A method and system for optimizing collection and analysis of operation energy consumption data of a kiln production line

Abstract

The application discloses a kiln production line operation energy consumption data optimization collection and analysis method and system, relates to the technical field of data processing, and comprises the following steps: a kiln car displacement encoder and a pre-buried RFID tag generate a space-time tag; based on the kiln working condition, trigger data collection and obtain pressure fluctuation data; from the pressure fluctuation data, extract the residual oxygen associated component related to the combustion efficiency; when the kiln car enters the rapid cooling area, calculate the deviation of the current rapid cooling area temperature distribution uniformity and the historical reference value, if the deviation exceeds the deviation threshold, then apply the pseudo-random air volume disturbance with the same frequency as the rapid cooling fan; analyze the slope change of the exhaust smoke negative pressure recovery curve and the phase lag amount of the firing zone temperature fluctuation during the disturbance, and generate the air volume redistribution instruction when the slope is greater than the recovery threshold and the phase lag amount exceeds the phase threshold; when the correlation coefficient of the time domain characteristics of the residual oxygen associated component and the residual oxygen concentration change rate exceeds the correlation threshold, output the heat value compensation parameter to the combustion controller.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data processing, more particularly to the technical field of industrial information and data processing, and specifically to a kiln production line operation energy consumption data optimized collection and analysis method and system. BACKGROUND

[0002] As the core equipment in the ceramic and building material industries, the operation energy consumption of a kiln production line accounts for a significant proportion of the total energy consumption of an enterprise. Optimized collection and analysis of kiln operation energy consumption data not only provides data support for energy efficient use and helps reduce overall production costs, but also ensures product quality stability through precise control, meeting the urgent needs of green and intelligent development in the current industrial field.

[0003] In the prior art, there are certain limitations in the collection and analysis of kiln energy consumption data. On the one hand, the correlation matching of the real-time position of the kiln car in the kiln and the energy consumption related data is not close enough, making it difficult to accurately correspond the operation state of the specific area of the kiln with the collected data, resulting in a lack of clear spatial direction in energy consumption analysis. On the other hand, when there is a fluctuation deviation in the temperature of the rapid cooling zone, there is often a lack of targeted dynamic adjustment means for air volume, making it difficult to correct the deviation in a timely manner through reasonable air volume regulation, which can easily cause energy waste or affect the firing effect.

[0004] To address the above problems, no effective solutions have been proposed so far. SUMMARY

[0005] The present application provides a kiln production line operation energy consumption data optimized collection and analysis method and system to solve the above technical problems.

[0006] The present application provides a kiln production line operation energy consumption data optimized collection and analysis method, comprising:

[0007] Generate a space-time tag through a kiln car displacement encoder and a pre-embedded RFID tag, each space-time tag corresponding to the real-time position area of the kiln car in the kiln;

[0008] Trigger data collection based on kiln operating conditions and obtain pressure fluctuation data;

[0009] Extract the residual oxygen associated component related to combustion efficiency from the pressure fluctuation data;

[0010] When the kiln car enters the rapid cooling zone, calculate the deviation of the current rapid cooling zone temperature distribution uniformity from the historical reference value, and if the deviation exceeds the deviation threshold, apply a pseudo-random air volume disturbance with the same frequency as the rapid cooling fan;

[0011] The slope change of the flue gas negative pressure recovery curve during the disturbance and the phase lag amount of the firing zone temperature fluctuation are analyzed, and a wind volume redistribution instruction is generated when the slope is greater than the recovery threshold and the phase lag amount exceeds the phase threshold;

[0012] When the correlation coefficient of the time domain feature of the residual oxygen correlation component and the residual oxygen concentration change rate exceeds the correlation threshold, a heat value compensation parameter is output to a combustion controller.

[0013] The present application provides a kind of kiln production line operation energy consumption data optimization acquisition analysis system, comprising:

[0014] Space-time correlation module, for generating space-time label by kiln car displacement encoder and embedded RFID tag, each space-time label corresponds the real-time position area of kiln car in kiln;

[0015] Pressure fluctuation data acquisition module, for triggering data acquisition and acquiring pressure fluctuation data based on kiln working condition;

[0016] Residual oxygen correlation component extraction module, for extracting residual oxygen correlation component related to combustion efficiency from the pressure fluctuation data;

[0017] Wind volume disturbance applying module, for calculating the deviation of current quenching zone temperature distribution uniformity and historical reference value when kiln car enters quenching zone, and applying pseudo-random wind volume disturbance with the same frequency as quenching fan if the deviation exceeds the deviation threshold;

[0018] Wind volume redistribution module, for analyzing the slope change of the flue gas negative pressure recovery curve during the disturbance and the phase lag amount of the firing zone temperature fluctuation, and generating wind volume redistribution instruction when the slope is greater than the recovery threshold and the phase lag amount exceeds the phase threshold;

[0019] Heat value compensation module, for outputting heat value compensation parameter to combustion controller when the correlation coefficient of the time domain feature of the residual oxygen correlation component and the residual oxygen concentration change rate exceeds the correlation threshold.

[0020] Based on the embodiments provided in the present application, the space-time tag of the corresponding kiln car real-time position area is generated by the kiln car displacement encoder and the embedded RFID tag, the precise association of the energy consumption related data and the specific position of the kiln car in the kiln is realized, the energy consumption data collected and analyzed can be clearly corresponding to the running state of the specific area of the kiln, and the spatial direction and pertinence of energy consumption analysis are effectively improved; when the kiln car enters the rapid cooling area, the deviation of the temperature distribution uniformity and the historical reference value is calculated, the pseudo-random air volume disturbance with the same frequency as the rapid cooling fan is applied when the deviation is out of limit, and the air volume redistribution instruction is generated by combining the slope of the exhaust negative pressure recovery curve during the disturbance and the temperature fluctuation phase lag of the firing zone, forming a dynamic air volume regulation mechanism for the temperature deviation of the rapid cooling area, which can timely and accurately correct the temperature deviation, reduce the energy waste caused by improper air volume regulation, and at the same time ensure the firing effect of the product; based on the kiln working condition, the pressure fluctuation data is collected and the residual oxygen correlation component is extracted, the correlation coefficient between the residual oxygen concentration change rate and the heat value compensation parameter is analyzed to output, so that the combustion control can better adapt to the actual combustion state, which is beneficial to improve the combustion efficiency and further optimize the kiln running energy consumption. BRIEF DESCRIPTION OF DRAWINGS

[0021] The drawings described herein are used to provide further understanding of the embodiments of the present application, and constitute a part of the present application. The schematic embodiments of the present application and their descriptions are used to explain the present application, and do not constitute improper limitation on the present application. In the drawings:

[0022] Figure 1 The flow chart of an optional kiln production line running energy consumption data optimization collection and analysis method according to the embodiments of the present application;

[0023] Figure 2 The flow chart of another optional kiln production line running energy consumption data optimization collection and analysis method according to the embodiments of the present application;

[0024] Figure 3 The structure diagram of an optional kiln production line running energy consumption data optimization collection and analysis system according to the embodiments of the present application;

[0025] Figure 4 The structure diagram of an optional electronic device according to the embodiments of the present application.

[0026] The implementation, functional features and advantages of the present application will be further described with reference to the embodiments and the drawings. DETAILED DESCRIPTION

[0027] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative effort belong to the scope of protection of the present application.

[0028] According to an aspect of the embodiments of the present application, as shown in Figure 1 The present application provides a method for optimizing collection and analysis of operation energy consumption data of a kiln production line, comprising:

[0029] S101, generating a space-time tag through a kiln car displacement encoder and a pre-embedded RFID tag, each space-time tag corresponding to a real-time position area of the kiln car in the kiln;

[0030] In S101, the real-time position of the kiln car is converted into a tag of time plus area through the combination of the kiln car displacement encoder and the pre-embedded RFID tag, solving the problem of disconnection between traditional kiln data collection and the position of the kiln car. The continuity of displacement data and the area identification of the RFID tag are used to realize dynamic binding of data and physical position, providing a spatial reference for subsequent partition energy consumption analysis, so that each group of data can be traced back to the specific process stage (such as the firing zone and the quenching zone) of the specific kiln car, significantly improving the interpretability and pertinence of the data.

[0031] In S101, the real-time position of the kiln car is converted into a tag of time plus area through the combination of the kiln car displacement encoder and the pre-embedded RFID tag, solving the problem of disconnection between traditional kiln data collection and the position of the kiln car. The continuity of displacement data and the area identification of the RFID tag are used to realize dynamic binding of data and physical position, providing a spatial reference for subsequent partition energy consumption analysis, so that each group of data can be traced back to the specific process stage (such as the firing zone and the quenching zone) of the specific kiln car, significantly improving the interpretability and pertinence of the data.

[0032] S102, triggering data collection based on the kiln working condition and obtaining pressure fluctuation data;

[0033] In S102, the actual working condition of the kiln (such as pressure fluctuation and residual oxygen change) is used to dynamically activate collection, and high-precision monitoring is started only when the combustion state may be abnormal, which not only reduces the amount of invalid data storage, but also ensures the data integrity under the key working condition. For example, collection is intensified only when the residual oxygen concentration mutates, avoiding resource waste caused by continuous high-frequency collection, and at the same time, key feature data is reserved for subsequent combustion efficiency analysis.

[0034] S103, extracting a residual oxygen-related component related to the combustion efficiency from the pressure fluctuation data;

[0035] In S103, by separating the interference (such as sealing leakage) irrelevant to combustion efficiency in pressure fluctuation, the core signal related to residual oxygen change is focused. This step eliminates the influence of non-combustion factors such as poor sealing on pressure data, so that the extracted component can directly reflect the sufficiency of fuel combustion, providing a pure analysis basis for subsequent heat value compensation, and improving the accuracy of combustion efficiency evaluation.

[0036] In S104, when the kiln car enters the rapid cooling zone, the deviation of the current rapid cooling zone temperature distribution uniformity from the historical reference value is calculated, and if the deviation exceeds the deviation threshold, a pseudo-random air volume disturbance at the same frequency as the rapid cooling fan is applied.

