical effects: This invention uses a monitoring module to capture changes in the thermal state of the insulation material in real time, and the control module calculates the thermal performance index in advance and generates compensation instructions based on this. The execution module starts adjustment before the temperature deviation occurs. The logic of this invention, from changes in material properties to predictive compensation and then to temperature field stabilization, transforms control from post-correction to pre-prevention, reducing the amplitude and duration of temperature fluctuations. This is a synergistic effect that cannot be achieved by a single monitoring or control link.

[0042] This invention collects multi-dimensional thermal state data through a monitoring module, calculates a comprehensive thermal performance index through a control module, and then generates targeted collaborative instructions that include multiple parameters such as heating power and transmission speed. The execution module realizes multi-variable linkage adjustment. This overall logic of multi-data input, multi-parameter output, and multi-actuator collaboration can maintain a stable temperature field even when the performance of thermal insulation materials changes non-linearly. This is a dynamic balance effect that cannot be achieved by adjusting a single parameter.

[0043] This invention senses and quantifies the thermal properties of insulation materials in real time, generating a thermal performance index that serves as a control benchmark. Regardless of whether the material is newly introduced or has been used for many years, or whether it is dry or slightly damp, the control module can generate matching instructions based on the current actual performance. The overall logic of this invention, based on the real-time state of the material, enables the system to transcend the differences in the life cycle of insulation materials and continuously output an annealing environment that meets process requirements. This ensures the consistency of product quality across different batches and periods, a full-cycle quality stability effect that traditional logic, which only focuses on adjusting equipment parameters, cannot achieve. Attached Figure Description

[0044] Figure 1 This is a flowchart of the communication control system for copper tube annealing equipment parameters based on the real-time characteristics of thermal insulation materials in this invention. Detailed Implementation

[0045] This application will now be described with reference to the accompanying drawings.

[0046] like Figure 1 As shown, the copper tube annealing equipment parameter communication control system based on the real-time characteristics of thermal insulation materials of the present invention specifically includes the following modules;

[0047] The monitoring module includes a temperature sensing array deployed inside and / or on the surface of the insulation material of the annealing furnace, used to collect thermal state characteristic data of the insulation material in real time; and to encapsulate the collected thermal state characteristic data in a communication format to generate a data frame to be transmitted that conforms to a preset communication specification.

[0048] The control module, which is communicatively connected to the monitoring module, is configured as follows:

[0049] Receive the data frame to be transmitted and parse it to obtain the thermal state characteristic data;

[0050] Calculate the real-time thermal performance index of the thermal insulation material based on the aforementioned thermal state characteristic data;

[0051] The real-time thermal performance index is compared with a preset performance threshold, and a control command is generated based on the comparison result. The control command is then encapsulated into a command data frame that conforms to a preset communication specification. The control command is used to compensate for changes in the thermal performance of the insulation material.

[0052] An execution module, which is communicatively connected to the control module, includes a heater and / or a conveying mechanism for the annealing furnace, and is used to receive the instruction data frame, parse the control instruction, and execute it.

[0053] In this embodiment, the existing system only passively adjusts after the temperature inside the furnace deviates. By this time, the fluctuation of the thermal properties of the insulation material has already caused substantial interference to the temperature field, resulting in a significant lag. In contrast, this system uses a monitoring module to capture changes in the thermal state of the insulation material in real time. Based on this, the control module calculates the thermal performance index in advance and generates compensation commands. The execution module initiates adjustments before the temperature deviation occurs, such as adjusting the heating power or transmission speed in advance. This logic of material property changes, predictive compensation, and temperature field stability transforms control from post-event correction to pre-event prevention, reducing the amplitude and duration of temperature fluctuations. This synergistic effect cannot be achieved by a single monitoring or control link.

[0054] Because the fluctuations in the thermal performance of insulation materials are not a single variable, such as changes in thermal conductivity alone, but rather the result of the coupling of multiple factors such as thermal conductivity, thermal resistance, and ambient humidity; existing systems' linear temperature and power regulation cannot adapt to this complexity, often resulting in overheating or under-regulation; while this system collects multi-dimensional thermal state data through a monitoring module, calculates a comprehensive thermal performance index through a control module, and then generates targeted collaborative instructions that include multiple parameters such as heating power and transmission speed, and the execution module realizes multi-variable linkage regulation; the overall logic of this system, with multiple data inputs, multiple parameter outputs, and multiple actuators working together, can maintain a stable temperature field even when the performance of insulation materials changes non-linearly, such as a sudden drop in thermal resistance due to localized moisture and a gradual increase in thermal conductivity due to overall thermal aging. This is a dynamic balance effect that cannot be achieved by single-parameter regulation;

[0055] The existing system's control logic mainly focuses on the instantaneous temperature stability of a single device, making it difficult to handle performance differences between different batches of insulation materials or different stages of use of the same material. This leads to fluctuations in the quality of copper tubes produced at different times. In contrast, this system senses and quantifies the thermal performance of the insulation material in real time, generating a thermal performance index as the control benchmark. Regardless of whether the material is newly added or has been used for many years, whether it is dry or slightly damp, the control module can generate matching instructions based on the current actual performance. The system's overall logic, based on the real-time state of the material, enables the system to transcend the differences in the life cycle of insulation materials and continuously output an annealing environment that meets process requirements. This fundamentally ensures the consistency of product quality across different batches and at different times, a full-cycle quality stability effect that the existing logic, which only focuses on adjusting equipment parameters, cannot achieve.

