A porcelain insulator carbon footprint full life cycle calculation management system and method

Abstract

The present application relates to product life cycle management technical field, specifically to a kind of porcelain insulator carbon footprint whole life cycle calculation management system and method, system includes: batch data anchoring module matches hysteresis constant and constructs batch energy consumption record, endogenous carbon quantification module calculates flue gas oxygen concentration deviation and constructs carbon emission quantification data, carbon emission total amount integration module summation constructs dynamic carbon emission total amount, noise weight calculation module combines material difference and cumulative number and constructs measurement weight, account adaptive filtering module calculates gain and establishes management account, in the present application, by matching electric heating physical delay, each batch energy consumption traceability benchmark is built, the limitation of total amount is broken, and based on the change of flue gas, the internal carbon conversion of material is analyzed, the deviation of fixed value estimation is effectively avoided, and the measurement disturbance reliability is determined by comprehensively considering product list replacement and historical production capacity, to adaptively weighted smoothing correction priori benchmark, drive carbon management account to accurately fit actual production process.

Description

Technical Field

[0001] This invention relates to the field of product lifecycle management technology, and in particular to a carbon footprint full lifecycle calculation and management system and method for porcelain insulators. Background Technology

[0002] Product lifecycle management technology mainly refers to the business process system for managing the entire product lifecycle. This entire process covers product demand, planning, design, production, distribution, operation and use, and even recycling and disposal. Its core aspects include bill of materials recording and engineering change management, as well as the tracking and summarization of material and energy flows across production stages. Overall, this technology relies on hardware equipment to extract underlying physical data from the design, manufacturing, and sales stages, and connects the data to establish a unified business ledger across the product lifecycle. The traditional carbon footprint lifecycle calculation management system and method for porcelain insulators refers to the workflow for compiling and statistically analyzing greenhouse gas emissions. This workflow targets various physical stages of porcelain insulator production, including raw material extraction, product manufacturing, logistics, and disposal. Traditionally, accounting personnel conduct regular manual inspections, recording readings from water, electricity, and natural gas meters in the production workshop. Simultaneously, they manually collect purchase and delivery documents for raw materials such as high-alumina porcelain clay, clay, and feldspar to obtain the actual physical consumption of each material. Subsequently, personnel process the physical consumption data in spreadsheets, multiplying each collected physical consumption by the corresponding carbon emission factor value published by the government. Finally, they accumulate the products item by item using direct arithmetic addition to obtain the total carbon dioxide equivalent value of this type of product at a specific lifecycle stage.

[0003] Traditional accounting methods rely on personnel to inspect and record on-site instrument readings and manually collect raw material storage vouchers to extract physical consumption. The consumption is then directly multiplied and added with the publicly available static emission factor to obtain the equivalent total value. This model, which relies solely on manual post-processing, results in time lags and input errors in data collection. The business flow based on total allocation makes it difficult to achieve precise traceability of energy consumption for each batch of production. At the same time, the static mapping formula masks the actual physical fluctuations of endogenous carbon released by material decomposition during the high-temperature firing stage, making it difficult for the final ledger to reflect transient carbon emission disturbances caused by production anomalies and process changes. Summary of the Invention

[0004] To address the technical problems existing in the prior art, embodiments of the present invention provide a carbon footprint full life cycle calculation and management system and method for porcelain insulators.

[0005] On the one hand, a full life-cycle calculation and management system for the carbon footprint of porcelain insulators is provided, the system including: The batch data anchoring module collects temperature sensor data arrays and voltage sag timestamps within the ceramic firing temperature zone cycle, calculates the first derivative of the temperature sensor data array to extract the abrupt temperature loss timestamp, calculates the absolute deviation between the two types of timestamps and matches the heat transfer hysteresis constant, associates the insulator batch code and interval energy consumption data, and constructs a batch energy consumption association record. The endogenous carbon quantification module collects the oxygen concentration sequence of kiln flue gas, calculates the deviation between the first derivative of oxygen concentration over time and the theoretical oxygen demand rate for complete fuel combustion, obtains the excess oxygen consumption rate difference and converts it into the transient material carbon release rate, and performs time-domain integration to construct endogenous carbon emission quantification data. The total carbon emission integration module extracts the electrical energy value and preset energy conversion factor from the batch energy consumption correlation record, calculates the product of the two and the sum of the endogenous carbon emission quantification data, and constructs the measured dynamic total carbon emission. The noise weight calculation module determines the material composition change score based on the product of the bill of materials version difference item and the unit weight, determines the cumulative number of productions by combining the total number of historical production record items, calculates the ratio of the material composition change score to the cumulative number of productions, and constructs a dynamic measurement noise weight. The adaptive filtering module of the ledger obtains the composite noise component based on the sum of the theoretical static noise weight and the dynamic measured noise weight. It calculates the ratio of the theoretical static noise weight to the composite noise component to obtain the adaptive fusion gain factor. The gain factor is multiplied by the difference between the measured dynamic carbon emissions and the prior estimated total carbon emissions and then superimposed to the prior estimated total carbon emissions to establish a balanced carbon management ledger value.

[0006] As a further aspect of the present invention, the batch energy consumption association record specifically includes insulator batch code, interval energy consumption value, and equipment mapping pointer; the endogenous carbon emission quantification data includes transient material carbon release rate, cumulative carbon emission flux, and total reaction release; the measured dynamic carbon emission total specifically refers to basic carbon emission value, energy consumption conversion parameter, and multi-source integrated data; the dynamic measurement noise weight includes material composition variation score, cumulative production number, and observation noise component; and the carbon management ledger value specifically refers to feedback correction amount, prior expected correction value, and ledger output item.

[0007] As a further aspect of the present invention, the batch data anchoring module includes: The temperature loss timestamp extraction submodule acquires the temperature sensor value array within a preset period when the ceramic part enters the firing temperature zone, calculates the rate of change of the temperature sensor value array along the time dimension to obtain the first derivative, locates the minimum value position in the first derivative, and extracts the timestamp corresponding to the minimum value position to obtain the abrupt temperature loss timestamp. The time consistency determination submodule monitors the voltage sag timestamp collected by the power supply line, performs algebraic deviation calculation on the voltage sag timestamp and the rapid temperature loss timestamp to obtain the algebraic deviation value, performs absolute value calculation on the algebraic deviation value to obtain the absolute time difference value, retrieves the preset heat transfer hysteresis constant, determines the consistency between the absolute time difference value and the value of the heat transfer hysteresis constant, and obtains the consistency determination result. The batch energy consumption association submodule obtains the insulator batch code and interval energy consumption value according to the batch corresponding to the consistency judgment result, performs an association binding operation on the insulator batch code and interval energy consumption value, and constructs a batch energy consumption association record.