[0037] The deviation threshold is a critical value for judging whether the temperature of the rapid cooling zone is abnormal, and needs to be set according to the kiln product type (such as ceramics, refractory materials) and the function of the rapid cooling zone. If the value is exceeded, air volume disturbance needs to be applied.

[0038] For example, a kiln for producing precision ceramics (with high requirements for temperature uniformity), the historical reference value of the rapid cooling zone temperature distribution uniformity is ±2℃, and the deviation threshold can be set to 3℃ (i.e. the actual uniformity exceeds ±5℃ to trigger disturbance); a kiln for producing building ceramic tiles (with slightly lower requirements for temperature uniformity), the historical reference value is ±5℃, and the deviation threshold can be set to 5℃ (i.e. the actual uniformity exceeds ±10℃ to trigger disturbance).

[0039] In S104, when the temperature distribution in the rapid cooling zone is uneven, a pseudo-random air volume disturbance at the same frequency as the fan is applied to actively test the system's response to temperature, and the feedback data after disturbance is used to identify the root cause of temperature unevenness. Compared with passive waiting for temperature to adjust naturally, this active intervention can quickly locate the air volume distribution problem of the fan, such as discovering temperature deviation caused by insufficient air volume in a certain area through disturbance, providing dynamic test basis for subsequent air volume adjustment, and shortening the adjustment time of temperature balance.

[0040] In S105, the slope change of the exhaust gas negative pressure recovery curve and the phase lag of the firing zone temperature fluctuation during the disturbance are analyzed, and when the slope is greater than the recovery threshold and the phase lag exceeds the phase threshold, an air volume redistribution instruction is generated.

[0041] In some embodiments, the exhaust gas negative pressure recovery curve is a continuous data curve recording the recovery of the exhaust gas negative pressure from the disturbance state to the stable state after the air volume disturbance is applied, and the generation step is as follows:

[0042] When the pseudo-random air volume disturbance is applied, 10 negative pressure data (such as -35Pa, -32Pa, -28Pa...) are collected by the exhaust gas outlet pressure sensor every second;

[0043] After stopping the disturbance, continue to collect negative pressure data until stable (such as -40Pa→-42Pa→-45Pa→stable at -48Pa).

[0044] Plot the data on the horizontal axis (in seconds) and the vertical axis (in Pa) to form a curve, and you will get the smoke exhaust negative pressure recovery curve.

[0045] For example: After a disturbance is applied to the quenching zone of a kiln, the negative pressure drops from a stable value of -50 Pa to -30 Pa (during the disturbance). After the disturbance stops, the pressure drops to -32 Pa in the first second, -36 Pa in the second second, -42 Pa in the third second, -48 Pa in the fourth second, and stabilizes at -50 Pa in the fifth second. Plotting these data (time: 0-5 seconds, negative pressure: -30→-32→-36→-42→-48→-50) into a curve is the flue gas negative pressure recovery curve for this disturbance. Its slope (e.g., the slope in the second-3rd second is 6 Pa / s) can be used to determine the recovery rate.

[0046] The recovery threshold is the critical slope value for determining whether the smoke exhaust system can recover to a stable state. If the slope is greater than this value, it means that the negative pressure recovery speed meets the requirements and an air volume redistribution command can be generated.

[0047] For example, for the smoke exhaust system in the firing zone (with small negative pressure fluctuations), the recovery threshold can be set to 0.3 kPa / s (that is, after the disturbance stops, when the negative pressure of the smoke exhaust recovers from -30 Pa to -50 Pa, it is determined to be back to normal when the pressure rises by ≥0.3 kPa per second); for the smoke exhaust system in the quenching zone (with large negative pressure fluctuations), the recovery threshold can be set to 0.5 kPa / s (that is, when the negative pressure recovers from -20 Pa to -40 Pa, it is determined to be back to normal when the pressure rises by ≥0.5 kPa per second).

[0048] For example, the phase threshold is the critical angle for determining whether there is an abnormal lag between temperature fluctuations and airflow disturbances (phase lag = time difference / firing cycle × 360°). Exceeding this value indicates abnormal heat transfer efficiency. For example, for a small kiln (firing cycle of 2 hours), the phase threshold can be set to 30° (i.e., when the time difference between temperature fluctuations and lag disturbances exceeds 10 minutes, the lag is considered abnormal); for a large kiln (firing cycle of 4 hours), the phase threshold can be set to 45° (i.e., when the time difference exceeds 30 minutes, the lag is considered abnormal).

[0049] In S105, the regulating capacity of the quench system is determined by the curve characteristics, and the system response after disturbance (negative pressure recovery slope, temperature lag) is converted into control indicators. For example, if the negative pressure recovery is slow and the temperature lag is large, it indicates that there is a bottleneck in the air volume distribution. The generated redistribution command can accurately optimize the fan output, ensuring the quench effect while avoiding energy waste caused by excessive air volume, thus achieving a balance between efficiency and energy consumption.

[0050] S106 When the correlation coefficient between the time-domain characteristics of the residual oxygen correlation component and the rate of change of residual oxygen concentration exceeds the correlation threshold, the calorific value compensation parameter is output to the combustion controller.

[0051] The correlation threshold is a critical value used to determine whether the pressure fluctuation component can reflect combustion efficiency. A correlation coefficient exceeding this value indicates that the component can be used to calculate calorific value compensation parameters. For example, for natural gas combustion kilns (with stable combustion and high correlation), the correlation threshold can be set to 0.8 (i.e., when the correlation coefficient ≥ 0.8, the component effectively reflects residual oxygen changes); for mixed fuel (coal gas + biomass) kilns (with large combustion fluctuations), the correlation threshold can be set to 0.7 (i.e., when the correlation coefficient ≥ 0.7, it can be used to calculate compensation parameters).

[0052] In S106, the correlation between pressure fluctuations and residual oxygen changes is used to calculate the compensation value of fuel calorific value in real time. This allows the combustion controller to dynamically adjust the fuel supply according to the current combustion state (e.g., increasing the calorific value input when residual oxygen is high), ensuring complete combustion of fuel and directly reducing fuel consumption per unit product. This achieves closed-loop control from data analysis to energy consumption optimization.

[0053] refer to Figure 1 The industrial information and data processing in this application belongs to the field of computer and auxiliary equipment technology.

[0054] Furthermore, such as Figure 2 As shown, data acquisition is triggered based on kiln operating conditions to obtain pressure fluctuation data; residual oxygen components related to combustion efficiency are extracted from the pressure fluctuation data, including:

[0055] S201, associates the real-time data collected by the distributed thermocouples, pressure sensors and residual oxygen meter with the kiln car location area identified by the current spatiotemporal tag;

[0056] In S201, data from thermocouples, pressure sensors, and residual oxygen meters are linked to the kiln car's location area. For example, the temperature, pressure, and residual oxygen data of a kiln car in three sections of the firing zone are uniformly marked, providing an accurate location basis for subsequent regional fault diagnosis (such as a section's seal failure).

[0057] S202, when the rate of change of residual oxygen concentration exceeds the first threshold, the primary triggering condition is activated;

[0058] The first threshold is the critical value for activating the primary data acquisition trigger condition. When the rate of change in residual oxygen concentration exceeds this value, it indicates that the combustion state may be abnormal, and further monitoring of the pressure spectrum is required. For example, for natural gas combustion kilns (stable combustion), the first threshold can be set to ±0.5% / min (i.e., when the residual oxygen concentration rises from 5% to 5.5% or falls to 4.5% within 1 minute, the primary trigger is activated); for coal gas combustion kilns (large combustion fluctuations), the first threshold can be set to ±1.0% / min (i.e., when the residual oxygen concentration changes by more than ±1% within 1 minute, the primary trigger is activated).

[0059] In S202, residual oxygen is the most direct indicator of combustion efficiency, and its sudden changes often indicate combustion abnormalities. Subsequent monitoring is only initiated when the rate of change in residual oxygen exceeds a threshold, avoiding excessive intervention in normal combustion conditions. This simplifies the triggering logic and ensures that abnormal operating conditions are captured in a timely manner, providing a reasonable start point for multimodal data acquisition.

[0060] S203, after activating the primary triggering condition, when the pressure spectrum of the kiln car location area changes more than the second threshold in the preset frequency band and the infrared detection device detects that the seal of the kiln car location area has failed, the multi-mode acquisition strategy is dynamically configured according to the sealing status and pressure spectrum characteristics of the kiln car location area.

[0061] The second threshold is a critical value for determining whether pressure fluctuations are related to abnormal combustion. It needs to be set in conjunction with the energy changes in the preset frequency band. When this value is exceeded, multimodal acquisition needs to be activated.

[0062] For example, in the firing zone (mainly low-frequency pressure fluctuations), the second threshold can be set to 15% (that is, when the pressure energy in the preset frequency band 50-150Hz changes by more than 15% compared to the baseline value, the trigger condition is met); in the preheating zone (mainly high-frequency pressure fluctuations), the second threshold can be set to 20% (that is, when the pressure energy in the preset frequency band 150-300Hz changes by more than 20%, the trigger condition is met).

[0063] In some embodiments, the preset frequency band is divided according to the source of pressure fluctuations in the kiln, including but not limited to: 10-50Hz: pressure fluctuations caused by low-frequency mechanical disturbances such as kiln car movement and kiln door opening and closing; 50-150Hz: turbulent fluctuations caused by medium-speed operation of emergency cooling fans; 150-300Hz: pressure fluctuations caused by high-frequency turbulence such as high-pressure fans and nozzle injection; 300-500Hz: high-frequency pressure oscillations caused by pulse combustion of burners (only applicable to pulse combustion kilns).

[0064] Infrared detection devices include, but are not limited to, infrared thermal imagers.

[0065] In S203, data acquisition parameters are customized according to the fault type (such as seal cracks or missing sand seals). For example, the infrared scanning frequency is increased when the seal fails, and the sampling rate is increased when the pressure fluctuates at high frequencies. This allows the acquisition strategy to match the actual fault characteristics, avoiding data redundancy or missing key information caused by a "one-size-fits-all" approach to acquisition.