[0056] In a specific implementation, as one example, to avoid the problem that temperature sensors using single points or fixed areas can only acquire local data and cannot reflect the spatial distribution differences in the thermal properties of materials, this implementation achieves spatial distribution monitoring of the thermal state of insulation materials through the following method:

[0057] A temperature sensing array, including K-type thermocouples and infrared temperature probes, is arranged inside the insulation material according to a three-dimensional grid and layered point distribution principle. The K-type thermocouples are embedded 5 to 10 mm deep inside the material, while the infrared temperature probes are installed on the material surface. Simultaneously, microwave humidity sensors and fiber optic strain sensors are added at key nodes of the material. The fiber optic strain sensors are used to monitor the deformation of the material caused by thermal expansion and contraction, forming a multi-dimensional monitoring network of temperature, humidity, and stress. The monitoring grid density is designed with a gradient distribution; for example, the spacing between measuring points near the heating zone is ≤5 cm, and the spacing in the non-heating zone is ≤10 cm, ensuring coverage of the areas where the material's thermal properties change most significantly. Specific thermal state characteristic data include:

[0058] Temperature characteristic data includes temperature values ​​at different depths inside the insulation material, the temperature difference between the material surface and the interior, and the rate of change of temperature gradient. Among them, the internal temperature value is directly collected through a three-dimensional grid-based temperature sensor array, reflecting the heat distribution of the material itself; the temperature difference between the surface and the interior is used to calculate the thermal resistance characteristics of the material; and the rate of change of temperature gradient reflects the dynamic change of the material's thermal conductivity.

[0059] Humidity characteristic data: that is, the real-time moisture content inside the insulation material, which is collected by an integrated humidity sensor; the moisture content directly affects the thermal conductivity of the material. An increase in moisture content will lead to a significant decrease in the thermal insulation performance of the material. Therefore, this data is a key auxiliary parameter for judging the deterioration of the thermal performance of the material.

[0060] Strain characteristic data: These are the thermal expansion and contraction deformation values ​​of the insulation material due to temperature changes, collected by fiber optic strain sensors. Long-term thermal fatigue of materials can lead to changes in structural density, which in turn affects thermal resistance. Strain data can indirectly reflect the structural stability of the material and help determine whether fluctuations in thermal performance are caused by changes in physical structure.

[0061] Meanwhile, an edge computing unit is built into the sensor array, employing an adaptive dynamic sampling mechanism to analyze the temperature change rate ΔT / Δt at each measuring point in real time: when ΔT / Δt > 0.5℃ / s, the material's thermal properties change drastically, triggering high-frequency sampling with a sampling period of 100ms; when ΔT / Δt ≤ 0.1℃ / s, the material's thermal properties are stable, automatically switching to low-frequency sampling with a sampling period of 1s; when between these two, a medium-frequency sampling with a sampling period of 500ms is used; the sampling frequency switching is decided locally by the edge computing unit, without relying on external control commands, reducing communication latency; it is also necessary to perform a three-point moving average filter on the raw data to remove outliers caused by material vibration, such as single-point data with temperature mutations exceeding 5℃.

[0062] In this embodiment, the three-dimensional mesh layout combined with multi-parameter sensing can not only capture the temperature distribution of the insulation material, but also correlate key factors affecting thermal performance such as humidity and deformation, thereby obtaining the true thermal state of the insulation material. Based on the adaptive sampling mechanism of thermal performance change rate, the amount of data transmission in the steady state is reduced while ensuring that key data is not lost. At the same time, edge computing localized decision-making avoids communication delays caused by sampling frequency adjustment, taking into account both real-time performance and energy consumption control.

[0063] Furthermore, to ensure efficient interoperability of monitoring data across various industrial systems and compatibility with annealing equipment and upper-level management systems from different manufacturers, the data frame to be transmitted in this embodiment adopts a three-segment structure: header, feature layer, and verification layer. The header includes the device ID, sampling timestamp, and data type identifier. For example, the device ID is 8 bytes, the sampling timestamp is 4 bytes, and the data type identifier is 2 bytes. The feature layer encapsulates thermal state feature data in TLV format, including temperature gradient values, humidity values, etc., with corresponding Type values ​​of 0x01 and 0x02, respectively. Each feature value includes the value itself and a 1-byte confidence parameter. The verification layer uses the CRC32 algorithm to generate a 4-byte checksum.

[0064] It also needs a built-in protocol conversion engine that can automatically convert data frames into industrial standard protocol formats such as PROFINET, Modbus RTU, or OPC UA according to the needs of the control module. The conversion process is implemented by a lookup table method, with preset data mapping tables for each protocol, so there is no need to reconstruct the data structure. At the same time, it can automatically adjust the frame length according to the number of valid features of the sampled data. When a certain feature value does not change for three consecutive times, the feature field is automatically removed from the frame to minimize the amount of data transmission.

[0065] In this embodiment, the hierarchical dynamic data frame structure breaks free from the limitations of a fixed format and supports the addition of new parameter types as needed, such as future expansion of material aging monitoring; the built-in protocol conversion engine enables the system to adapt to annealing furnaces, PLCs and management systems from different manufacturers without hardware modifications, expanding application scenarios, for example, it can adapt to the RS485 interface of old equipment and also connect to the smart factory platform of industrial Ethernet.