[0008] As a further aspect of the present invention, the process of performing an absolute value operation on the algebraic deviation value to obtain an absolute time difference value, retrieving a preset heat transfer hysteresis constant, and determining the consistency between the absolute time difference value and the heat transfer hysteresis constant value specifically involves: A numerical modulus extraction operation is performed on the algebraic deviation value to obtain the absolute time difference value characterizing the electrothermal induction time difference; Access the stored records to retrieve the thermodynamic response benchmark pre-calibrated based on the kiln wall thickness, the thermal conductivity of the kiln material, and the sensor installation depth, and determine it as the heat transfer hysteresis constant; The absolute value of the algebraic difference between the absolute time difference and the heat transfer hysteresis constant is calculated to obtain the offset value. The time-domain tolerance band established based on the sensor sampling frequency is retrieved as the deviation tolerance limit. The relationship between the offset value and the deviation tolerance limit is determined. If the offset value is within the deviation tolerance limit range, it is determined that the value is consistent, and the consistency determination result is established.

[0009] As a further aspect of the present invention, the endogenous carbon quantification module includes: The oxygen concentration derivative submodule acquires a continuous oxygen concentration numerical sequence during the operation of the kiln flue gas pipeline network, and performs a first derivative operation on the continuous oxygen concentration numerical sequence along the time dimension to obtain the oxygen consumption rate variable. The excess consumption conversion submodule calls the oxygen consumption rate variable, retrieves the theoretical oxygen demand rate for complete fuel combustion, performs a numerical deviation calculation on the oxygen consumption rate variable and the theoretical oxygen demand rate for complete fuel combustion to obtain the excess oxygen consumption rate difference, retrieves the carbon oxidation chemical reaction equivalent constant, and performs a product operation on the excess oxygen consumption rate difference and the carbon oxidation chemical reaction equivalent constant to obtain the transient material carbon release rate. The time-domain integral summarization submodule calls the transient material carbon release rate and performs integral calculations along the time dimension for the transient material carbon release rate to construct endogenous carbon emission quantification data.

[0010] As a further aspect of the present invention, the total carbon emission integration module includes: The energy value extraction submodule calls the batch energy consumption association record, extracts the energy consumption field corresponding to each batch from the batch energy consumption association record, and obtains the energy consumption value of the interval. The basic carbon emission calculation submodule calls the range of electrical energy consumption values, retrieves the preset energy conversion factor, and performs a product operation on the range of electrical energy consumption values ​​and the energy conversion factor to obtain the basic carbon emission values. The total carbon emissions summary submodule calls the basic carbon emissions value and the endogenous carbon emissions quantification data, performs an addition and summation operation on the basic carbon emissions value and the endogenous carbon emissions quantification data, and constructs the measured dynamic total carbon emissions.

[0011] As a further aspect of the present invention, the noise weight calculation module includes: The anomaly score generation submodule accesses the product bill of materials, extracts the current bill of materials version and the previous bill of materials version, performs a component comparison between the current bill of materials version and the previous bill of materials version to obtain component difference items, retrieves the preset unit influence weight, performs a product operation on the change in mass percentage of each component difference item and the corresponding unit influence weight to obtain the individual anomaly component, and performs an addition operation on all individual anomaly components to obtain the material component anomaly score; The cumulative count statistics submodule accesses historical production records, calls the current bill of materials version, performs version filtering operations on historical production records, counts the total number of execution entries corresponding to the current bill of materials version in historical production records, and obtains the cumulative production count. The weight ratio calculation submodule calls the material composition change score and the cumulative number of productions, performs a division ratio calculation on the material composition change score and the cumulative number of productions, and constructs a dynamic measurement noise weight.

[0012] As a further aspect of the present invention, the process of comparing the components between the current bill of materials version and the previous bill of materials version to obtain the component differences is specifically as follows: Extract the mass percentage parameters of each raw material component from the current bill of materials version and the previous bill of materials version; Calculate the change in the mass percentage of the same raw material component between the two versions, and identify heterogeneous raw material components unique to the current bill of materials version; The raw material components whose mass percentage change is not zero and the heterogeneous raw material components are marked as the component difference items; The process of obtaining the unit influence weight is as follows: Access the material carbon release characteristics table established based on the determination of component chemical bond carbon content, and retrieve the carbonate percentage, organic binder ratio and thermal decomposition weight loss rate of each component in the component difference item; Using the mass ratio of carbon elements in the molecular structure of each component as a weight, a weighted summation operation is performed on the corresponding carbonate percentage, organic binder ratio, and thermal decomposition weight loss rate of each component to generate the theoretical carbon emission intensity change value corresponding to the unit mass change of the monomer material, and this value is determined as the unit influence weight.

[0013] As a further aspect of the present invention, the ledger adaptive filtering module includes: The gain factor generation submodule obtains the theoretical static noise weight, performs an addition operation on the theoretical static noise weight and the dynamic measurement noise weight to obtain the composite noise component, and performs a division ratio operation on the theoretical static noise weight and the composite noise component to obtain the adaptive fusion gain factor. The carbon flow residual calculation submodule obtains the prior estimated total carbon emissions, calls the measured total dynamic carbon emissions, and performs algebraic deviation calculation on the measured total dynamic carbon emissions and the prior estimated total carbon emissions to obtain the carbon flow residual term. The feedback correction superposition submodule calls the carbon flow residual term and the adaptive fusion gain factor, performs a product operation on the carbon flow residual term and the adaptive fusion gain factor to obtain the feedback correction amount, calls the prior estimated total carbon emission value, performs an addition and summation operation on the feedback correction amount and the prior estimated total carbon emission value, and establishes a balanced carbon management ledger value. As a further aspect of the present invention, the process of obtaining the theoretical static noise weights specifically includes: The deviation distribution sequence between the prior estimated total carbon emissions and the measured total dynamic carbon emissions under historical stable operating conditions is retrieved. Variance calculation is performed on the deviation distribution sequence to quantify the inherent uncertainty of the model and obtain the theoretical static noise weight. The process of obtaining the prior estimated total carbon emissions is as follows: Extract the mass ratio of carbonates and organic binders of each component in the current bill of materials version, call the stoichiometric conversion coefficient based on the molecular weight of the components, perform a weighted summation operation on the mass ratio of each component and the corresponding stoichiometric conversion coefficient to obtain the carbon emission intensity per unit mass of raw materials, retrieve the single raw material feed mass and planned output of the current batch, and perform a product operation on the carbon emission intensity per unit mass of raw materials, the single raw material feed mass and the planned output to obtain the carbon emission baseline value of the raw materials of the current batch under standard reaction conditions; Obtain the unit product energy consumption limit determined based on industry standards, calculate the product of the unit product energy consumption limit, planned output and the preset energy conversion factor to obtain the energy carbon emission benchmark value; The sum of the raw material carbon emission baseline value and the energy carbon emission baseline value is calculated to establish the prior estimated total carbon emission value.