[0066] S204, execute a multimodal acquisition strategy to obtain pressure fluctuation data;

[0067] In S204, characteristic signals of different faults can be captured by flexibly adjusting the sampling rate and duration. For example, a low sampling rate and long acquisition time are used for low-frequency pressure fluctuations of sealing cracks, while a high sampling rate and short acquisition time are used for high-frequency fluctuations of combustion oscillations, thus optimizing acquisition costs while ensuring data quality.

[0068] S205, calculate leakage parameters based on the geometric characteristics of seal failure determined by infrared detection, and use the leakage parameters to calculate seal-related pressure components;

[0069] In S205, physical structural defects are converted into pressure disturbance values. This step accurately separates pressure fluctuations caused by sealing problems, avoids misjudging leaks as combustion anomalies, clears the way for subsequent extraction of combustion-related pressure components, and improves the accuracy of data interpretation.

[0070] S206, Subtract the sealing-related pressure component from the pressure fluctuation data to obtain the residual pressure component;

[0071] In S206, the sealing-related component is subtracted from the total pressure fluctuation to eliminate interference from non-combustion factors, retaining only the pressure signal related to combustion. For example, if pressure fluctuations occur in a certain area due to the lack of sand seals, removing this influence will leave the residual component that accurately reflects the burner's operating status, providing an interference-free data source for analyzing combustion efficiency.

[0072] S207, extract the frequency band with the strongest time-domain correlation with the residual oxygen concentration change rate from the residual pressure component as the residual oxygen correlation component.

[0073] In some embodiments, the residual pressure component is the pressure fluctuation after removing the effects of sealing leakage (related only to the combustion state). The frequency band with the strongest correlation in the time domain refers to the frequency band where the pressure fluctuation and the change in residual oxygen concentration have the highest matching degree in time trend (which can be calculated by the Pearson correlation coefficient; the closer the coefficient is to 1, the stronger the correlation).

[0074] Changes in combustion efficiency affect both residual oxygen concentration (low residual oxygen when combustion is complete, and high residual oxygen when combustion is incomplete) and pressure fluctuations (changes in combustion intensity cause furnace pressure fluctuations). The correlation between pressure fluctuations and residual oxygen changes varies at different frequency bands, and calculations are needed to find the frequency band that best reflects combustion efficiency.

[0075] For example, the residual pressure component of a kiln includes fluctuations in three frequency bands: 20-40Hz, 60-80Hz, and 100-120Hz. Calculate the time-domain correlation coefficient between each frequency band and the rate of change of residual oxygen concentration.

[0076] 20-40Hz frequency band: correlation coefficient 0.6 (pressure fluctuations and residual oxygen change trends partially match);

[0077] 60-80Hz frequency band: correlation coefficient 0.85 (the time points of pressure fluctuation peak / valley and residual oxygen concentration peak / valley are highly coincident, and the trends are completely consistent);

[0078] 100-120Hz frequency band: correlation coefficient 0.4 (low correlation);

[0079] At this point, 60-80Hz is the frequency band with the strongest correlation to the time domain. Pressure fluctuations in this frequency band can directly reflect the fluctuations in residual oxygen concentration caused by changes in combustion efficiency, and therefore it is extracted as the residual oxygen correlation component.

[0080] In S207, core features are identified by utilizing temporal correlation. By calculating the time trend matching degree between each frequency band and the residual oxygen change, the pressure signal frequency band that best reflects the combustion state is found. For example, if the peak pressure in a certain frequency band occurs at a time that is completely synchronized with the trough of residual oxygen concentration (complete combustion), this frequency band is identified as the residual oxygen correlation component. This allows subsequent analysis to directly focus on the key signal of combustion efficiency, significantly improving the accuracy of energy consumption optimization parameter calculation.

[0081] Based on the embodiments provided in this application, by associating real-time data collected by distributed sensors with the kiln car location area corresponding to the spatiotemporal tag, energy consumption data is more closely bound to specific spatial locations; through multi-level triggering conditions such as residual oxygen concentration change rate, pressure spectrum characteristics, and sealing failure state, dynamic activation of data acquisition is realized, avoiding redundancy caused by indiscriminate acquisition; by combining the geometric characteristics of sealing failure to calculate leakage parameters and stripping the sealing-related pressure components, the interference of sealing problems on pressure fluctuation data is effectively eliminated, making the extracted residual oxygen correlation components more consistent with the actual combustion state and improving the accuracy of combustion efficiency-related data analysis.

[0082] Furthermore, a multi-modal acquisition strategy is dynamically configured based on the sealing status and pressure spectrum characteristics of the kiln car location area, including:

[0083] Construct an energy transfer topology model for the kiln heat exchange unit;

[0084] The energy transfer topology model includes the following heat transfer paths: the radiative heat transfer link between the firing zone unit and the current kiln car location area, with the radiative heat flux calculated using the Stefan-Boltzmann law; the convective heat transfer link between the quench zone unit and the current kiln car location area, with the convective heat flux calculated using Newton's law of cooling; and the heat conduction link between the kiln body fixed structure unit (including the kiln wall and kiln roof) and the firing zone unit and quench zone unit, with the conduction heat flux calculated using Fourier's law.

[0085] Calculate the turbulence intensity coefficient based on the pressure spectrum characteristics of the kiln car location region;

[0086] Among them, the turbulence intensity coefficient is an indicator reflecting the degree of airflow turbulence inside the kiln, and its calculation is based on the spectral characteristics of the fluctuation data collected by the pressure sensor:

[0087] In some embodiments, the pressure signal is first subjected to Fourier transform to obtain the pressure energy distribution (i.e., pressure spectrum) at different frequencies; characteristic frequency bands related to airflow turbulence are extracted (e.g., turbulence in the main flow zone of the kiln is concentrated in 20-200Hz, while that in the edge zone is concentrated in 5-50Hz), and the root mean square (RMS) value of pressure fluctuation in this frequency band is calculated; the RMS value is divided by the average static pressure of the region to obtain the turbulence intensity coefficient (the formula is simplified to: turbulence intensity coefficient = characteristic frequency band pressure RMS / average static pressure). For example, when a kiln car is in the middle of the firing zone, the average static pressure is -40Pa, and the RMS pressure in the 20-100Hz frequency band is 6Pa, then the turbulence intensity coefficient = 6 / 40 = 0.15, reflecting the moderate degree of turbulence in the airflow at this time.

[0088] By loading real-time temperature gradient and turbulence intensity coefficient into the energy transfer topology model, the heat loss rate in the kiln car location region is calculated.

[0089] In some embodiments, the heat loss rate is the proportion of heat lost in the region through radiation, convection, and conduction to the total input heat. The calculation logic is as follows: In the energy transfer topology model, the radiation heat transfer path is calculated according to the Stefan-Boltzmann law; the convection heat transfer path is modified by the turbulence intensity coefficient (the stronger the turbulence, the larger the heat transfer coefficient); the heat conduction path is calculated according to Fourier's law (based on the temperature gradient of the kiln structure); the total heat loss rate = (radiation heat loss + convection heat loss + conduction heat loss) / total input heat × 100%.

[0090] When a deviation of the heat loss rate from the theoretical value is detected, the sensors of adjacent heat exchange units are activated for collaborative monitoring.

[0091] The theoretical value is the ideal heat loss rate calculated based on kiln design parameters (such as the thermal conductivity of insulation materials, sealing structure design standards, and theoretical fuel combustion efficiency), used to determine whether the actual heat loss is abnormal. For example, in the firing zone (high-temperature zone, with a designed insulation layer thickness of 300mm), the theoretical heat loss rate is calculated based on the material's thermal conductivity (0.1W / (m·K)) and the designed temperature difference (500℃), and the theoretical value is set to ≤15%; in the quench zone (medium-temperature zone, with a insulation layer thickness of 200mm), the theoretical heat loss rate is calculated based on the convective heat transfer design limit, and the theoretical value is set to ≤20%.

[0092] Based on the type of seal failure and the deviation of heat loss rate, the optimal combination of acquisition parameters is matched from the historical database. The optimal combination of acquisition parameters includes sampling rate, acquisition duration and sensor activation scheme.

[0093] In one specific implementation, the dynamic heat loss rate is determined based on the following formula:

[0094]

[0095] Where, η loss The dynamic heat loss rate reflects the proportion of total heat loss in the kiln car location area; q rad The basic heat flux density for radiative heat transfer (W / m³) 2 The radiative heat flux measured by the thermocouple array in the firing zone (e.g., up to 5000 W / m in the high-temperature zone of the firing zone) is obtained from the actual radiative heat flux measured by the thermocouple array in the firing zone. 2 );ε comp As a material emissivity compensation factor, it is dynamically adjusted according to the kiln age (1.0 for new kilns, 0.92 for 3-year-old kilns, and 0.85 for kilns over 5 years old) to correct the decrease in radiation capacity caused by the aging of refractory materials. The temperature gradient (K / m) between the firing zone and the kiln car area, for example, the temperature gradient between the edge of the firing zone and the surface of the kiln car is 800 K / m; q ref The reference heat flux density (W / m) 2 The rated heat flow of the kiln design (e.g., 8000W / m³) should be taken as the reference. 2 );k tur q is the turbulence intensity coefficient, calculated from the pressure spectrum (typically 0.15 to 0.25 in the quench zone due to fan disturbance); conv The basic heat flux density for convective heat transfer (W / m³) 2 ), converted from the wind speed sensor in the rapid cooling zone (e.g., 1200W / m when the wind speed is 10m / s). 2 );f(k tur () is a nonlinear function of the convection coefficient and turbulence intensity, expressed as: This demonstrates the nonlinear effect of enhanced turbulence on convective heat transfer; This represents the temperature gradient (K / m) between the quench zone and the kiln car area; for example, the gradient near the quench tuyeres is 500 K / m. wall q is the kiln body heat dissipation surface area coefficient, the ratio of the actual heat dissipation area to the reference area (e.g., 1.2 for the firing zone); cond The basic heat flux density for conduction and heat transfer (W / m³) 2 ), calculated from the kiln body temperature monitoring points (e.g., 300W / m corresponding to the temperature difference inside the kiln wall). 2 );ρ grid is the grid density coefficient, which is 1.5 when the temperature gradient is large (e.g., inside the kiln wall) and 0.8 when the temperature gradient is small (e.g., outside the kiln wall). The temperature gradient (K / m) between the inner and outer walls of the kiln, such as 200K / m corresponding to the temperature difference on both sides of the refractory brick; α seal The coefficient for sealing failure is 1.4 for masonry cracks, 1.2 for missing sand seals, and 1.0 for no failures.