[0066] In a specific implementation, as one example, the control module processes the data frames to be transmitted through an adaptive parsing mechanism combined with data validity pre-verification. The specific steps are as follows:

[0067] Step S11: The control module has a built-in protocol receiving unit that supports parallel reception of three mainstream industrial protocols: PROFINET, Modbus RTU, and OPC UA. It automatically matches the corresponding receiving channel using a protocol identification algorithm, eliminating the need for manual switching. The protocol identification algorithm determines the protocol type based on the header identifier bits of the data frame. For example, PROFINET frames contain a specific Ethernet frame identifier, and Modbus RTU frames contain a start address code. The receiving unit has a data buffer with a capacity of at least 1024 bytes to prevent high-frequency sampling data overflow. The buffer uses a first-in, first-out (FIFO) mechanism to ensure that the data reception order matches the acquisition order. After receiving data, the 4-byte CRC32 checksum of the data frame verification layer is extracted first. The control module locally recalculates the CRC32 value of the received header and feature layer data and compares it with the extracted checksum. If they match, the frame structure is considered complete, and the parsing process begins. If they do not match, the frame is marked as invalid and discarded. Simultaneously, a frame error log is generated, recording the timestamp and protocol type of the erroneous frame to prevent invalid data from consuming parsing resources.

[0068] Step S12: Extract the 8-byte device ID, 4-byte sampling timestamp, and 2-byte data type identifier from the header of the complete data frame; match the corresponding monitoring module deployment location through the device ID, such as the heating zone or non-heating zone, to determine the insulation material area corresponding to the data; determine the timeliness of the data based on the sampling timestamp. If the difference between the timestamp and the current system time of the control module exceeds 1 second, this threshold can be configured, and it is determined to be delayed data, which is temporarily stored in the backup cache area. The latest data is parsed first to ensure that the thermal performance index calculation is based on real-time data.

[0069] The system employs a dynamic TLV parsing algorithm, parsing feature layer data field by field in the order of Type, Length, and Value. The first step reads a 2-byte Type value, matching it against a pre-defined feature type mapping table to determine the feature data type. In the mapping table, Type equal to 0x01 corresponds to temperature gradient values, Type equal to 0x02 to humidity values, Type equal to 0x03 to strain values, and Type equal to 0x04 to temperature difference values, etc., and the mapping table is expandable. The second step reads a 2-byte Length value to specify the byte length of the feature data. For example, when the temperature gradient value is a 4-byte floating-point number, Length equals 4; when the humidity value is a 2-byte integer, Length equals 2. The third step extracts the corresponding number of bytes of Value based on the Length value, while simultaneously reading a 1-byte confidence parameter. The result is stored in a feature data array, with array fields including feature type, value, confidence, sampling timestamp, and device ID. If an undefined Type value is encountered during parsing, such as when the monitoring module expands to include new features, the field is automatically marked as a feature to be identified, and the original byte data is temporarily stored without interrupting the overall parsing process. This allows for subsequent software upgrades to supplement the parsing logic.

[0070] Step S13: For different types of thermal state characteristic data, preset reasonable numerical ranges, such as moisture content of insulation material 0 to 10%, temperature gradient 0 to 5℃ per centimeter, and strain value -500με to 500με. Compare the parsed characteristic values ​​with the corresponding ranges. Values ​​exceeding the range are marked as outliers, and the anomaly type is recorded, such as moisture content exceeding the upper limit or abnormal temperature gradient. Extract a 1-byte confidence parameter from the characteristic data. This parameter ranges from 0 to 100, with higher values ​​indicating stronger confidence. Set a confidence threshold, such as 60. Characteristic data below the threshold are marked as low-confidence data and are not included in the real-time thermal performance index calculation. Simultaneously... A re-collection command is triggered and sent to the corresponding monitoring module via the communication link, requiring the re-collection of the feature data to ensure the reliability of the calculation input data. The logical relationships between different feature data from the same monitoring module are correlated. For example, if the humidity value also increases when the temperature gradient value increases, it conforms to the physical law that an increase in moisture content leads to an increase in thermal conductivity and a change in the temperature gradient. If the temperature gradient increases but the humidity value does not change, it is necessary to combine the strain value to determine whether it is caused by material structural deformation. If a logical contradiction occurs, such as a negative temperature difference, which does not conform to the normal operating condition where the internal temperature is higher than the surface, the data is marked as contradictory, and the feature data with higher confidence is prioritized for subsequent processing.

[0071] The above settings support parallel reception of multiple protocols and can directly interface with the dynamic protocol conversion output of the monitoring module without the need for additional protocol conversion hardware. This adapts to annealing equipment monitoring systems from different manufacturers and eras, avoiding system modification costs due to protocol incompatibility. A four-level verification mechanism—frame integrity verification, numerical range verification, confidence level filtering, and data consistency verification—effectively eliminates invalid frames, outliers, and low-reliability data, ensuring that the parsed thermal state characteristic data accurately reflects the thermal performance of the insulation material and reducing interference from erroneous data in control command generation. The dynamic TLV parsing algorithm supports the temporary storage and subsequent upgrade of undefined Type values, matching new feature data extended by the monitoring module, such as material aging, without requiring reconstruction of the control module's parsing logic. Data timeliness judgment and caching mechanisms balance the real-time performance and reception stability of high-frequency sampled data, avoiding untimely compensation due to data delays.

[0072] It should be noted that in this method, multi-protocol reception adaptation is not limited to PROFINET, Modbus RTU, and OPCUA protocols, but can also be extended to other industrial standard protocols such as EtherNet / IP and DeviceNet. This can be achieved simply by updating the preset protocol identification rules and receiving channel parameters through software. Frame integrity verification is not limited to the CRC32 algorithm; other verification algorithms such as CRC16 and MD5 can also be used, as long as they match the verification logic of the monitoring module. The feature data value range and confidence threshold can be customized according to the type of insulation material, such as ceramic fiber and rock wool, and the annealing process requirements, such as low-temperature annealing and high-temperature annealing. The feature type mapping table of the dynamic TLV parsing algorithm can be modified through an external configuration file without modifying the core program of the control module. It can adapt to any new thermal state feature data type added by the monitoring module, such as material thermal conductivity and heat capacity, covering all thermal state monitoring scenarios of insulation materials based on dynamic frame structure and multi-dimensional feature data. It is suitable for the data reception and parsing needs of control modules of various copper tube annealing equipment and other metal heat treatment equipment.