[0014] On the other hand, a method for calculating and managing the carbon footprint of porcelain insulators throughout their entire lifecycle, wherein the method is executed based on the aforementioned carbon footprint calculation and management system for porcelain insulators, includes the following steps: S1: During the firing temperature zone of the ceramic parts, collect temperature sensor data array and voltage sag timestamp, calculate the first derivative of temperature sensor data array to extract the abrupt temperature loss timestamp, calculate the absolute deviation of the two types of timestamps and match the heat transfer hysteresis constant, associate the insulator batch code and interval energy consumption data, and construct a batch energy consumption association record. S2: Collect the oxygen concentration sequence of the kiln flue gas, calculate the deviation between the first derivative of the oxygen concentration over time and the theoretical oxygen demand rate for complete combustion of fuel, obtain the excess oxygen consumption rate difference and convert it into the transient material carbon release rate, and perform time-domain integration to construct the endogenous carbon emission quantification data. S3: Extract the electrical energy value and preset energy conversion factor from the batch energy consumption correlation record, calculate the sum of the product of the two and the endogenous carbon emission quantification data, and construct the measured dynamic total carbon emission. S4: Determine the material composition change score based on the product of the bill of materials version difference item and the unit weight, determine the cumulative number of productions by combining the total number of historical production record items, calculate the ratio of the material composition change score to the cumulative number of productions, and construct a dynamic measurement noise weight. S5: Based on the sum of the theoretical static noise weight and the dynamic measured noise weight, the composite noise component is obtained. The ratio of the theoretical static noise weight to the composite noise component is calculated to obtain the adaptive fusion gain factor. The gain factor is multiplied by the difference between the measured dynamic carbon emissions and the prior estimated total carbon emissions and then superimposed to the prior estimated total carbon emissions to establish a balanced carbon management ledger value.

[0015] The beneficial effects of the technical solutions provided by the embodiments of the present invention include at least the following: By capturing the extreme time points of kiln temperature differences and matching the physical delay of voltage mutations, a coupled benchmark including physical markers and synchronous interval power consumption is constructed. This effectively breaks the limitations of fuzzy total classification to meet the precise power consumption traceability of each manufacturing cycle. Simultaneously, the actual oxygen dissipation rate and the transient deviation of pure fuel oxygen consumption in the kiln are analyzed and time-domain integration is introduced to quantitatively reconstruct the internal carbon conversion of raw and auxiliary materials induced by high temperature. This avoids the risk of missing components in static coefficient estimation. Furthermore, dynamic perturbation observation reliability is established by comprehensively considering the product list change difference and historical statistics of production maturity. An adaptive weighting mechanism is used to apply smooth correction to the prior carbon emission prediction benchmark, driving the global carbon footprint ledger to present a dynamic correction capability that fits the physical truth. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a schematic diagram of a carbon footprint full life cycle calculation and management system for porcelain insulators provided in an embodiment of the present invention; Figure 2 This is a schematic diagram of the system framework of the present invention; Figure 3 This is a flowchart of the batch data anchoring module in this invention; Figure 4 This is a flowchart of the endogenous carbon quantification module in this invention; Figure 5 This is a flowchart of the total carbon emissions integration module in this invention; Figure 6 This is a flowchart of the noise weight calculation module in this invention; Figure 7 This is a flowchart of the ledger adaptive filtering module in this invention; Figure 8 This is a flowchart of a method for calculating and managing the carbon footprint of porcelain insulators throughout their entire life cycle, provided by an embodiment of the present invention. Detailed Implementation

[0018] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0019] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0020] This invention provides a carbon footprint full lifecycle calculation and management system for porcelain insulators, such as... Figures 1-2 The diagram shown illustrates the lifecycle calculation and management system for the carbon footprint of porcelain insulators. This system includes: The batch data anchoring module collects temperature sensor data arrays and voltage sag timestamps within the ceramic firing temperature zone cycle, calculates the first derivative of the temperature sensor data array to extract the abrupt temperature loss timestamp, calculates the absolute deviation between the two types of timestamps and matches the heat transfer hysteresis constant, associates the insulator batch code and interval energy consumption data, and constructs a batch energy consumption association record. The endogenous carbon quantification module collects the oxygen concentration sequence of kiln flue gas, calculates the deviation between the first derivative of oxygen concentration over time and the theoretical oxygen demand rate for complete fuel combustion, obtains the excess oxygen consumption rate difference and converts it into the transient material carbon release rate, and performs time-domain integration to construct endogenous carbon emission quantification data. The total carbon emission integration module extracts the electrical energy values ​​and preset energy conversion factors from the batch energy consumption correlation records, calculates the product of the two and the sum of the endogenous carbon emission quantification data, and constructs the measured dynamic total carbon emission. The noise weight calculation module determines the material composition change score based on the product of the bill of materials version difference item and the unit weight, determines the cumulative number of productions by combining the total number of historical production record items, calculates the ratio of the material composition change score to the cumulative number of productions, and constructs a dynamic measurement noise weight. The adaptive filtering module of the ledger obtains the composite noise component based on the sum of the theoretical static noise weight and the dynamic measurement noise weight. It calculates the ratio of the theoretical static noise weight to the composite noise component to obtain the adaptive fusion gain factor. The gain factor is multiplied by the difference between the measured dynamic carbon emissions and the prior estimated total carbon emissions and then superimposed to the prior estimated total carbon emissions to establish a balanced carbon management ledger value.

[0021] The batch energy consumption correlation record specifically includes the insulator batch code, the interval energy consumption value, and the equipment mapping pointer. The intrinsic carbon emission quantification data includes the transient material carbon release rate, the cumulative carbon emission flux, and the total reaction release. The measured dynamic carbon emission total specifically refers to the basic carbon emission value, energy consumption conversion parameters, and multi-source integrated data. The dynamic measurement noise weight includes the material composition change score, the cumulative number of productions, and the observation noise component. The carbon management ledger value adjustment specifically refers to the feedback correction amount, the prior expected correction value, and the ledger output items.