[0096] In some embodiments, the sensor activation scheme matches the optimal combination of sensors (number, location, type) from a historical database based on the seal failure type and pressure spectrum characteristics.

[0097] For example, the database stores historical cases: such as "masonry crack failure + low-frequency pressure fluctuation" corresponds to "activating 3 pressure sensors near the crack + 1 set of infrared thermal imagers (scanning crack length)"; "sand seal missing + high-frequency pressure fluctuation" corresponds to "activating 4 pressure sensors at the bottom + laser rangefinder (measuring sand seal thickness)".

[0098] When matching, prioritize historical case schemes that are closest to the current sealing type and turbulence intensity coefficient. If the similarity is ≥80%, reuse directly; otherwise, fine-tune the number of sensors (e.g., add 1 redundant sensor).

[0099] For example: if "sand seal failure" is detected and the pressure spectrum shows prominent energy in the 50-150Hz range, the historical solution is matched with "activating bottom pressure sensors (numbered P1-P4) + laser rangefinder (L2)" to ensure accurate capture of air leakage characteristics.

[0100] Based on the embodiments provided in this application, an energy transfer topology model incorporating multiple paths of radiation, convection, and conduction is constructed to comprehensively reflect the complex heat exchange process within the kiln. The turbulence intensity coefficient converted from pressure spectrum characteristics and the real-time temperature gradient are loaded into the model, making the calculation of heat loss rate more closely match the actual thermal state of the kiln car location area. The collaborative monitoring of adjacent units is activated through heat loss rate deviation, expanding the correlation of data acquisition. Based on the sealing failure type and heat loss rate deviation, the optimal combination of acquisition parameters is matched, enabling the data acquisition strategy to dynamically adapt to different operating states of the kiln, improving the relevance and effectiveness of energy consumption data.

[0101] Furthermore, a multimodal acquisition strategy is implemented to obtain pressure fluctuation data, including:

[0102] The disturbance target is selected based on the main source of the heat loss rate deviation in the kiln car location area, including: if the deviation mainly comes from the radiative heat transfer link in the firing zone, then the air-fuel ratio of the burner in the firing zone is disturbed; if the deviation mainly comes from the convective heat transfer link in the quench zone, then the speed of the quench fan is disturbed; if the deviation mainly comes from the heat conduction link in the kiln body structure, then the heating power of the kiln wall insulation system is disturbed.

[0103] During the duration of the disturbance, pressure sensor data are recorded according to the optimal combination of acquisition parameters, including the sampling rate and acquisition duration.

[0104] A two-dimensional energy consumption response curve is constructed based on the recorded pressure sensor data, with the horizontal axis representing the change in disturbance parameters and the vertical axis representing the change in heat loss rate.

[0105] Among them, the two-dimensional energy consumption response curve is a visualization tool that reflects the relationship between the change in disturbance parameters and the change in heat loss rate, and is used to locate the potential area for energy consumption optimization:

[0106] The horizontal axis “disturbance parameter change” includes parameters such as air-fuel ratio adjustment (±5%) and fan speed change (±100r / min), set in gradients (e.g., adjust the air-fuel ratio by 2% per step).

[0107] The vertical axis, "Change in heat loss rate," corresponds to the difference between the heat loss rate and the initial value for each disturbance parameter (e.g., from 30% to 25%, the change is -5%).

[0108] Identify the section in the two-dimensional energy consumption response curve where the heat loss rate continuously decreases with the increase of the disturbance parameter as the potential area for optimization;

[0109] Among them, continuous decline means that during the adjustment of disturbance parameters, the heat loss rate continues to decrease as the parameters increase, and the results of two adjacent adjustments both show a downward trend (without rebound), and the duration must cover at least 3 disturbance cycles (to ensure trend stability).

[0110] For example, in a small kiln, when the air-fuel ratio is adjusted (1 minute per cycle), the heat loss rate decreases from 32% to 30% to 28% to 27% for 4 consecutive cycles, lasting for 4 minutes, when the air-fuel ratio changes from 1.0 to 1.03 to 1.06 to 1.09, and this decrease is considered an effective optimization range. In a large kiln, when the fan speed is adjusted (2 minutes per cycle), the heat loss rate decreases from 26% to 24% to 23% for 3 consecutive cycles, lasting for 6 minutes, when the fan speed changes from 900 to 950 to 1000 r / min, and this decrease is considered a continuous decrease, meeting the "continuous decrease" condition.

[0111] This setting can eliminate the interference of random fluctuations and accurately locate the truly effective optimization direction.

[0112] The acquisition parameters are adjusted according to the characteristics of the optimization potential area, including: when the main frequency of pressure fluctuation exceeds the set frequency threshold, the sampling rate is increased to the first set value; when the rate of decrease of heat loss rate in the optimization potential area exceeds the set rate threshold, the acquisition time is extended to the second set value.

[0113] The frequency threshold is set to determine whether the pressure fluctuation frequency is too high, requiring an increase in the sampling rate to capture details. For example, the airflow in the firing zone is relatively stable, so the frequency threshold is set to 100Hz (when the main frequency of pressure fluctuation exceeds 100Hz, it indicates that there may be abnormal turbulence, and the sampling rate needs to be increased); in the quench zone, because the fan operates at a higher frequency, the threshold is set to 150Hz (if it exceeds this, the sampling rate should be increased).

[0114] A rate threshold is set to determine if the rate of decrease in heat loss is too fast, and the sampling time needs to be extended to observe stability. For example, the heat loss rate in the firing zone usually decreases more slowly, so the rate threshold is set to 2% / min (if the rate of decrease exceeds 2% / min, the sampling time should be extended); the adjustment effect is more obvious in the quenching zone, so the threshold is set to 3% / min (if it exceeds this, the sampling time should be extended).

[0115] The first set value (sampling rate) is the sampling rate standard when there are high-frequency fluctuations. For example, the original sampling rate of the firing zone is 100Hz, which is increased to 200Hz after exceeding the frequency threshold to ensure that the fluctuation details of 100-200Hz are captured; the original sampling rate of the quench zone is 150Hz, which is increased to 300Hz after exceeding the threshold to cover high-frequency signals of 150-300Hz.

[0116] The second setting (sampling time) is the standard sampling time during rapid decline. For example, the original sampling time for the firing zone is 5 minutes, which is extended to 10 minutes after the rate threshold is exceeded to observe whether the heat loss rate decreases steadily; the original sampling time for the quenching zone is 8 minutes, which is extended to 15 minutes after the threshold is exceeded to avoid missing key changes due to insufficient sampling time.

[0117] Reacquire pressure fluctuation data using the adjusted acquisition parameters;

[0118] The energy transfer topology model is updated based on the newly acquired pressure fluctuation data; the updated heat loss rate deviation is calculated.

[0119] If the updated heat loss rate deviation does not exceed the set convergence threshold, output the pressure fluctuation data.

[0120] The convergence threshold is set to determine whether the deviation of the heat loss rate is small enough. For example, the firing zone has high accuracy requirements, so the convergence threshold is set to 1% (the data is reliable when the deviation between the actual heat loss rate and the theoretical value is ≤1%); the threshold is slightly more lenient in the quenching zone, so the convergence threshold is set to 2% (output is possible when the deviation is ≤2%).

[0121] If the updated heat loss rate deviation exceeds the set convergence threshold, the current collection parameter combination will be marked as invalid and a rematching process will be triggered, that is, the best collection parameter combination will be matched again from the historical database.

[0122] It should be noted that kiln operating conditions are dynamic (e.g., seals gradually age, fuel calorific value fluctuates), and the initial data collected may lead to inaccurate calculations of heat loss rate due to initial model errors (e.g., failure to consider newly emerging micro-cracks). By re-collecting data and updating the energy transfer topology model (e.g., correcting the convective heat transfer coefficient), the deviation between theoretical and actual values ​​can be gradually reduced. If the deviation is less than or equal to the convergence threshold, it indicates that the data can reflect the true energy consumption state and can be used to generate optimization instructions; if the deviation still exceeds the limit, the current parameter combination is marked as invalid and re-matched to avoid adjustments based on erroneous data (e.g., blindly increasing fan power due to misjudgment of insufficient airflow, which actually increases energy consumption). This closed-loop process ensures that the data is always synchronized with the actual operating conditions, significantly improving the reliability of energy consumption optimization.

[0123] Based on the embodiments provided in this application, the disturbance object is accurately selected according to the main source of the heat loss rate deviation, so that the energy consumption data collection can focus on key influencing factors; by constructing a two-dimensional energy consumption response curve to identify the optimization potential area, a clear basis is provided for the adjustment of the collection parameters; the sampling rate and collection duration are dynamically adjusted according to the pressure fluctuation frequency and the rate of decrease of the heat loss rate, ensuring that more refined data can be obtained in areas with drastic energy consumption changes; by iteratively updating the energy transfer topology model until the heat loss rate deviation converges, the reliability of the pressure fluctuation data is improved.