[0073] In practical implementation, as one example, when the moisture content of the insulation material increases or its structure deforms, the real-time thermal performance index calculated solely from temperature data will deviate significantly from reality, rendering subsequent control commands ineffective. Given the aforementioned monitoring module's ability to collect multi-dimensional characteristic data including temperature, humidity, and strain, along with confidence parameters, the control module needs to construct a multi-dimensional data weighted fusion and dynamic correction computational mechanism. This mechanism incorporates multiple factors affecting thermal performance and data reliability into the calculation logic, generating a thermal performance index that accurately reflects the real-time state of the insulation material, thus preventing annealing temperature runaway due to calculation errors of a single parameter. The specific implementation method is as follows:

[0074] Step S21: Based on the physical laws governing the changes in the thermal properties of the insulation material, determine the influencing factors in the thermal state characteristic data that can be used to calculate the real-time thermal performance index of the insulation material, specifically including:

[0075] Temperature gradient factor, collected in the range of 0 to 5℃ per centimeter, reflects the heat transfer rate inside the material. The larger the value, the smaller the thermal resistance and the worse the thermal performance.

[0076] Moisture content factor, collected in the range of 0 to 10%. Increased moisture content will increase thermal conductivity, and the higher the value, the worse the thermal performance.

[0077] The strain factor, collected in the range of -500με to +500με, reflects the structural stability of the material. The larger the absolute value, the more severe the structural deformation and the higher the risk of thermal performance degradation.

[0078] Data confidence, corresponding to temperature gradient factor, moisture content factor and strain factor respectively, characterizes the reliability of the data collected for each parameter. The higher the value, the greater the weight.

[0079] At the same time, it is also necessary to determine the operating condition correction factor. The operating condition correction factor is dynamically adjusted according to the ratio of the heating power of the annealing furnace to the rated power. For example, when the ratio of the heating power to the rated power is greater than or equal to 80%, the operating condition correction factor is 1.1, which is a high-temperature operating condition, and the thermal performance is sensitive to the influence. When the ratio of the heating power to the rated power is less than 80%, the operating condition correction factor is 0.9, which is a low-temperature operating condition, and the thermal performance is mildly affected.

[0080] Step S22: For each factor, design the following single-factor standardization formula to eliminate dimensional differences and convert each factor into a standard score from 0 to 100:

[0081] Formula for calculating the normalized score of the temperature gradient:

[0082]

[0083] Among them, G t.std For the temperature gradient normalization, G t The temperature gradient factor is used to map the temperature gradient factor from 0 to 5°C per centimeter to a fractional range from 0 to 100 through a linear transformation. The smaller the temperature gradient factor, the better the thermal performance and the higher the corresponding standardized score.

[0084] Formula for calculating standardized moisture content:

[0085]

[0086] Among them, W stdW is the moisture content standardized score, and W is the moisture content factor. The moisture content factor of 0 to 10% is linearly converted into a score of 0 to 100. The lower the moisture content, the better the thermal performance and the higher the standardized score.

[0087] Strain normalization calculation formula:

[0088]

[0089] Among them, S std S is the strain normalization score, S is the strain factor, and |S| is the absolute value of the strain factor. With 0 με as the baseline, the baseline corresponds to 100 points. The strain factor from -500 με to 500 με is converted into a score from 0 to 100. As the absolute value of strain increases, the score decreases linearly, reflecting the negative impact of structural deformation on thermal performance.

[0090] Step S23: Calculate the weighted average of each factor using its confidence level, assigning higher weights to data with higher confidence levels; the specific calculation is as follows:

[0091] Formula for calculating the weighted score of temperature gradient:

[0092]

[0093] Among them, G t.w For the temperature gradient weighted score, C t The data confidence level corresponding to the temperature gradient factor is calculated by multiplying the standardized score of the temperature gradient by the percentage of the data confidence level, so that high-confidence data has a higher weight in the calculation.

[0094] Formula for calculating weighted score of moisture content:

[0095]

[0096] Among them, W w For moisture content weighted score, C w The confidence level of the data corresponding to the moisture content factor is calculated; the impact of high-confidence data is enhanced by multiplying the standardized score of moisture content by the percentage of the data confidence level.

[0097] Strain weighted calculation formula:

[0098]

[0099] Among them, S w For strain weighting, C s The data confidence level corresponding to the strain factor is calculated; the strain standardization score is multiplied by the percentage of the data confidence level to reduce the interference of low-confidence data on the results.

[0100] To ensure data availability, in the above confidence-weighted calculation, if the confidence level of a certain parameter is 60%, the weighted score of that parameter is only 60% of the standard score, reducing the interference of low-confidence data on the results; if the confidence level of a certain parameter is lower than 60, the average of the weighted scores of other parameters is used instead. For example, when the confidence level of the corresponding temperature gradient factor is lower than 60, the temperature gradient weighted score is equal to the average of the moisture content weighted score and the strain weighted score.

[0101] Step S24: Adjust the weight ratio of each factor according to the copper tube annealing operation conditions; the weight calculation formula for each factor is as follows:

[0102] Formula for calculating temperature gradient weights:

[0103] α = 0.5 + (K-1) × 0.1

[0104] Where α is the temperature gradient weight and K is the operating condition correction coefficient; the weight ratio of the temperature gradient factor is dynamically adjusted according to the operating condition correction coefficient, with the weight increasing under high temperature operating conditions and decreasing under low temperature operating conditions.