[0022] Specifically, such as Figure 2 , 3 As shown, the batch data anchoring module includes: The temperature loss timestamp extraction submodule acquires the temperature sensor value array within a preset period when the ceramic part enters the firing temperature zone, calculates the rate of change of the temperature sensor value array along the time dimension to obtain the first derivative, locates the minimum value position in the first derivative, and extracts the timestamp corresponding to the minimum value position to obtain the abrupt temperature loss timestamp. The system connects in real-time via an industrial fieldbus to 12 sets of platinum-rhodium thermocouple sensors arranged in the kiln's firing zone, periodically acquiring temperature sensor data arrays at a sampling frequency of 2Hz. The data preprocessing component performs 5-point moving average noise reduction on the acquired raw temperature sequence, and then calls a logic calculator to perform a time-dimensional rate of change calculation on the processed temperature sensor data array. Specifically, the calculator extracts the difference between the current temperature value and the previous sampling temperature value and divides it by a 0.5s time interval to generate a first-order derivative sequence reflecting the thermodynamic changes inside the kiln. When the kiln experiences a malfunction in the air supply equipment or an unexpected burner shutdown, the temperature exhibits an abnormally rapid downward trend, at which point a significant negative pulse appears in the first-order derivative sequence. The extreme value retrieval operator searches for the minimum position of this sequence within the 1250℃ to 1380℃ range corresponding to the firing temperature zone. For example, if a sudden change in the derivative value from -0.05℃ / s to -8.2℃ / s is detected, it is determined to be the inflection point of rapid temperature loss. The unit then extracts the precise time information corresponding to the location of the minimum value at the industrial time service center, as a time stamp of abrupt temperature loss (e.g., 14:30:12.700), and stores it in a cache queue as a key time criterion for anchoring energy consumption in subsequent batches.

[0023] The time consistency determination submodule monitors the voltage sag timestamp collected by the power supply line, performs algebraic deviation calculation on the voltage sag timestamp and the rapid temperature loss timestamp to obtain the algebraic deviation value, performs absolute value calculation on the algebraic deviation value to obtain the absolute time difference value, retrieves the preset heat transfer hysteresis constant, determines the consistency between the absolute time difference value and the value of the heat transfer hysteresis constant, and obtains the consistency determination result. The bus voltage on the factory's power distribution side is continuously monitored via a high-frequency sampling line. When the voltage drops below 85% of the rated value and lasts for more than 10 ms, the voltage sag timestamp at the power distribution end is automatically collected and recorded. The logic unit performs an algebraic deviation calculation on the voltage sag timestamp (e.g., 14:30:05.200) and the extracted abrupt temperature drop timestamp to determine their physical distance on the time axis. Since voltage disturbances in the power grid can cause a decrease in fan speed or a frequency converter reset, leading to a rapid temperature drop inside the kiln, there is an inherent time difference between the two types of timestamps due to the physical lag in heat transfer.

[0024] Table 1 Correspondence between kiln physical parameters and heat transfer hysteresis constant 350 0.55 150 5.8 450 0.62 200 7.5 550 0.68 250 9.2 As shown in Table 1, the decision-making component, based on the current kiln wall thickness of 450 mm and sensor installation depth of 200 mm, retrieves a pre-calibrated heat transfer hysteresis constant of 7.5 s from the stored records. The decision-making component calculates the absolute value of the algebraic difference between the absolute time difference and this constant of 7.5 s, obtaining an offset value of 0.0 s. This offset value is within the 1.5 s time-domain tolerance band established based on the sensor sampling frequency. Therefore, it is determined that the two events have a strong thermodynamic mapping correlation, confirming that they meet the numerical consistency requirement, and generating a consistency determination result of "true".

[0025] The batch energy consumption association submodule obtains the insulator batch code and interval energy consumption value according to the batch corresponding to the consistency judgment result, performs association binding operation on the insulator batch code and interval energy consumption value, and constructs batch energy consumption association record; Upon receiving the numerical consistency determination result, the data traceability engine is activated. It accesses the manufacturing execution platform to obtain the insulator batch code (e.g., BATCH-2026-N10) currently located at the center of the firing zone. The associated component synchronously calls the real-time power consumption data stream provided by the energy gateway. Based on the time window corresponding to the consistency determination result, it extracts the energy consumption value of this batch from entering the firing temperature zone to the moment of disturbance (e.g., 1240.5 kWh). The binder within the associated component performs an atomic encapsulation operation on the insulator batch code, the energy consumption value, and the corresponding device mapping pointer. By establishing a unique hash index, it physically anchors the energy consumption data to the physical batch, thereby constructing a batch energy consumption association record. This record achieves precise batch-level splitting of energy consumption in the continuous production process, providing a reliable data foundation for subsequent calculations of the unit product carbon footprint of each batch of ceramic parts, effectively avoiding the insufficient accuracy caused by amortizing total energy consumption in conventional calculations.

[0026] Specifically, such as Figure 2 , 4 As shown, the endogenous carbon quantification module includes: The oxygen concentration derivative submodule acquires a continuous oxygen concentration numerical sequence during the operation of the kiln flue gas pipeline network, and performs a first derivative operation on the continuous oxygen concentration numerical sequence along the time dimension to obtain the oxygen consumption rate variable. During the operation of the kiln exhaust network, a continuous oxygen concentration sequence with 100ms intervals is acquired using a zirconia probe installed at the front end of the induced draft fan. The internal data processor first normalizes the raw oxygen percentage values, mapping the concentration from 0% to 21% to per-unit values ​​from 0 to 1. Next, the derivative component performs a first-order derivative calculation along the time dimension for this sequence, using a difference approximation method to calculate the variation in oxygen concentration within the sampling interval. This derivative calculation accurately captures the instantaneous decrease in flue gas oxygen content caused by the decomposition of organic matter or the oxidation of carbonates within the material. For example, if the measured oxygen concentration decreases from 8.50% to 8.20% within 1 second, the calculated oxygen consumption rate variable is 0.30% / s. This variable quantifies the intensity of oxygen consumption per unit time. By differentiating this dynamic indicator, the monitoring method is upgraded from static oxygen content reading to dynamic chemical reaction rate tracking, providing a crucial precursor parameter for subsequent quantitative analysis of carbon dioxide released from the oxidation of components within the ceramic parts.