[0124] Furthermore, a pseudo-random airflow disturbance with the same frequency as the quench fan is applied, including:

[0125] Set the basic air volume by adjusting the butterfly valve on the quench air duct;

[0126] The frequency converter is controlled to superimpose random fluctuation components onto the base air volume to generate a disturbance sequence;

[0127] In some embodiments, in the kiln quench zone, the base air volume is a reference value to ensure the cooling effect of the product (set by a scale-adjusting butterfly valve, for example, 5000 m³ / h based on the kiln car loading capacity). 3 The addition of random fluctuation components is to simulate the system response under different airflow rates and find the optimal airflow distribution. Specifically, a small random variation (e.g., ±5% fluctuation, i.e., 4750~5250m³ / h) is added to the base airflow using a frequency converter. 3 These fluctuations (which vary randomly between / h) are arranged in chronological order to form a disturbance sequence (e.g., 5100m at the 1st second). 3 / h, 4800m in the 2nd second 3 / h, 3rd second 5050m 3 / h…). This pseudo-random sequence retains synchronization with the frequency of the quench fan (e.g., 50Hz) while covering different airflow conditions, ensuring that subsequent analysis can capture the system's true response to changes in airflow (e.g., a significant improvement in temperature uniformity under a certain fluctuation value).

[0128] Real-time monitoring of the pressure difference between the air curtain ducts on both sides of the kiln wall;

[0129] The disturbance amplitude is automatically attenuated when the pressure difference exceeds the safety difference threshold;

[0130] If the pressure difference between the air curtain ducts on both sides of the kiln wall is too large, it may lead to uneven stress on the kiln wall (such as excessive pressure on one side squeezing the kiln wall), or even damage to the sealing structure. The safety difference threshold is the critical value to avoid this risk. For example, for newly built kilns (with intact kiln wall structures), the safety difference threshold is set at 50Pa (i.e., the pressure difference between the two sides ≤ 50Pa, such as 100Pa on the left and 60Pa on the right, a difference of 40Pa is safe); for old kilns that have been in use for more than 5 years (with slight deformation of the kiln wall), the safety difference threshold is set at 30Pa (the difference ≤ 30Pa, to avoid the inability to withstand large pressure differences due to structural aging).

[0131] During the duration of the disturbance, the rate of change of the pressure gradient inside the kiln shall not exceed the set rate limit.

[0132] If the rate of change of the pressure gradient inside the kiln is too rapid, it will cause a sudden rise and fall in temperature (for example, a sudden increase in pressure may bring in a large amount of cold air, causing the product to crack due to rapid cooling). The set rate limit is a "retarder" to control the pressure change. For example, when producing precision ceramics (which have weak resistance to temperature differences), the set rate limit is set to 0.2 kPa / min (that is, the pressure change does not exceed 0.2 kPa per minute; for example, it takes 1 minute for the pressure to change from -40 Pa to -39.8 Pa by 0.2 kPa); when producing building ceramic tiles (which have strong resistance to temperature differences), the set rate is set to 0.5 kPa / min (the pressure change can be slightly faster, while taking into account cooling efficiency).

[0133] Obtain the phase synchronization index between the disturbance waveform and the operating frequency of the quench fan;

[0134] Among them, the phase synchronization index is used to measure the degree of matching between pseudo-random airflow disturbance and the operating frequency of the quench fan (the fan frequency determines the basic fluctuation period of the airflow, and the disturbance needs to be synchronized with it to accurately reflect the system response). For example, the phase difference can be used to measure: the smaller the time difference between the peak value of the disturbance waveform and the peak value of the fan frequency, the higher the synchronization (the synchronization is best when the time difference is 0).

[0135] For example, if the quench fan operates at a frequency of 50Hz (period 0.02 seconds), and the time difference between the peak value of the disturbance waveform and the peak value of the fan is 0.002 seconds, the phase difference is 36 degrees (0.002 / 0.02×360), the synchronization index can be expressed as "1-36 / 180=0.8" (the closer the value is to 1, the better the synchronization). The set standard is the critical value for judging whether the synchronization meets the standard: for the end of the firing zone with high synchronization requirements, the standard is set to 0.8 (synchronization is qualified when the index ≥ 0.8); the standard can be slightly relaxed in the middle section of the quench zone, set to 0.7 (qualified when the index ≥ 0.7). If the index is lower than the standard (e.g., only 0.6), the disturbance sequence needs to be regenerated to ensure that the disturbance resonates with the fan frequency, avoiding distortion of response data due to asynchrony.

[0136] The perturbation sequence is reinitialized when the phase synchronization index falls below the set standard.

[0137] In one specific implementation, the phase synchronization index is determined based on the following formula:

[0138]

[0139] Where, γ sync t represents the phase synchronization index (dimensionless, 0 to 1), with a value closer to 1 indicating better synchronization between the disturbance and the wind turbine frequency; n is the number of sampling points, which is 100 in this embodiment (sampling once every 0.001 seconds, covering one wind turbine cycle); t dis,i Let t be the peak time (s) of the disturbance waveform at point i, such as the peak moment of pseudo-random airflow fluctuation; fan,i f represents the peak frequency time (in seconds) of the fan at point i, such as the peak time of a 50Hz quench fan; fan The fan's operating frequency (Hz), such as a quench fan rated at 50Hz; ΔP curtain P represents the pressure difference in the air curtain duct (Pa), specifically the real-time pressure difference between the air curtains on both sides of the kiln wall (e.g., 30 Pa). base The reference pressure (Pa) is the air curtain pressure at the base air volume (e.g., 200Pa).

[0140] Based on the embodiments provided in this application, the basic air volume is set by adjusting the butterfly valve with a scale and superimposing a random fluctuation component, which ensures the basic stability of the air volume in the quench zone while realizing pseudo-random air volume disturbance; the pressure difference of the kiln wall air curtain is monitored in real time and the disturbance amplitude is attenuated after exceeding the limit, avoiding excessive impact of the disturbance on the pressure balance inside the kiln; the rate of change of the pressure gradient inside the kiln is maintained within the limit, ensuring the safety of kiln operation; the disturbance sequence is calibrated by the phase synchronization index, ensuring the coordination between the air volume disturbance and the working frequency of the quench fan, and improving the effectiveness of the disturbance in correcting temperature deviation.

[0141] Furthermore, the calculation of the geometric characteristics of the seal failure includes:

[0142] The sealing structure of the kiln car location area is scanned using an infrared detection device;

[0143] Identify temperature anomaly distribution patterns and classify failure types, including: when the temperature is distributed in a strip pattern, it is classified as masonry crack failure; when the temperature is distributed in a patchy pattern, it is classified as sand seal loss failure.

[0144] In infrared detection of kiln seal failure, abnormal temperature distribution directly reflects the failure mode:

[0145] Strip-shaped distribution: When cracks appear in the refractory brick masonry between the kiln car and the kiln wall, high-temperature flue gas leaks linearly along the cracks, appearing as a continuous long strip of high-temperature area on the infrared image (such as a red strip 1 meter long and 5 centimeters wide), corresponding to "masonry crack failure" (the crack is a narrow channel, and the flue gas flows directionally along the channel).

[0146] Patchy distribution: When the sand seal layer at the bottom of the kiln car is partially missing, cold air seeps in and forms a dispersed low-temperature area. In the infrared image, it appears as an irregular blocky low-temperature area (such as a blue patch with a diameter of 30 cm), which corresponds to "sand seal failure" (the missing area is a local depression, and the air leakage is diffused in a planar manner).

[0147] Measure the geometry of the failure area, including: extracting the length and depth of masonry cracks; extracting the area and boundary curvature of missing sand seals;

[0148] The hydraulic model is selected based on the failure type, including: for masonry cracks, the slit flow model is used to calculate the leakage area; for missing sand seals, the orifice flow model is used to calculate the leakage area.

[0149] In some embodiments, the slot flow model is used for masonry cracks (elongated channels, such as cracks 2 mm wide and 50 cm long). The crack is treated as a slit between parallel plates, and the effective flow area of ​​flue gas through the slit is calculated based on the length, width, and depth of the crack (e.g., if the crack is 2 mm wide and 50 cm long, the effective leakage area is approximately 0.001 m²). 2 );

[0150] The orifice flow model is used for sand seal defects (local depressions, such as a circular defect with a diameter of 30cm). The defect area is treated as a circular orifice or irregular opening. Based on the area and boundary curvature of the defect area, the effective flow area for cold air infiltration is calculated (for example, for a circular defect with a diameter of 30cm, the effective leakage area is approximately 0.07m²). 2 ).

[0151] It should be noted that both the slit flow model and the orifice flow model are well-known technologies, and will not be described in detail in this embodiment.

[0152] Input the calculated leakage area into the fluid dynamics equation to calculate the dynamic leakage coefficient;

[0153] The dynamic leakage coefficient is a parameter reflecting the change in leakage rate with pressure (the greater the pressure difference, the faster the leakage). The fluid dynamics equations are well-known and will not be elaborated upon in this embodiment.

[0154] A sealing component correction curve is generated based on dynamic leakage coefficient and pressure gradient data.

[0155] In one specific implementation, the dynamic leakage coefficient is determined based on the following formula:

[0156]

[0157] Among them, C leak The dynamic leakage coefficient (m) 3 / (s·Pa 0.5 )), representing the air leakage under a unit pressure difference; λ type For failure type correction factors, 0.8 is used for masonry cracks (high flow resistance in narrow slots), and 1.2 is used for missing sand seals (low flow resistance at orifices); A leak Leakage area (m) 2 ), calculated using a slit flow model (crack) or an orifice flow model (missing sand seal) (e.g., crack 0.001m). 2 );ρ gas Flue gas density under standard conditions (kg / m³) 3 );L path The leakage path length (m) is taken as 0.5m for masonry cracks and 0.1m for missing sand seals; 300K is the reference temperature; T leak The failure zone temperature (K), such as 1173K (900℃) at the crack in the firing zone; D hyd Hydraulic diameter (m) (characterizing the diameter of the channel; for cracks, the width is taken as the crack width; for sand seals, the equivalent diameter is taken as the equivalent diameter).

[0158] Based on the embodiments provided in this application, the sealing structure is scanned by an infrared detection device and failure types such as masonry cracks and missing sand seals are classified, thus achieving accurate identification of sealing problems. For different failure types, the corresponding geometric dimensions are measured and a matching hydraulic model is selected, making the calculation of the leakage area more consistent with the actual failure state. Based on the dynamic leakage coefficient and pressure gradient, a sealing component correction curve is generated, which provides an accurate basis for removing sealing interference in pressure fluctuation data and further improves the correlation between pressure data and combustion state.