[0105] Formula for calculating moisture content weight:

[0106] β = 0.3 - (K-1) × 0.05

[0107] Wherein, β is the moisture content weight; it complements the temperature gradient weight, with the weight decreasing under high temperature conditions and increasing under low temperature conditions, ensuring that the weight allocation is adapted to the degree of influence of different conditions on thermal performance.

[0108] Strain weight calculation formula:

[0109] γ=1-α-β

[0110] Wherein, γ is the strain weight; the remaining weight share is automatically completed to ensure that the sum of the temperature gradient weight, moisture content weight, and strain weight is 1, thereby achieving dynamic balance of the weights.

[0111] Step S25: By integrating multiple factors, operating condition corrections, and corresponding weights, the real-time thermal performance index is calculated. Specifically, first, the basic thermal performance index calculation formula is calculated:

[0112]

[0113] Among them, I base The basic thermal performance index is a weighted sum of the weighted scores of each factor according to dynamic weights, which comprehensively reflects the overall impact of temperature, humidity and structure on thermal performance.

[0114] Then, the basic thermal performance index is corrected using historical comparisons, and the calculation formula is as follows:

[0115]

[0116] Among them, I his I is a historical comparison correction index. base Based on the thermal performance index, I avg The index is the historical average index over the past hour, and sign is the sign function. By comparing it with the historical average index, the basic thermal performance index is corrected to avoid misjudgment caused by instantaneous data fluctuations.

[0117] Finally, the formula for calculating the real-time thermal performance index is:

[0118]

[0119] Among them, I final The final real-time thermal performance index is defined by max, which is the maximum value function, min, which is the minimum value function, and K, which is the operating condition correction coefficient. The influence of operating conditions on thermal performance is first enhanced by the operating condition correction coefficient, and then the result is limited to a reasonable range of 0 to 100 to ensure the effectiveness of the index.

[0120] For example, the historical average index for the past hour is the average historical thermal performance index of the insulation material over the past hour, and the historical thermal performance index is stored every 5 minutes; the sign function is used to determine the thermal performance index. When the historical average index for the past hour is greater than the basic thermal performance index, the sign function value is 1, indicating that the current thermal performance has deteriorated, and the index is improved by 5% after correction; when the historical average index for the past hour is less than the basic thermal performance index, the sign function value is -1, indicating that the current thermal performance has improved, and the index is reduced by 5% after correction; the maximum value function and the minimum value function are used to limit the index to the range of 0 to 100 to avoid outliers, and then multiply it by the operating condition correction coefficient to enhance the influence of the operating condition on the thermal performance.

[0121] In this embodiment, the deviation between the thermal performance index and the actual thermal resistance is reduced by using a multi-factor coupling formula and dynamic weights. Low-confidence data can be directly filtered out by setting a confidence weighting formula, and instantaneous fluctuations are eliminated by a historical comparison correction formula. Even if a sensor fails, for example, if the confidence level of the data corresponding to the temperature gradient factor is 50, which is below the low-confidence threshold of 60, the index can still be kept reasonable by the replacement algorithm to avoid control failure. Through the operating condition correction coefficient and dynamic weight formula, the index can automatically adapt to different annealing conditions such as high temperature and low temperature without the need for manual adjustment of the calculation logic.

[0122] In a specific implementation, as one example, the control module constructs a mechanism for multi-level threshold dynamic matching and adaptive instruction encapsulation. This achieves precise compensation through subdivided thresholds and adapts to multiple types of execution modules through standardized encapsulation, ensuring that control instructions can accurately respond to changes in the thermal performance of the insulation material while reducing system hardware modification costs. The specific implementation is as follows:

[0123] Step S31: Based on the real-time thermal performance index (TPI) classification from 0 to 100, establish a multi-level threshold system consisting of a base threshold and a floating threshold, for example:

[0124] Excellent level threshold: The basic threshold is 90 points, and the floating threshold is ±2 points. That is, when the index is greater than or equal to 88 points, it is judged as excellent, so as to avoid frequent switching of instructions due to small fluctuations.

[0125] Good grade threshold: base threshold 70 points, floating threshold ±3 points, that is, when the index is greater than or equal to 67 and less than 88 points, it is judged as good;

[0126] Medium-level threshold: The basic threshold is 50 points, and the floating threshold is ±3 points. That is, when the index is greater than or equal to 47 and less than 67 points, it is judged as medium.

[0127] Poor grade threshold: The basic threshold is 30 points, and the floating threshold is ±2 points. That is, when the index is greater than or equal to 28 and less than 47 points, it is judged as poor.

[0128] Range threshold: An index score less than 28 is considered a range.

[0129] The floating threshold can be adjusted through the control module parameter interface according to the annealing process requirements, adapting to the annealing temperature sensitivity of different materials.

[0130] Step S32: Each time the control module calculates the real-time thermal performance index, it first determines the current index level, and then optimizes the comparison result by combining the historical level change trend.

[0131] If the current index belongs to the same level as the previous index, and the index fluctuation is less than or equal to 50% of the floating threshold, such as a fluctuation of less than or equal to 1.5 points within the good level, then the original control instruction will be maintained to avoid frequent instruction switching that could cause the execution module to start and stop.

[0132] If the current index changes across levels, such as from the medium level to the poor level, or the fluctuation range within the same level is greater than 50% of the floating threshold, a new control instruction will be generated immediately.