[0027] The excess consumption conversion submodule calls the oxygen consumption rate variable, retrieves the theoretical oxygen demand rate for complete fuel combustion, performs a numerical deviation calculation on the oxygen consumption rate variable and the theoretical oxygen demand rate for complete fuel combustion to obtain the excess oxygen consumption rate difference, retrieves the carbon oxidation chemical reaction equivalent constant, and performs a product operation on the excess oxygen consumption rate difference and the carbon oxidation chemical reaction equivalent constant to obtain the transient material carbon release rate. First, the oxygen consumption rate variable obtained above is retrieved via the internal bus, and the theoretical oxygen demand rate for complete fuel combustion corresponding to the current fuel supply is retrieved from the configuration parameters of the combustion controller. This theoretical oxygen demand rate (e.g., 0.25% / s) represents the ideal oxygen consumption caused solely by fuel combustion. The conversion unit performs a numerical deviation calculation between the oxygen consumption rate variable and the theoretical oxygen demand rate for complete fuel combustion. If the measured oxygen consumption rate is greater than the theoretical value, the difference is the excess oxygen consumption rate difference, calculated to be 0.05% / s. Simultaneously, the conversion unit retrieves the real-time mass flow rate of the kiln exhaust network (e.g., 100 kg / s) and the preset carbon oxidation chemical reaction equivalent constant, which is determined to be 0.375 based on the mass conservation law of carbon dioxide produced by the reaction of carbon and oxygen. The conversion unit performs a product calculation on the excess oxygen consumption rate difference, the flue gas mass flow rate, and the equivalent constant, i.e., 0.05% × 100kg / s × 0.375, yielding a transient material carbon release rate of 0.01875kg / s. This result eliminates the interference of fuel combustion on carbon emission calculations and directly reflects the endogenous carbon emission dynamics of raw materials such as kaolin and feldspar in ceramic components at high temperatures.

[0028] The time-domain integral summarization submodule calls the transient material carbon release rate and performs integral calculations along the time dimension for the transient material carbon release rate to construct endogenous carbon emission quantification data. As the final step in endogenous carbon accounting, the transient material carbon release rate is invoked, and the integration and accumulation logic is initiated. Within the 45-minute (2700s) timeframe of the entire batch of ceramic parts passing through the high-temperature reaction zone, the integrator performs high-precision time-domain integration calculations on the transient material carbon release rate. Specifically, a discrete integration method is used, multiplying the transient rate value calculated at each sampling moment by the corresponding sampling step size of 0.1s and accumulating it in the total register. For example, a constant rate of 0.01875 kg / s is accumulated over 2700s. Finally, when the batch of ceramic parts leaves the reaction zone, the accumulated value output by the register is the endogenous carbon emission data, which is 50.625 kg (0.0506t). This data accurately reflects the emission increment caused by non-energy factors during the ceramic part production process. Through macroscopic integration of the microscopic chemical reaction process, the rigor of the carbon footprint life-cycle accounting at the materials science level is ensured.

[0029] Specifically, such as Figure 2 , 5 As shown, the total carbon emissions integration module includes: The energy consumption value extraction submodule calls the batch energy consumption association record, extracts the energy consumption field corresponding to each batch from the batch energy consumption association record, and obtains the energy consumption value of the interval. The database middleware calls the aforementioned batch energy consumption association records as the starting point for carbon emission total integration. The internal parsing engine performs key-value extraction on the structured fields in the records, accurately identifying the range of electrical energy consumption corresponding to each insulator batch code. During processing, the extraction component verifies the legality of the source of the electrical energy value based on the device mapping pointer carried in the record, eliminating auxiliary electrical consumption from non-firing sections. For example, for batch number BATCH-2026-N10, the extracted electrical energy consumption value is 1240.5 kWh. This process, through secondary deconstruction of the batch association data, transforms the scattered energy consumption records into standardized carbon emission accounting input metadata, achieving a smooth transition from data flow to carbon accounting logic.

[0030] The basic carbon emission calculation submodule calls the range of electrical energy consumption values, retrieves the preset energy conversion factor, and performs a product operation on the range of electrical energy consumption values ​​and the energy conversion factor to obtain the basic carbon emission values. The system retrieves the extracted interval energy consumption data and uses a preset energy conversion factor from the cloud-based carbon factor library. This factor is set based on the latest annual average emission factor of the power grid and is 0.5703 t / MWh (i.e., 0.0005703 t / kWh). The product operator performs floating-point multiplication on the interval energy consumption data and the energy conversion factor. Taking 1240.5 kWh as an example, substituting it into the calculation logic, i.e., 1240.5 multiplied by 0.0005703, yields a basic carbon emission value of approximately 0.7075 t for this batch. The calculation component also encapsulates the energy consumption conversion parameters, indicating the corresponding energy type, conversion basis, and validity period, ensuring the traceability of the basic carbon emission data. Through standardized quantitative mapping, this component converts direct energy consumption into a unified carbon dioxide equivalent, providing a dimensionally consistent calculation benchmark for subsequent multi-dimensional carbon emission aggregation.

[0031] The total carbon emissions summary submodule calls the basic carbon emissions value and the endogenous carbon emissions quantification data, performs an addition and summation operation on the basic carbon emissions value and the endogenous carbon emissions quantification data, and constructs the measured dynamic total carbon emissions. The system calls upon baseline carbon emission figures and quantified endogenous carbon emission data in real time, executing the final summation logic. The adder performs a summation operation on the baseline carbon emission figure (0.7075t) and the quantified endogenous carbon emission data (0.0506t) to construct the measured dynamic total carbon emission. This aggregation process integrates data from external energy sources and internal material sources. For example, the final aggregated measured dynamic total carbon emission is 0.7581t. The aggregation component adds a batch label to this result and outputs it to the management terminal. This comprehensive dynamic integration method logically stitches together previously isolated energy consumption monitoring and flue gas monitoring data at the batch level, effectively improving the accuracy of dynamic carbon footprint calculation and providing highly reliable measured evidence for subsequent asset verification.

[0032] Specifically, such as Figure 2 , 6 As shown, the noise weight calculation module includes: The anomaly score generation submodule accesses the product bill of materials, extracts the current bill of materials version and the previous bill of materials version, performs a component comparison between the current bill of materials version and the previous bill of materials version to obtain component difference items, retrieves the preset unit influence weight, performs a product operation on the change in mass percentage of each component difference item and the corresponding unit influence weight to obtain the individual anomaly component, and performs an addition operation on all individual anomaly components to obtain the material component anomaly score; When configuration is updated or materials are changed, the component automatically accesses the factory's product bill of materials (BOM) management database. It extracts the current batch's planned BOM version and compares it with the previous historical batch version, performing a component-by-component comparison. The comparison logic includes matching raw material component names, extracting mass percentage parameters, and identifying added or missing items. For example, when it detects that the kaolin proportion has increased from 30.0% to 32.0%, it determines that there is a difference of 2.0%. The anomaly generation component then retrieves a preset unit impact weight, which is pre-calibrated based on the theoretical decarburization rate of different raw materials under high-temperature conditions.