[0159] Furthermore, the calculation of the heat transfer path includes:

[0160] Temperature distribution data of the sintered tape thermocouple array is loaded into the radiative heat transfer link;

[0161] It should be noted that uneven temperature distribution in the firing zone can lead to deviations in radiative heat transfer. Thermocouple arrays (such as one set arranged every 1 meter along the kiln length, with 3 thermocouples in each set measuring the upper, middle, and lower temperatures) can capture a three-dimensional temperature field (e.g., the upper side of the front section of the firing zone is 1200℃, and the lower side is 1150℃). By loading this data into the radiative heat transfer link, the radiative heat at different locations can be accurately calculated (e.g., the upper high-temperature zone radiates 20% more heat to the kiln car than the lower zone), avoiding misjudgments of heat loss due to average temperature calculations.

[0162] When calculating radiative heat flux, a material emissivity compensation factor is introduced, which is dynamically adjusted according to the kiln age cycle.

[0163] The emissivity (a parameter measuring radiation capacity) of kiln refractory materials changes with kiln age (new kilns have smooth material surfaces, while older kilns have rough surfaces due to coking or wear). In some embodiments, a material emissivity compensation factor is used to correct for this variation:

[0164] New kiln (within 1 year of use): refractory brick surface is clean, emissivity is the original design value of 0.85, compensation factor is set to 1.0 (no correction required);

[0165] Mid-aged kiln (3 years of use): slight coking on the surface, emissivity actually dropped to 0.8, compensation factor set to 0.8 / 0.85≈0.94 (the calculated radiant heat is more accurate after correction);

[0166] Old kiln (used for more than 5 years): The surface is severely worn, and the emissivity drops to 0.7. The compensation factor is set to 0.7 / 0.85≈0.82.

[0167] Real-time measurements from a quench zone wind speed sensor are integrated into the convective heat transfer link.

[0168] A nonlinear function that correlates the convection coefficient with the turbulence intensity coefficient;

[0169] The convection coefficient (a parameter measuring convective heat transfer efficiency) is positively correlated with turbulence intensity, but its growth is not linear (it increases slowly in low turbulence and rapidly in high turbulence). In the quench zone of the kiln: when the turbulence intensity coefficient is 0.1 (low turbulence), the turbulence intensity convection coefficient is 50 W / (m²). 2 When the coefficient increases to 0.2 (moderate turbulence), the convection coefficient rises to 120 W / (m³). 2 • K)(The increase exceeds 2 times due to more intense heat exchange caused by enhanced turbulence); when the turbulence intensity coefficient reaches 0.3 (high turbulence), the convection coefficient increases to 200 W / (m²). 2 •K)(The rate of increase slows down because the airflow has been fully mixed). This nonlinear correlation makes the convective heat transfer calculations more closely resemble actual airflow conditions.

[0170] Data from temperature monitoring points on the kiln structure are embedded in the heat conduction path;

[0171] A distributed computational grid for heat conduction flux is established, with the grid density increasing as the temperature gradient increases;

[0172] In this kiln structure, heat conduction is uneven along the thickness direction (e.g., the temperature is higher on the inside of the kiln wall and lower on the outside, with a large temperature gradient). The distributed computing grid is refined according to the temperature gradient: the inner 10cm of the kiln wall (temperature gradient 50℃ / cm) has a grid density of 1 calculation point per centimeter (fine grid) to accurately capture rapidly changing heat transfer; the outer 10cm (temperature gradient 5℃ / cm) has a grid density of 1 calculation point per 5 centimeters (coarse grid) to reduce redundant calculations. Through this combination of dense and sparse grids, the calculated heat flux (e.g., 1000kJ per square meter per hour on the inside, and only 200kJ on the outside) is more accurate.

[0173] The energy conservation error of the three heat transfer links is periodically checked, and the sensor calibration process is triggered when the error exceeds the limit.

[0174] Among them, the energy conservation error of the three heat transfer links (radiation, convection, and conduction) refers to the difference between "total input heat" and "heat loss due to radiation + convection + conduction + heat absorbed by materials". An error exceeding the limit means that the difference exceeds the allowable range.

[0175] For example, if the total heat input to the firing zone is 1000kW, and the calculated heat loss is 300kW and the material absorption is 650kW, the total balance is 950kW, with an error of 50kW (5%), exceeding the set threshold of 3% (30kW), it is considered out of limit. If the total heat input to the quench zone is 500kW, and the calculated heat loss is 100kW and the material absorption is 410kW, the total balance is 510kW, with an error of 10kW (2%), which is below the threshold of 3%, it is considered normal. Exceeding the limit triggers sensor calibration (e.g., checking for thermocouple drift) to ensure reliable heat calculation.

[0176] Based on the embodiments provided in this application, a material emissivity compensation factor that is dynamically adjusted with the kiln age is introduced into the radiative heat transfer link to improve the long-term accuracy of radiative heat flux calculation; the convection coefficient is correlated with a nonlinear function of the turbulence intensity coefficient to make the calculation of convective heat transfer more consistent with the actual state of the airflow inside the kiln; a distributed computing grid that is densified with the temperature gradient is established in the heat conduction link to improve the local calculation accuracy of conduction heat flux; and the overall reliability of heat transfer path calculation is ensured by periodically verifying the energy conservation error of the three heat transfer links and triggering sensor calibration, providing more accurate thermal baseline data for energy consumption analysis.

[0177] Furthermore, the method also includes:

[0178] When adjusting the air-fuel ratio corresponding to the optimization potential zone, the calorific value compensation parameter is mapped to the gas proportional valve control curve; the following safety constraints are set in the gas proportional valve control curve: the air-fuel ratio fluctuation range does not exceed ±5% of the rated value;

[0179] It should be noted that the air-fuel ratio is the volume ratio of air to fuel during combustion (e.g., 1m³). 3 Natural gas requires 10m³ for complete combustion 3 Air-fuel ratio (10:1) directly affects combustion efficiency (too high a ratio wastes air, too low a ratio results in incomplete combustion of fuel).

[0180] The gas proportional valve control curve represents the correspondence between the "target air-fuel ratio" and the "valve opening" under different operating conditions: for example, when the air-fuel ratio is set to 1.2, the valve opening is 30% (corresponding to a gas flow rate of 20m³). 3 / h); at an air-fuel ratio of 1.1, the opening degree is 35% (gas flow rate 25m³ / h). 3 / h). The curve translates the abstract air-fuel ratio target into actionable valve actions, ensuring that the burner supplies gas at the optimal ratio.

[0181] The rated value is the optimal air-fuel ratio baseline determined during the design phase. For example, for natural gas kilns (with stable combustion), the rated value is set at 1.1 (i.e., 10% excess air to ensure complete combustion). For mixed fuel (coal gas + biomass) kilns (with large combustion fluctuations), the rated value is set at 1.2 (20% excess air to avoid incomplete combustion). The safety constraint of "not exceeding the rated value ±5%" means that the air-fuel ratio of natural gas kilns should be between 1.045 and 1.155, and that of mixed fuels should be between 1.14 and 1.26, to prevent an imbalance that could lead to a surge in energy consumption.

[0182] When the fan speed corresponding to the optimization potential zone is adjusted, the change in wind speed is converted into a frequency command for the inverter.

[0183] In some embodiments, the change in wind speed (e.g., from 10 m / s to 12 m / s) is positively correlated with the fan speed, which is controlled by the inverter frequency (higher frequency means faster speed). The conversion logic is based on the fan characteristics:

[0184] For example, a 50Hz rated frequency for a quench fan corresponds to a wind speed of 15m / s. For every 1m / s increase in wind speed, the frequency needs to increase by 3.3Hz (50 / 15≈3.3). If the wind speed needs to increase from 10m / s to 12m / s (a change of +2m / s), the inverter frequency command increases from 33Hz (10×3.3) to 40Hz (12×3.3). Here, the command is a specific frequency value (e.g., 40Hz), sent to the inverter via an electrical signal to directly control the motor speed.

[0185] Based on the embodiments provided in this application, when the air-fuel ratio is adjusted corresponding to the optimization potential zone, the calorific value compensation parameter is mapped to the gas proportional valve control curve and the air-fuel ratio fluctuation constraint is set to ensure the safety and stability of combustion adjustment; when the corresponding fan speed is adjusted, the wind speed change is converted into the frequency command of the frequency converter to achieve accurate wind speed control; both adjustment methods are directly related to the characteristics of the optimization potential zone, enabling energy consumption optimization measures to be implemented more accurately and improving the operability of control.

[0186] Further analysis of the temperature fluctuations in the firing zone includes:

[0187] Three sets of redundant thermocouples are arranged at the gas curtain at the end of the firing zone;

[0188] Temperature signals are collected by thermocouples installed at the gas curtain at the end of the firing zone.

[0189] Noise filtering and drift compensation are performed on the acquired temperature signals;

[0190] Calculate the time difference between the initiation point of the rapid cooling disturbance and the temperature peak, that is, extract the time interval between the initiation time of the rapid cooling disturbance and the time when the temperature peak of the firing zone appears; divide the time difference by the firing period and convert it into a phase angle value;

[0191] Establish a joint decision matrix for phase lag and flue gas negative pressure recovery slope, including: when the phase lag is greater than 45 degrees and the flue gas negative pressure recovery slope is greater than 0.5 kPa per second, the cooling section resistance is determined to be abnormal; when the phase lag is less than 30 degrees but the flue gas negative pressure recovery slope is greater than 0.8 kPa per second, the flue gas system is determined to be blocked.

[0192] Link the diagnostic report to the kiln car location recorded by the spatiotemporal tag;

[0193] An early warning signal is triggered when the same diagnostic result is obtained at three consecutive kiln car locations.

[0194] The early warning signal is input into the energy transfer topology model to locate the anomaly source.