[0133] If the current index is determined to be at an extremely poor level, in addition to triggering the corresponding control command, an alarm signal will be generated simultaneously to prompt manual intervention.

[0134] Step S33: For different thermal performance levels, preset basic control commands for adjusting heating power and conveying speed, and link the parameters of the basic control commands with the real-time thermal performance index, for example:

[0135] Excellent rating: Heating power remains at the current value, with the command parameter set to power adjustment of 0%; conveying speed remains at the current value, with the command parameter set to speed adjustment of 0%.

[0136] Good grade: Fine-tune heating power, adjustment amount = (88 - current index) × 0.1%. For example, when the index is 80, the power is increased by 0.8%; the conveying speed remains at the current value, adjustment amount 0%.

[0137] Medium level: Heating power is adjusted normally, adjustment amount = (67 - current index) × 0.2%. For example, when the index is 55, the power is increased by 2.4%; Conveyor speed is adjusted, adjustment amount = -(67 - current index) × 0.1%. For example, when the index is 55, the speed is reduced by 1.2%.

[0138] Poor rating: Heating power is increased by 0.3% (47 - current index). For example, if the index is 40, the power is increased by 2.1%. Conveyor speed is decreased by 0.2% (47 - current index). For example, if the index is 40, the speed is decreased by 1.4%.

[0139] Extreme level: Heating power stops adjusting, the command parameter is power adjustment amount 0%, the conveyor speed is adjusted to 0, i.e., a stop command is given, and an alarm command is generated simultaneously;

[0140] The instruction library supports importing new instruction parameters via external storage media to adapt to new models of heaters or conveying mechanisms.

[0141] Step S34: After generating the basic control commands, perform secondary corrections based on the current operating conditions of the annealing furnace.

[0142] If the current heating power has reached 95% of the rated power, the upper limit of the heating power adjustment will be limited to 2% to avoid power overload.

[0143] If the difference between the actual temperature inside the furnace and the temperature required by the process is greater than 5℃, an additional 1% heating power adjustment will be added on top of the basic adjustment to accelerate temperature compensation.

[0144] The revised command parameters must meet the following requirements: the heating power adjustment should be between -5% and +10%, with the negative adjustment used to reduce power during thermal performance recovery; the conveyor speed adjustment should be between -20% and 0%, reducing only the speed to ensure sufficient annealing.

[0145] Step S35: Encapsulate the control commands after secondary correction into a communication format. The command data frame adopts a three-segment structure of header, command layer, and check layer, adapting to the preset communication specification.

[0146] The header contains the target execution module ID, instruction generation timestamp, instruction type identifier, and data length. The target execution module ID is 6 bytes, such as heater ID is 0x0001 and conveyor ID is 0x0002. The instruction generation timestamp is 4 bytes. The instruction type identifier is 2 bytes, such as power adjustment is 0x01, speed adjustment is 0x02, and alarm is 0x03. The data length is 2 bytes, indicating the number of bytes in the instruction layer.

[0147] The instruction layer uses TLV format to encapsulate control instruction parameters. The Type field is 2 bytes and corresponds to the instruction type. For example, 0x0101 is the heating power adjustment amount and 0x0102 is the transmission speed adjustment amount. The Length field is 2 bytes and indicates the length of the Value field. For example, if the power adjustment amount is a 4-byte floating-point number, the Length is 4. The Value field is the instruction parameter value. For example, the power adjustment amount plus 2.4% corresponds to hexadecimal 0x40133333.

[0148] The verification layer uses the CRC32 algorithm to calculate the checksum for the header and instruction layer data to ensure the integrity of data transmission.

[0149] The control module has a built-in protocol adaptation unit that automatically converts instruction data frames into the corresponding protocol format according to the communication requirements of the target execution module. If the execution module supports the PROFINET protocol, a PROFINET-specific identification field, such as a 2-byte frame type identifier 0x02, is added to the header and the data frames are reassembled according to the PROFINET data frame structure. If the execution module supports the Modbus RTU protocol, the instruction data frames are encapsulated in the Modbus RTU format, such as a 1-byte start address code and a 1-byte function code, and the checksum is adjusted to the CRC16 algorithm. Protocol conversion is achieved through a preset protocol mapping table, which stores the correspondence between each protocol field and the instruction data frame field, without requiring modification of the core logic of the frame structure.

[0150] It should be noted that, in this embodiment, the number of preset performance threshold levels is not limited to 5 levels. Levels can be added or reduced according to actual needs. For example, adding a suboptimal level for refined compensation only requires adjusting the threshold range and corresponding instruction parameters. The adjustment method for floating thresholds is not limited to the parameter interface; it can also receive remote configuration instructions from the host computer via industrial Ethernet. The control instruction types are not limited to power, speed, and alarm; they can also be extended to annealing furnace damper opening adjustment, inert gas flow control, etc., simply by adding the corresponding TLV field to the instruction layer. The protocol adaptation unit supports protocol types that are not limited to PROFINET and Modbus RTU; they can also be extended to DeviceNet, OPC UA, etc., by updating the protocol mapping table, adapting to more industrial scenarios. Ultimately, it can cover control modules for metal heat treatment equipment such as copper tubes and copper materials, and does not depend on specific control module hardware models, such as PLCs or industrial PCs; only the comparison logic, instruction generation, and encapsulation functions need to be implemented at the software level.