[0033] Table 2 Correspondence between Material Composition Variation and Unit Impact Weight Kaolin 2.0 0.45 0.90 Feldspar 1.5 0.22 0.33 Talc 0.5 0.68 0.34 As shown in Table 2, the anomaly generation component performs a product operation on the total change of each component difference item and its corresponding unit influence weight, and sums the results of all difference items (i.e., 0.90 plus 0.33 plus 0.34), finally obtaining a material composition anomaly score of 1.57. This score quantifies the potential disturbance to the theoretical expected value of carbon emissions caused by changes in material formulation.

[0034] The cumulative count statistics submodule accesses historical production records, calls the current bill of materials version, performs version filtering operations on historical production records, counts the total number of execution entries corresponding to the current bill of materials version in historical production records, and obtains the cumulative production count. The system accesses historical production records in the storage medium via an interface and uses the currently executing bill of materials version number as the search keyword. The filtering executor performs version matching operations on the historical production records, counting the total number of qualified batches completed under that version number, which is determined as the cumulative production count. For example, if the current version has accumulated 120 batches, the output count value is 120. This count serves as a key hyperparameter for measuring model maturity; as the production frequency of the same formula increases, the measurement bias and process disturbances in the processing architecture exhibit statistically convergent characteristics. The cumulative statistics component, through the digital quantification of production experience, provides a probability-cumulative control basis for subsequent adjustments to the observation noise of the Kalman filter.

[0035] The weight ratio calculation submodule calls the material composition change score and the cumulative number of productions, performs a division ratio calculation on the material composition change score and the cumulative number of productions, and constructs a dynamic measurement noise weight. The algorithm calls upon the material composition anomaly score (1.57) output by the anomaly score generation logic and the cumulative production count (120) output by the statistical logic. The internal arithmetic unit performs a division ratio operation on the material composition anomaly score and the cumulative production count, calculating a quotient of approximately 0.0131. This value is defined as the dynamic measurement noise weight. This operation logic establishes an inverse proportional adjustment mechanism: when a new formula is put into production, the dynamic noise weight increases significantly, reflecting the high uncertainty of the current measured data; while as production stabilizes, the weight gradually decreases. This weight, as a real-time input parameter of the adaptive filtering stage, directly determines the trust weight of the observed values ​​in data fusion.

[0036] Specifically, such as Figure 2 , 7 As shown, the ledger adaptive filtering module includes: The gain factor generation submodule obtains the theoretical static noise weights, performs an addition operation on the theoretical static noise weights and the dynamic measurement noise weights to obtain the composite noise component, and performs a division ratio operation on the theoretical static noise weights and the composite noise component to obtain the adaptive fusion gain factor. First, the theoretical static noise weight is obtained, which serves as the initial baseline parameter (e.g., set to 0.05), representing the inherent white noise during sensor acquisition and transmission in a controlled environment. The adder synchronously calls the dynamic measurement noise weight (0.0131) transmitted from upstream, performing an addition operation on the theoretical static noise weight and the dynamic measurement noise weight to obtain a composite noise component of 0.0631. Next, the divider performs a division ratio operation on the theoretical static noise weight and the composite noise component, i.e., calculating 0.05 divided by 0.0631, resulting in an adaptive fusion gain factor of approximately 0.7924. This gain factor objectively reflects the trust weight ratio between the prior model prediction and the actual measurement value under the current measurement environment, providing a dynamic allocation coefficient for the accurate correction of the final carbon footprint data.

[0037] The carbon flow residual calculation submodule obtains the prior estimated total carbon emissions, calls the measured total dynamic carbon emissions, performs algebraic deviation calculation on the measured total dynamic carbon emissions and the prior estimated total carbon emissions, and obtains the carbon flow residual term. First, the priori projected total carbon emissions (e.g., 0.7300t) are retrieved. This value is a theoretical baseline calculated based on the current batch's formulation data and historical energy efficiency parameters. Simultaneously, the component acquires the measured dynamic total carbon emissions (0.7581t) output from the integration stage. The internal deviation calculator performs algebraic deviation calculations between the measured dynamic total carbon emissions and the priori projected total carbon emissions, calculating a difference of 0.0281t, which is defined as the carbon flow residual term. This residual term quantifies the deviation of sudden emissions in actual production from the theoretical model. The accurate extraction of the carbon flow residual identifies random disturbances that cannot be covered by the static model, providing the initial error signal for subsequent weighted feedback correction.

[0038] The feedback correction overlay submodule calls the carbon flow residual term and the adaptive fusion gain factor, performs a product operation on the carbon flow residual term and the adaptive fusion gain factor to obtain the feedback correction amount, calls the prior estimated total carbon emission value, performs an addition operation on the feedback correction amount and the prior estimated total carbon emission value, and establishes a balanced carbon management ledger value. The system receives the carbon flow residual term (0.0281t) and the adaptive fusion gain factor (0.7924) from upstream and performs numerical balancing logic. The product operator performs a multiplication operation on the carbon flow residual term and the adaptive fusion gain factor, calculating a feedback correction of approximately 0.0223t. This correction represents how much of the measured deviation should be extracted to correct the theoretical prediction based on the current data quality. Finally, the adder calls the prior predicted total carbon emissions (0.7300t) and performs an addition operation on the feedback correction and the prior predicted total carbon emissions, establishing a balanced carbon management ledger value of 0.7523t.

[0039] Table 3. Filtering Correction Effect of Carbon Management Ledger N10 0.7300 0.7581 0.7924 0.7523 N11 0.7350 0.7400 0.8245 0.7391 N12 0.7280 0.7700 0.6512 0.7554 As shown in Table 3, the final generated balanced carbon management ledger values ​​serve as the accounting data for the formal output of the processing architecture. This component, through weighted fusion of measured residuals, suppresses abnormal numerical jumps caused by sensor interference while retaining accurate production fluctuation information, achieving a balance between stability and accuracy in the ledger output items.