[0195] Based on the embodiments provided in this application, three sets of redundant thermocouples are arranged at the end of the firing zone and noise filtering and drift compensation are performed to improve the reliability of temperature data; by calculating the time difference between the rapid cooling disturbance and the temperature peak and converting it into a phase angle, the precise quantification of the phase lag of temperature fluctuation is achieved; a joint decision matrix of phase lag and flue gas negative pressure recovery slope is established, which can accurately distinguish different fault types such as abnormal resistance in the cooling section and blockage in the flue gas system; the diagnostic results are bound to the kiln car position and an early warning is triggered when there are continuous abnormalities. Combined with the energy transfer topology model, the source of the abnormality is located, which facilitates the rapid investigation of kiln operation problems and ensures the continuity of energy consumption optimization.

[0196] According to another aspect of the embodiments of this application, a system for optimizing, collecting, and analyzing operational energy consumption data of a kiln production line is also provided. For example... Figure 3 As shown, the system includes:

[0197] The spatiotemporal correlation module 301 is used to generate spatiotemporal tags through the kiln car displacement encoder and the pre-embedded RFID tag. Each spatiotemporal tag corresponds to the real-time location area of ​​the kiln car in the kiln.

[0198] The pressure fluctuation data acquisition module 302 is used to trigger data acquisition based on the kiln operating conditions and acquire pressure fluctuation data.

[0199] The residual oxygen correlation component extraction module 303 is used to extract residual oxygen correlation components related to combustion efficiency from pressure fluctuation data.

[0200] The air volume disturbance application module 304 is used to calculate the deviation between the current temperature distribution uniformity of the quench zone and the historical benchmark value when the kiln car enters the quench zone. If the deviation exceeds the deviation threshold, a pseudo-random air volume disturbance with the same frequency as the quench fan is applied.

[0201] The air volume redistribution module 305 is used to analyze the slope change of the flue gas negative pressure recovery curve and the phase lag of the temperature fluctuation in the firing zone during the disturbance. When the slope is greater than the recovery threshold and the phase lag exceeds the phase threshold, an air volume redistribution command is generated.

[0202] The calorific value compensation module 306 is used to output calorific value compensation parameters to the combustion controller when the correlation coefficient between the time-domain characteristics of the residual oxygen correlation component and the rate of change of residual oxygen concentration exceeds the relevant threshold.

[0203] In some embodiments of this application, the kiln body is constructed using lightweight refractory insulation materials, the kiln cars are surrounded by bricks, and the supporting positions are made of stacked bricks. The central part is filled with high-temperature ceramic wool or energy-saving sand, achieving a lightweight kiln and resulting in significant energy savings. The kiln cars are loaded with silicon carbide shelf supports; if silicon carbide combined frame kiln furniture is used, the energy-saving effect will be greatly improved. The kiln adopts a prefabricated structure, suitable for factory-based, modular manufacturing, which not only ensures manufacturing quality but also facilitates transportation and construction installation, shortening the on-site installation cycle. The firing process and the transportation of kiln cars inside and outside the kiln are automatically controlled, allowing for largely unmanned operation during production, except for product loading and unloading.

[0204] The kiln body is divided into a flue gas section, a firing section, and a cooling section, with a flat suspended ceiling structure throughout. The firing zone mainly consists of burners, a stirring air curtain, and a conversion air curtain. The front section of the firing zone only has lower-layer nozzles, with a stirring air hole opposite each nozzle above it. This creates a strong swirling airflow between the flame and the stirring air within the kiln, resulting in a more uniform temperature throughout the kiln. Furthermore, the numerous nozzles located close to the kiln head allow the temperature difference between the upper and lower sections of the preheating zone to be controlled within 50℃, providing a strong guarantee for shortening the firing cycle and achieving rapid firing. The rear section of the firing zone has two rows of burners, facilitating temperature adjustment within the kiln, with the temperature difference controlled within a range of 3±2℃.

[0205] Two vertical conversion air curtains are set up in the kiln area around 1050℃ in the firing zone. Cold air from outside the kiln is drawn in by the combustion fan and then injected into the kiln through small vertical holes in the side wall, so as to realize the atmosphere conversion from oxidizing flame to reducing flame in the kiln.

[0206] The kiln cooling zone consists of a rapid cooling section, a slow cooling section, and a tail cooling section. The rapid cooling section has two vertical air curtains and two rows of horizontal cooling holes on both sides of the kiln walls and the kiln roof, drawing in cold air from outside the kiln to rapidly cool the products according to process requirements. The slow cooling section only has a few exhaust hoods on the kiln roof and side walls to draw the hot air from the rapid cooling and tail cooling sections out of the kiln, allowing the products to cool slowly. The kiln tail is the tail cooling section, where cold air from outside the kiln is blown into the kiln by a tail cooling fan and a cooling hood, rapidly cooling the products before they exit the kiln; the product exit temperature can be below 70℃.

[0207] The flue gas inside the kiln flows through several sets of exhaust ports and branch flues at the top and bottom of the kiln head and then into the main exhaust pipe.

[0208] The combustion system consists of two parts: natural gas and combustion air pipelines. Due to the many advantages of natural gas, such as complete combustion and high product quality, it is the ideal fuel for manufacturers.

[0209] The firing temperature is controlled proportionally, with a more sophisticated control unit than conventional control (each unit in the lower layer controls 4-5 burners, and each unit in the upper layer controls 2-3 burners). The gas / combustion air flow is proportionally adjusted to ensure that the kiln temperature meets the requirements of the firing curve process. Specifically, the system consists of thermocouples installed on the side wall or kiln top to collect signals, which are transmitted to a microcomputer intelligent PID digital display instrument in the instrument room via compensating wires. The instrument calculates the signals and drives the actuator to operate forward / reverse, changing the opening of the combustion air valve to control the flow of the combustion air. Pressure is then sampled from the combustion air branch pipe and fed back to the gas proportional valve to achieve proportional pressure control.

[0210] The combustion system is the core component of the entire kiln. It not only directly affects the quality of the kiln firing, but natural gas is also a dangerous gas that is low-flammable and highly explosive. Therefore, the reliability and safety of the gas components are also very important.

[0211] The cooling system includes quench air ducts and cooling air ducts. Cold air from outside the kiln is directly injected into the kiln through small holes on both sides of the kiln wall via quench air blowers and quench air ducts, so that the products can be rapidly cooled from above 1300℃ to 700℃.

[0212] The kiln wall air curtain duct is made of silicon carbide pipe with many small holes drilled in it. This allows for the powerful and uniform blowing of cold air into the kiln, effectively blocking the flow of flue gas and achieving energy saving, consumption reduction, and atmosphere conversion.

[0213] The heat and air volume of the kiln cooling zone are collected by the waste heat hood and waste heat duct on the kiln top, and then evenly extracted outside the kiln by the boiler induced draft fan. In order to ensure uniform heat extraction and reduce the impact of heat extraction from the kiln cooling zone on the temperature and atmosphere of the firing zone, this kiln has a large number of heat extraction holes.

[0214] It should be noted that the embodiments implemented on the side of the kiln production line operation energy consumption data optimization acquisition and analysis system can be referenced with the embodiments implemented on the side of the kiln production line operation energy consumption data optimization acquisition and analysis method, and will not be described in detail here.

[0215] According to another aspect of the embodiments of this application, an electronic device for implementing the above method is also provided, the electronic device being... Figure 4 The terminal device or server shown. This embodiment uses this electronic device as an example of a server. Figure 4 As shown, the electronic device includes a memory 402, a processor 404, and a transmission device 406. The memory 402 stores a computer program, and the processor 404 is configured to execute the steps in any of the above method embodiments through the computer program.

[0216] Optionally, in this embodiment, the aforementioned electronic device may be located in at least one of a plurality of network devices in a computer network.

[0217] Optionally, the transmission device 406 is used to receive or send data via a network. Specific examples of the network described above may include wired and wireless networks. In one example, the transmission device 406 includes a Network Interface Controller (NIC), which can be connected to other network devices and a router via a network cable to communicate with the Internet or a local area network. In another example, the transmission device 406 is a radio frequency (RF) module used to communicate with the Internet wirelessly.

[0218] In addition, the aforementioned electronic device also includes: a display 408 for displaying target identification characters contained in the identity identifier of the identified target object; and a connection bus 410 for connecting various module components in the aforementioned electronic device.

[0219] The above are merely preferred embodiments of the present invention and do not limit the scope of the patent. Any equivalent structural or procedural transformations made based on the description and drawings of the present invention, or direct or indirect applications in other related technical fields, are similarly included within the scope of patent protection of the present invention.

Claims

1. A method for optimizing the collection and analysis of energy consumption data during the operation of a kiln production line, characterized in that, include: Spatiotemporal tags are generated by kiln car displacement encoders and pre-embedded RFID tags. Each spatiotemporal tag corresponds to the real-time location area of ​​the kiln car in the kiln. Data acquisition and pressure fluctuation data are obtained based on kiln operating conditions; Extract the residual oxygen component related to combustion efficiency from the pressure fluctuation data; When the kiln car enters the quench zone, the deviation between the current temperature distribution uniformity of the quench zone and the historical benchmark value is calculated. If the deviation exceeds the deviation threshold, a pseudo-random air volume disturbance with the same frequency as the quench fan is applied. The slope of the flue gas negative pressure recovery curve and the phase lag of the temperature fluctuation in the firing zone are analyzed during the disturbance. When the slope is greater than the recovery threshold and the phase lag exceeds the phase threshold, an air volume redistribution command is generated. When the correlation coefficient between the time-domain characteristics of the residual oxygen correlation component and the rate of change of residual oxygen concentration exceeds the relevant threshold, the calorific value compensation parameter is output to the combustion controller. The process of acquiring pressure fluctuation data based on kiln operating conditions and extracting residual oxygen-related components associated with combustion efficiency from the pressure fluctuation data includes: The real-time data collected by distributed thermocouples, pressure sensors and residual oxygen meter are correlated with the kiln car location area identified by the current spatiotemporal tag. The primary trigger condition is activated when the rate of change in residual oxygen concentration exceeds the first threshold. After activating the primary triggering condition, when the pressure spectrum of the kiln car location area changes more than the second threshold in the preset frequency band, and the infrared detection device detects that the seal of the kiln car location area has failed, a multi-modal acquisition strategy is dynamically configured based on the sealing status and pressure spectrum characteristics of the kiln car location area. The multimodal acquisition strategy is executed to obtain the pressure fluctuation data; Leakage parameters are calculated based on the geometric features of seal failure determined by infrared detection, and the seal-related pressure components are calculated using the leakage parameters. Subtract the sealing-related pressure component from the pressure fluctuation data to obtain the residual pressure component; The frequency band with the strongest time-domain correlation to the residual oxygen concentration change rate in the residual pressure component is extracted as the residual oxygen correlation component.