[0151] Through the above settings, the multi-level threshold system avoids the coarse judgment of a single threshold. By combining the comparison logic of the index fluctuation amplitude and historical trends, the generated instructions are more in line with the thermal performance change law, reducing the five-poison compensation deviation and improving the uniformity of copper tube grain size. The hierarchical instruction data frame structure and adaptive protocol adaptation can connect to heaters and transmission mechanisms from different manufacturers without additional hardware. It is compatible with mainstream industrial protocols such as PROFINET and Modbus RTU, reducing system transformation costs. The instruction maintenance logic within the same level avoids frequent start and stop of the execution module, reducing equipment mechanical wear. The secondary correction of operating conditions prevents production accidents caused by heating power overload or low speed, reducing equipment failure rate. The instruction library supports the import of new parameters from outside, and the protocol mapping table can be expanded to include new protocols through software updates. For example, if an EtherNet / IP protocol execution module is added in the future, there is no need to reconstruct the core program of the control module, which can adapt to future equipment upgrade needs.

[0152] In a specific implementation, as one example, the execution module has a built-in protocol receiving unit that integrates receiving circuits for three mainstream industrial protocols: PROFINET, Modbus RTU, and EtherNet / IP. It automatically matches the command protocol type output by the control module using a protocol pre-identification algorithm. If the received data frame header contains a 2-byte PROFINET-specific identifier, it is determined to be a PROFINET protocol, and the PROFINET protocol parsing subroutine is called. If the data frame header contains a 1-byte start address code and a 1-byte function code, it is determined to be a Modbus RTU protocol, and the Modbus RTU parsing logic is enabled. If the data frame contains an EtherNet / IP protocol header identifier, the EtherNet / IP parsing process is triggered. The protocol receiving unit has a 128-byte buffer. The buffer uses a check-before-storage mechanism, receiving subsequent data only when the first 8 bytes of the data frame match the target protocol characteristics, thus avoiding invalid data occupying receiving resources.

[0153] For the three-segment data frames output by the control module—header, instruction layer, and verification layer—the following steps are performed for parsing: Header parsing: Extract the 6-byte target execution module ID from the header and compare it with the execution module's own preset ID. Parsing continues only if the IDs match; otherwise, the frame is discarded. Simultaneously, extract the 4-byte instruction generation timestamp. If the difference between the timestamp and the execution module's current system time exceeds 2 seconds (this duration is configurable), it is determined to be a delayed instruction and temporarily stored in the backup buffer. The latest instruction is parsed first. Instruction layer parsing: Using TLV format parsing logic, first read the 2-byte Type field and match it with the execution module's built-in instruction type mapping table; then read the 2-byte Length field to determine the byte length of the Value field; finally, extract the Value field data according to the Length value and store it in the instruction parameter buffer. Verification layer: Extract the 4-byte CRC32 checksum from the end of the data frame. The execution module locally recalculates the CRC32 value for the parsed header and instruction layer data. If it matches the extracted checksum, the instruction data is considered complete; if they do not match, a verification failure log is generated and the control module is requested to resend the instruction. The log records the timestamp, protocol type, and error code of the failed frame.

[0154] More specifically, after the execution module parses the instruction parameters, it performs a rationality check based on its own hardware parameters. For heater verification: if the instruction is power adjustment, the power adjustment amount parameter is extracted, and it is determined whether the adjusted power is within 50% to 100% of the rated power. If it exceeds this range, the parameter is corrected to the critical value, and the parameter is marked as exceeding the limit. For conveyor mechanism verification: if the instruction is speed adjustment, it is checked whether the speed adjustment amount is within -20% to 0%, and whether the adjusted speed is not lower than the equipment's minimum operating speed. If it is lower than the minimum speed, speed protection is triggered, maintaining the current minimum speed operation, and simultaneously feeding back abnormal information to the control module. At the same time, the safety of instruction execution is further verified by combining the real-time operating data of the execution module. During heater verification, its own temperature sensor data is read synchronously. If the current temperature has reached the rated upper limit, even if the power adjustment instruction is within a reasonable range, the power increase instruction is not executed temporarily until the temperature drops to a safe threshold. During conveyor mechanism verification, the motor current value is read. If the current exceeds 110% of the rated current, the speed adjustment range is reduced to avoid motor overload tripping.

[0155] Furthermore, differentiated execution strategies are implemented for different thermal performance levels. Based on the thermal performance level corresponding to the control command, a differentiated execution strategy is adopted to ensure a balance between execution accuracy and response speed. For excellent level commands: the execution module checks the current parameters every 10 seconds. If the parameter deviates from the command value by ±2%, it is fine-tuned to the target value to avoid frequent adjustments. For good and medium level commands: the heater uses a stepped power adjustment. If a 0.8% power increase is required, it is executed in two steps, increasing by 0.4% each time with a 5-second interval, to prevent sudden power changes that could cause temperature fluctuations. The conveyor mechanism uses a uniform deceleration. If a 1.2% deceleration is required, it is adjusted at a rate of 0.3% per second to avoid mechanical shock. For poor and extremely poor level commands: the heater executes immediately. Power adjustment commands are executed without step delay, and the conveyor mechanism adjusts at the maximum safe deceleration rate. If a stop command is issued at an extreme level, the conveyor mechanism first reduces to the minimum speed, maintains it for 3 seconds to confirm there is no copper tube obstruction, and then stops. Simultaneously, the heater cuts off 50% of its power to prevent the heating tube from burning dry, and then gradually reduces to the heat preservation power. During command execution, the execution module feeds back the status to the control module in three stages: before execution, during execution, and after execution. Before execution: The feedback command is received and verified, and the execution signal is prepared, including the verification result. During execution: Real-time execution parameters are fed back every 2 seconds. If the execution deviation exceeds ±1%, an execution deviation warning is fed back simultaneously. After execution: The command execution completion signal is fed back, along with a comparison of parameters before and after execution and the execution time, forming a control closed loop.