[0040] Please see Figure 8 The method for calculating and managing the carbon footprint of porcelain insulators throughout their entire lifecycle is based on the aforementioned management system and includes the following steps: S1: During the firing temperature zone of the ceramic parts, collect temperature sensor data array and voltage sag timestamp, calculate the first derivative of temperature sensor data array to extract the abrupt temperature loss timestamp, calculate the absolute deviation of the two types of timestamps and match the heat transfer hysteresis constant, associate the insulator batch code and interval energy consumption data, and construct a batch energy consumption association record. S2: Collect the oxygen concentration sequence of the kiln flue gas, calculate the deviation between the first derivative of the oxygen concentration over time and the theoretical oxygen demand rate for complete combustion of fuel, obtain the excess oxygen consumption rate difference and convert it into the transient material carbon release rate, and perform time-domain integration to construct the endogenous carbon emission quantification data. S3: Extract the electrical energy values ​​and preset energy conversion factors from the batch energy consumption correlation records, calculate the sum of their product and the endogenous carbon emission quantification data, and construct the measured dynamic total carbon emission. S4: Determine the material composition change score based on the product of the bill of materials version difference item and the unit weight, determine the cumulative number of productions by combining the total number of historical production record items, calculate the ratio of the material composition change score to the cumulative number of productions, and construct a dynamic measurement noise weight. S5: Based on the sum of theoretical static noise weight and dynamic measured noise weight, the composite noise component is obtained. The ratio of theoretical static noise weight to composite noise component is calculated to obtain adaptive fusion gain factor. The gain factor is multiplied by the difference between the measured dynamic carbon emission total and the prior estimated total carbon emission and then superimposed to the prior estimated total carbon emission to establish a balanced carbon management ledger value.

[0041] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A carbon footprint full lifecycle calculation and management system for porcelain insulators, characterized in that, The system includes: The batch data anchoring module collects temperature sensor data arrays and voltage sag timestamps within the ceramic firing temperature zone cycle, calculates the first derivative of the temperature sensor data array to extract the abrupt temperature loss timestamp, calculates the absolute deviation between the two types of timestamps and matches the heat transfer hysteresis constant, associates the insulator batch code and interval energy consumption data, and constructs a batch energy consumption association record. The endogenous carbon quantification module collects the oxygen concentration sequence of kiln flue gas, calculates the deviation between the first derivative of oxygen concentration over time and the theoretical oxygen demand rate for complete fuel combustion, obtains the excess oxygen consumption rate difference and converts it into the transient material carbon release rate, and performs time-domain integration to construct endogenous carbon emission quantification data. The total carbon emission integration module extracts the electrical energy value and preset energy conversion factor from the batch energy consumption correlation record, calculates the product of the two and the sum of the endogenous carbon emission quantification data, and constructs the measured dynamic total carbon emission. The noise weight calculation module determines the material composition change score based on the product of the bill of materials version difference item and the unit weight, determines the cumulative number of productions by combining the total number of historical production record items, calculates the ratio of the material composition change score to the cumulative number of productions, and constructs a dynamic measurement noise weight. The adaptive filtering module of the ledger obtains the composite noise component based on the sum of the theoretical static noise weight and the dynamic measured noise weight. It calculates the ratio of the theoretical static noise weight to the composite noise component to obtain the adaptive fusion gain factor. The gain factor is multiplied by the difference between the measured dynamic carbon emissions and the prior estimated total carbon emissions and then superimposed to the prior estimated total carbon emissions to establish a balanced carbon management ledger value.

2. The carbon footprint full life cycle calculation and management system for porcelain insulators according to claim 1, characterized in that, The batch energy consumption correlation record specifically includes insulator batch code, interval energy consumption value, and equipment mapping pointer. The intrinsic carbon emission quantification data includes transient material carbon release rate, cumulative carbon emission flux, and total reaction release. The measured dynamic carbon emission total specifically refers to the basic carbon emission value, energy consumption conversion parameter, and multi-source integrated data. The dynamic measurement noise weight includes material composition anomaly score, cumulative production times, and observation noise component. The carbon management ledger value specifically refers to feedback correction amount, prior expected correction value, and ledger output item.

3. The carbon footprint full life cycle calculation and management system for porcelain insulators according to claim 1, characterized in that, The batch data anchoring module includes: The temperature loss timestamp extraction submodule acquires the temperature sensor value array within a preset period when the ceramic part enters the firing temperature zone, calculates the rate of change of the temperature sensor value array along the time dimension to obtain the first derivative, locates the minimum value position in the first derivative, and extracts the timestamp corresponding to the minimum value position to obtain the abrupt temperature loss timestamp. The time consistency determination submodule monitors the voltage sag timestamp collected by the power supply line, performs algebraic deviation calculation on the voltage sag timestamp and the rapid temperature loss timestamp to obtain the algebraic deviation value, performs absolute value calculation on the algebraic deviation value to obtain the absolute time difference value, retrieves the preset heat transfer hysteresis constant, determines the consistency between the absolute time difference value and the value of the heat transfer hysteresis constant, and obtains the consistency determination result. The batch energy consumption association submodule obtains the insulator batch code and interval energy consumption value according to the batch corresponding to the consistency judgment result, performs an association binding operation on the insulator batch code and interval energy consumption value, and constructs a batch energy consumption association record.

4. The carbon footprint full life cycle calculation and management system for porcelain insulators according to claim 3, characterized in that, The process of performing an absolute value operation on the algebraic deviation value to obtain the absolute time difference, retrieving the preset heat transfer hysteresis constant, and determining the consistency between the absolute time difference value and the heat transfer hysteresis constant value is as follows: A numerical modulus extraction operation is performed on the algebraic deviation value to obtain the absolute time difference value characterizing the electrothermal induction time difference; Access the stored records to retrieve the thermodynamic response benchmark pre-calibrated based on the kiln wall thickness, the thermal conductivity of the kiln material, and the sensor installation depth, and determine it as the heat transfer hysteresis constant; The absolute value of the algebraic difference between the absolute time difference and the heat transfer hysteresis constant is calculated to obtain the offset value. The time-domain tolerance band established based on the sensor sampling frequency is retrieved as the deviation tolerance limit. The relationship between the offset value and the deviation tolerance limit is determined. If the offset value is within the deviation tolerance limit range, it is determined that the value is consistent, and the consistency determination result is established.