2. The method for optimizing the collection and analysis of operating energy consumption data for a kiln production line according to claim 1, characterized in that, The dynamic configuration of the multi-modal acquisition strategy based on the sealing state and pressure spectrum characteristics of the kiln car location area includes: Construct an energy transfer topology model for the kiln heat exchange unit; The energy transfer topology model includes the following heat transfer paths: radiative heat transfer link between the firing zone unit and the current kiln car location area; convective heat transfer link between the quench zone unit and the current kiln car location area; and heat conduction link between the kiln body fixed structure unit and the firing zone unit and the quench zone unit. The turbulence intensity coefficient is calculated based on the pressure spectrum characteristics of the kiln car location region. By loading the real-time temperature gradient and the turbulence intensity coefficient into the energy transfer topology model, the heat loss rate of the kiln car location region is calculated. When a deviation of the heat loss rate from the theoretical value is detected, the sensors of adjacent heat exchange units are activated for collaborative monitoring. Based on the type of seal failure and the deviation value of heat loss rate, the optimal combination of acquisition parameters is matched from the historical database. The optimal combination of acquisition parameters includes sampling rate, acquisition duration and sensor activation scheme.

3. The method for optimizing the collection and analysis of operating energy consumption data of a kiln production line according to claim 2, characterized in that, The execution of the multimodal acquisition strategy to obtain the pressure fluctuation data includes: The disturbance target is selected based on the main source of the heat loss rate deviation in the kiln car location area, including: if the deviation mainly comes from the radiative heat transfer link in the firing zone, the air-fuel ratio of the burner in the firing zone is disturbed; if the deviation mainly comes from the convective heat transfer link in the quench zone, the speed of the quench fan is disturbed; if the deviation mainly comes from the heat conduction link in the kiln body structure, the heating power of the kiln wall insulation system is disturbed. During the duration of the disturbance, pressure sensor data are recorded according to the sampling rate and acquisition duration included in the optimal acquisition parameter combination. A two-dimensional energy consumption response curve is constructed based on the recorded pressure sensor data, with the horizontal axis representing the change in disturbance parameters and the vertical axis representing the change in heat loss rate. The region in the two-dimensional energy consumption response curve where the heat loss rate continuously decreases with the increase of the disturbance parameter is identified as the potential area for optimization. The acquisition parameters are adjusted according to the characteristics of the optimization potential area, including: when the main frequency of pressure fluctuation exceeds the set frequency threshold, the sampling rate is increased to a first set value; when the rate of decrease of heat loss rate in the optimization potential area exceeds the set rate threshold, the acquisition time is extended to a second set value. Reacquire pressure fluctuation data using the adjusted acquisition parameters; The energy transfer topology model is updated based on the newly acquired pressure fluctuation data; the updated heat loss rate deviation is calculated. If the updated heat loss rate deviation does not exceed the set convergence threshold, output the pressure fluctuation data. If the updated heat loss rate deviation exceeds the set convergence threshold, the current collection parameter combination will be marked as invalid and a rematching process will be triggered.

4. The method for optimizing the collection and analysis of operating energy consumption data for a kiln production line according to claim 1, characterized in that, The application of a pseudo-random airflow disturbance at the same frequency as the quench fan includes: Set the basic air volume by adjusting the butterfly valve on the quench air duct; The frequency converter is controlled to superimpose random fluctuation components onto the base air volume to generate a disturbance sequence; Real-time monitoring of the pressure difference between the air curtain ducts on both sides of the kiln wall; The disturbance amplitude is automatically attenuated when the pressure difference exceeds the safety difference threshold. During the duration of the disturbance, the rate of change of the pressure gradient inside the kiln shall not exceed the set rate limit. Obtain the phase synchronization index between the disturbance waveform and the operating frequency of the quench fan; The perturbation sequence is reinitialized when the phase synchronization index falls below the set standard.

5. The method for optimizing the collection and analysis of operating energy consumption data of a kiln production line according to claim 1, characterized in that, The calculation of the geometric characteristics of the seal failure includes: The infrared detection device scans the sealing structure of the kiln car location area; Identify temperature anomaly distribution patterns and classify failure types, including: when the temperature is distributed in a strip pattern, it is classified as masonry crack failure; when the temperature is distributed in a patchy pattern, it is classified as sand seal loss failure. Measure the geometry of the failure area, including: extracting the length and depth of masonry cracks; extracting the area and boundary curvature of missing sand seals; The hydraulic model is selected based on the failure type, including: for masonry cracks, the slit flow model is used to calculate the leakage area; for missing sand seals, the orifice flow model is used to calculate the leakage area. Input the calculated leakage area into the fluid dynamics equation to calculate the dynamic leakage coefficient; A sealing component correction curve is generated based on the dynamic leakage coefficient and pressure gradient data.

6. The method for optimizing the collection and analysis of energy consumption data for kiln production lines according to claim 2, characterized in that, The calculation of the heat transfer path includes: Temperature distribution data of the sintered thermocouple array is loaded into the radiative heat transfer link; A material emissivity compensation factor is introduced when calculating the radiative heat flux, and the material emissivity compensation factor is dynamically adjusted according to the kiln age cycle; The real-time measurement value of the wind speed sensor in the quench zone is integrated into the convective heat transfer link. A nonlinear function relating the convection coefficient to the turbulence intensity coefficient; Data from kiln body structure temperature monitoring points are embedded in the heat conduction link; A distributed computational grid for heat conduction flux is established, with the grid density increasing as the temperature gradient increases; The energy conservation error of the three heat transfer links is periodically checked, and the sensor calibration process is triggered when the error exceeds the limit.

7. The method for optimizing the collection and analysis of operating energy consumption data of a kiln production line according to claim 3, characterized in that, The method further includes: When the air-fuel ratio corresponding to the optimization potential zone is adjusted, the calorific value compensation parameter is mapped to the gas proportional valve control curve; the following safety constraint is set in the gas proportional valve control curve: the air-fuel ratio fluctuation range does not exceed ±5% of the rated value; When the fan speed is adjusted corresponding to the optimization potential zone, the change in wind speed is converted into a frequency inverter command.

8. The method for optimizing the collection and analysis of operating energy consumption data of a kiln production line according to claim 2, characterized in that, The analysis of temperature fluctuations in the firing zone includes: Three sets of redundant thermocouples are arranged at the gas curtain at the end of the firing zone; Noise filtering and drift compensation are performed on the acquired temperature signals; Calculate the time difference between the onset of the rapid cooling disturbance and the temperature peak; divide the time difference by the firing period and convert it into a phase angle value; Establish a joint decision matrix for phase lag and flue gas negative pressure recovery slope, including: when the phase lag is greater than 45 degrees and the flue gas negative pressure recovery slope is greater than 0.5 kPa per second, the cooling section resistance is determined to be abnormal; when the phase lag is less than 30 degrees but the flue gas negative pressure recovery slope is greater than 0.8 kPa per second, the flue gas system is determined to be blocked. Link the diagnostic report to the kiln car location recorded by the spatiotemporal tag; An early warning signal is triggered when the same diagnostic result is obtained at three consecutive kiln car locations. The warning signal is input into the energy transfer topology model to locate the anomaly source.

9. A system for optimizing and analyzing energy consumption data of a kiln production line, characterized in that, include: The spatiotemporal correlation module is used to generate spatiotemporal tags by using the kiln car displacement encoder and the pre-embedded RFID tags. Each spatiotemporal tag corresponds to the real-time location area of ​​the kiln car in the kiln. The pressure fluctuation data acquisition module is used to trigger data acquisition based on the kiln operating conditions and acquire pressure fluctuation data. The residual oxygen correlation component extraction module is used to extract residual oxygen correlation components related to combustion efficiency from the pressure fluctuation data. The air volume disturbance application module is used to calculate the deviation between the current temperature distribution uniformity of the quench zone and the historical benchmark value when the kiln car enters the quench zone. If the deviation exceeds the deviation threshold, a pseudo-random air volume disturbance with the same frequency as the quench fan is applied. The air volume redistribution module is used to analyze the slope change of the flue gas negative pressure recovery curve and the phase lag of the temperature fluctuation in the firing zone during the disturbance. When the slope is greater than the recovery threshold and the phase lag exceeds the phase threshold, an air volume redistribution command is generated. The calorific value compensation module is used to output calorific value compensation parameters to the combustion controller when the correlation coefficient between the time-domain characteristics of the residual oxygen correlation component and the rate of change of residual oxygen concentration exceeds a relevant threshold. The process of acquiring pressure fluctuation data based on kiln operating conditions and extracting residual oxygen-related components associated with combustion efficiency from the pressure fluctuation data includes: The real-time data collected by distributed thermocouples, pressure sensors and residual oxygen meter are correlated with the kiln car location area identified by the current spatiotemporal tag. The primary trigger condition is activated when the rate of change in residual oxygen concentration exceeds the first threshold. After activating the primary triggering condition, when the pressure spectrum of the kiln car location area changes more than the second threshold in the preset frequency band, and the infrared detection device detects that the seal of the kiln car location area has failed, a multi-modal acquisition strategy is dynamically configured based on the sealing status and pressure spectrum characteristics of the kiln car location area. The multimodal acquisition strategy is executed to obtain the pressure fluctuation data; Leakage parameters are calculated based on the geometric features of seal failure determined by infrared detection, and the seal-related pressure components are calculated using the leakage parameters. Subtract the sealing-related pressure component from the pressure fluctuation data to obtain the residual pressure component; The frequency band with the strongest time-domain correlation to the residual oxygen concentration change rate in the residual pressure component is extracted as the residual oxygen correlation component.

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