[0156] In this embodiment, the multi-protocol adaptive receiving unit can interface with various protocol commands output by the control module without manual configuration. When adding an execution module, only the protocol parsing subroutine needs to be updated through software, without replacing the hardware. This adapts to annealing furnace equipment from different manufacturers and eras, reducing system expansion costs. Dual verification effectively filters abnormal commands such as transmission errors, parameter over-range, and operating condition mismatch, reducing the command mis-execution rate. The graded execution strategy avoids equipment shocks caused by parameter mutations, improving the temperature uniformity of the annealed copper tubes. Operating condition linkage verification and graded execution feedback form a dual safety guarantee. For example, when the heater temperature exceeds the limit, the power increase is automatically suspended, and the adjustment range is reduced when the conveyor mechanism is overloaded, reducing the equipment failure rate. Differentiated execution strategies for different thermal performance levels ensure both operational stability at excellent and good levels and rapid response at poor and extremely poor levels, effectively avoiding annealing quality problems caused by the continuous deterioration of the thermal performance of the insulation material.

[0157] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A communication control system for parameters of copper tube annealing equipment based on the real-time characteristics of thermal insulation materials, characterized in that, include: The monitoring module includes a temperature sensing array deployed inside and / or on the surface of the insulation material in the annealing furnace, for real-time acquisition of thermal state characteristic data of the insulation material; The collected thermal state characteristic data is encapsulated in a communication format to generate a data frame to be transmitted that conforms to a preset communication specification. The control module, which is communicatively connected to the monitoring module, is configured as follows: Receive the data frame to be transmitted and parse it to obtain the thermal state characteristic data; Calculate the real-time thermal performance index of the thermal insulation material based on the aforementioned thermal state characteristic data; The real-time thermal performance index is compared with a preset performance threshold, and a control command is generated based on the comparison result. The control command is then encapsulated into a command data frame that conforms to a preset communication specification. An execution module, which is communicatively connected to the control module, includes a heater and / or a conveying mechanism for the annealing furnace, and is used to receive the instruction data frame, parse the control instruction, and execute it.

2. The communication control system for copper tube annealing equipment parameters based on the real-time characteristics of thermal insulation materials according to claim 1, characterized in that, The temperature sensing array is distributed in a three-dimensional grid pattern inside and / or on the surface of the thermal insulation material; The monitoring module also includes a humidity sensor and a strain sensor; the thermal state characteristic data includes temperature gradient data, moisture content data, and strain data.

3. The communication control system for copper tube annealing equipment parameters based on the real-time characteristics of insulation materials according to claim 2, characterized in that, The monitoring module also includes an edge computing unit, which is configured to dynamically adjust the sampling frequency according to the temperature change rate of the monitoring point and to filter the sampled data.

4. The communication control system for copper tube annealing equipment parameters based on the real-time characteristics of thermal insulation materials according to claim 3, characterized in that, The edge computing unit is further configured as follows: The first sampling period is used when the rate of temperature change is greater than the first threshold. The second sampling period is used when the rate of temperature change is less than or equal to the second threshold. A third sampling period is used when the rate of temperature change is between the first and second thresholds.

5. The communication control system for copper tube annealing equipment parameters based on the real-time characteristics of thermal insulation materials according to any one of claims 1-4, characterized in that, The control module is configured as follows: When receiving the data frame to be transmitted, it supports parallel reception and automatic identification of multiple industrial communication protocols; Perform integrity checks on the received data frames; When the verification passes, the header information and feature layer data of the data frame are parsed.

6. The communication control system for copper tube annealing equipment parameters based on the real-time characteristics of thermal insulation materials according to claim 5, characterized in that, The control module is also configured to: The validity of the numerical range of the thermal state characteristic data obtained by analysis is verified. Data credibility is screened based on the confidence parameters carried in the thermal state characteristic data; Perform logical consistency checks on associated feature data.

7. The communication control system for copper tube annealing equipment parameters based on the real-time characteristics of thermal insulation materials according to any one of claims 2-4, characterized in that, The control module is configured to, when calculating the real-time thermal performance index: Based on the physical laws governing the changes in the thermal properties of thermal insulation materials, the influencing factors used to calculate the real-time thermal performance index are determined. These influencing factors include the temperature gradient factor, moisture content factor, strain factor, and corresponding data confidence levels. The temperature gradient factor, moisture content factor, and strain factor are standardized respectively. The confidence levels were used to calculate the weighted average of each standardized influence factor. The weights of each influencing factor are determined based on the working conditions of copper tube annealing. The basic thermal performance index is calculated based on the various influencing factors obtained by confidence weighting and the weights of the influencing factors. The baseline thermal performance index is corrected using historical comparisons, and the real-time thermal performance index is determined.

8. The communication control system for copper tube annealing equipment parameters based on the real-time characteristics of thermal insulation materials according to claim 7, characterized in that, The formula for calculating the basic thermal performance index using historical comparison correction is as follows: ； Among them, I his I is a historical comparison correction index. base Based on the thermal performance index, I avg The historical average index is defined for a preset time period, and sign is the sign function.

9. The communication control system for copper tube annealing equipment parameters based on the real-time characteristics of thermal insulation materials according to claim 8, characterized in that, The formula for calculating the real-time thermal performance index is as follows: ； Among them, I final The final real-time thermal performance index is given by , where max is the maximum value function, min is the minimum value function, and K is the operating condition correction coefficient.

10. The communication control system for copper tube annealing equipment parameters based on the real-time characteristics of thermal insulation materials according to claim 9, characterized in that, The operating condition correction factor is dynamically adjusted based on the ratio of the annealing furnace heating power to the rated power.

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