5. The carbon footprint full life cycle calculation and management system for porcelain insulators according to claim 3, characterized in that, The endogenous carbon quantification module includes: The oxygen concentration derivative submodule acquires a continuous oxygen concentration numerical sequence during the operation of the kiln flue gas pipeline network, and performs a first derivative operation on the continuous oxygen concentration numerical sequence along the time dimension to obtain the oxygen consumption rate variable. The excess consumption conversion submodule calls the oxygen consumption rate variable, retrieves the theoretical oxygen demand rate for complete fuel combustion, performs a numerical deviation calculation on the oxygen consumption rate variable and the theoretical oxygen demand rate for complete fuel combustion to obtain the excess oxygen consumption rate difference, retrieves the carbon oxidation chemical reaction equivalent constant, and performs a product operation on the excess oxygen consumption rate difference and the carbon oxidation chemical reaction equivalent constant to obtain the transient material carbon release rate. The time-domain integral summarization submodule calls the transient material carbon release rate and performs integral calculations along the time dimension for the transient material carbon release rate to construct endogenous carbon emission quantification data.

6. The carbon footprint full life cycle calculation and management system for porcelain insulators according to claim 5, characterized in that, The total carbon emissions integration module includes: The energy value extraction submodule calls the batch energy consumption association record, extracts the energy consumption field corresponding to each batch from the batch energy consumption association record, and obtains the energy consumption value of the interval. The basic carbon emission calculation submodule calls the range of electrical energy consumption values, retrieves the preset energy conversion factor, and performs a product operation on the range of electrical energy consumption values ​​and the energy conversion factor to obtain the basic carbon emission values. The total carbon emissions summary submodule calls the basic carbon emissions value and the endogenous carbon emissions quantification data, performs an addition and summation operation on the basic carbon emissions value and the endogenous carbon emissions quantification data, and constructs the measured dynamic total carbon emissions.

7. The carbon footprint full life cycle calculation and management system for porcelain insulators according to claim 6, characterized in that, The noise weight calculation module includes: The anomaly score generation submodule accesses the product bill of materials, extracts the current bill of materials version and the previous bill of materials version, performs a component comparison between the current bill of materials version and the previous bill of materials version to obtain component difference items, retrieves the preset unit influence weight, performs a product operation on the change in mass percentage of each component difference item and the corresponding unit influence weight to obtain the individual anomaly component, and performs an addition operation on all individual anomaly components to obtain the material component anomaly score; The cumulative count statistics submodule accesses historical production records, calls the current bill of materials version, performs version filtering operations on historical production records, counts the total number of execution entries corresponding to the current bill of materials version in historical production records, and obtains the cumulative production count. The weight ratio calculation submodule calls the material composition change score and the cumulative number of productions, performs a division ratio calculation on the material composition change score and the cumulative number of productions, and constructs a dynamic measurement noise weight.

8. The carbon footprint full life cycle calculation and management system for porcelain insulators according to claim 7, characterized in that, The process of comparing the components between the current bill of materials version and the previous bill of materials version to obtain the component differences is as follows: Extract the mass percentage parameters of each raw material component from the current bill of materials version and the previous bill of materials version; Calculate the change in the mass percentage of the same raw material component between the two versions, and identify heterogeneous raw material components unique to the current bill of materials version; The raw material components whose mass percentage change is not zero and the heterogeneous raw material components are marked as the component difference items; The process of obtaining the unit influence weight is as follows: Access the material carbon release characteristics table established based on the determination of component chemical bond carbon content, and retrieve the carbonate percentage, organic binder ratio and thermal decomposition weight loss rate of each component in the component difference item; Using the mass ratio of carbon elements in the molecular structure of each component as a weight, a weighted summation operation is performed on the corresponding carbonate percentage, organic binder ratio, and thermal decomposition weight loss rate of each component to generate the theoretical carbon emission intensity change value corresponding to the unit mass change of the monomer material, and this value is determined as the unit influence weight.

9. The carbon footprint full life cycle calculation and management system for porcelain insulators according to claim 7, characterized in that, The ledger adaptive filtering module includes: The gain factor generation submodule obtains the theoretical static noise weight, performs an addition operation on the theoretical static noise weight and the dynamic measurement noise weight to obtain the composite noise component, and performs a division ratio operation on the theoretical static noise weight and the composite noise component to obtain the adaptive fusion gain factor. The carbon flow residual calculation submodule obtains the prior estimated total carbon emissions, calls the measured total dynamic carbon emissions, and performs algebraic deviation calculation on the measured total dynamic carbon emissions and the prior estimated total carbon emissions to obtain the carbon flow residual term. The feedback correction overlay submodule calls the carbon flow residual term and the adaptive fusion gain factor, performs a product operation on the carbon flow residual term and the adaptive fusion gain factor to obtain the feedback correction amount, calls the prior estimated total carbon emission value, performs an addition and summation operation on the feedback correction amount and the prior estimated total carbon emission value, and establishes a balanced carbon management ledger value.

10. A method for calculating and managing the carbon footprint of porcelain insulators throughout their entire lifecycle, characterized in that, The execution of the carbon footprint full life cycle calculation and management system for porcelain insulators according to any one of claims 1-9 includes the following steps: S1: During the firing temperature zone of the ceramic parts, collect temperature sensor data array and voltage sag timestamp, calculate the first derivative of temperature sensor data array to extract the abrupt temperature loss timestamp, calculate the absolute deviation of the two types of timestamps and match the heat transfer hysteresis constant, associate the insulator batch code and interval energy consumption data, and construct a batch energy consumption association record. S2: Collect the oxygen concentration sequence of the kiln flue gas, calculate the deviation between the first derivative of the oxygen concentration over time and the theoretical oxygen demand rate for complete combustion of fuel, obtain the excess oxygen consumption rate difference and convert it into the transient material carbon release rate, and perform time-domain integration to construct the endogenous carbon emission quantification data. S3: Extract the electrical energy value and preset energy conversion factor from the batch energy consumption correlation record, calculate the sum of the product of the two and the endogenous carbon emission quantification data, and construct the measured dynamic total carbon emission. S4: Determine the material composition change score based on the product of the bill of materials version difference item and the unit weight, determine the cumulative number of productions by combining the total number of historical production record items, calculate the ratio of the material composition change score to the cumulative number of productions, and construct a dynamic measurement noise weight. S5: Based on the sum of the theoretical static noise weight and the dynamic measured noise weight, the composite noise component is obtained. The ratio of the theoretical static noise weight to the composite noise component is calculated to obtain the adaptive fusion gain factor. The gain factor is multiplied by the difference between the measured dynamic carbon emissions and the prior estimated total carbon emissions and then superimposed to the prior estimated total carbon emissions to establish a balanced carbon management ledger value.

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