A building energy consumption and carbon neutralization analysis system based on big data analysis

Abstract

The application relates to the technical field of building energy consumption analysis, and discloses a building energy consumption and carbon neutralization analysis system based on big data analysis, which comprises an acquisition unit, an analysis unit, a calculation unit, a deficit unit, a judgment unit and a carbon neutralization management unit. According to a carbon neutralization urgency index, carbon neutralization management instructions of a target object are generated. The performance attenuation of the target object with the increase of the service life is captured. The analysis unit combines real-time carbon emission data of a power grid and generates a dynamic carbon trajectory slope. The real-time carbon emission change trend is reflected. The calculation unit performs segmented processing on the running length based on big data, constructs a full-cycle carbon trajectory vector, generates a health deficit degree and a ladder-type urgency index through big data analysis of the deficit unit and the judgment unit, and finally outputs management instructions by the management unit. The problems of insufficient tracking of the full-life-cycle energy consumption attenuation and carbon emission increment of the target object in traditional analysis are effectively solved, and the accuracy of long-term carbon neutralization planning is greatly improved.

Description

Technical Field

[0001] This invention relates to the field of building energy consumption analysis technology, specifically to a building energy consumption and carbon neutrality analysis system based on big data analysis. Background Technology

[0002] Currently, in building energy consumption and carbon neutrality analysis, the system often collects various energy consumption data and environmental parameters through sensors, and then analyzes the energy consumption data through machine learning algorithms to formulate energy-saving and emission-reduction plans, thereby achieving the goal of carbon neutrality.

[0003] However, the current analysis methods in shopping malls still have significant shortcomings. Specifically, since shopping mall equipment is often used for a long time, the energy consumption and carbon emissions of some equipment, such as large refrigeration equipment, will change with the increase in service life. The current analysis methods are insufficient in dynamically tracking and analyzing the energy consumption decay and carbon emission increase of equipment throughout its entire life cycle when analyzing building energy consumption and carbon neutrality, which affects the accuracy of long-term carbon neutrality planning. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a building energy consumption and carbon neutrality analysis system based on big data analysis, which solves the aforementioned problems.

[0005] The above-mentioned technical objective of the present invention is achieved through the following technical solution:

[0006] A building energy consumption and carbon neutrality analysis system based on big data analytics includes:

[0007] The data acquisition unit is used to collect basic operating data and static data of the target object in the shopping mall in real time, analyze the basic operating data and static data to obtain the operating degradation value of the target object, which is a large refrigeration equipment.

[0008] The analysis unit is used to obtain the real-time carbon emissions per kilowatt-hour of the current power grid, analyze the operating degradation value, the real-time carbon emissions per kilowatt-hour of the power grid and the rated power of the target object, and obtain the dynamic carbon trajectory slope.

[0009] The computing unit is used to calculate the dynamic carbon trajectory slope and the runtime of the target object, and generate a full-cycle carbon trajectory vector.

[0010] The deficit unit is used to analyze the full-cycle carbon trajectory vector and the lifetime of the target object to obtain the health deficit degree.

[0011] The judgment unit is used to generate a step-by-step carbon neutrality urgency index when the health deficit exceeds a critical point;

[0012] The carbon neutrality and management unit is used to generate carbon neutrality and management instructions for target entities based on the carbon neutrality urgency index.

[0013] Furthermore, by analyzing the basic operational data and static data, the operational degradation value of the target object is obtained, including:

[0014] The basic operational data and static data are processed to obtain the data standardization coefficient;

[0015] Based on the data standardization coefficient, the dynamic deviation is obtained by comparing the basic operating data with the rated benchmark parameters in the static data.

[0016] Analyze the manufacturing time, running time, and service life in static data to generate a time decay coefficient;

[0017] The dynamic deviation, time decay coefficient, and data standardization coefficient are weighted and fused to obtain the operational degradation value.

[0018] Furthermore, by analyzing the operational degradation value, real-time carbon emissions per kilowatt-hour of the power grid, and the rated power of the target object, the dynamic carbon trajectory slope is obtained, including:

[0019] Analyze the deviation between the operational degradation value and the rated power of the target object, and generate the actual power deviation coefficient;

[0020] Based on the actual power offset coefficient and the real-time carbon emissions per kilowatt-hour of the power grid, the real-time change in carbon emissions per unit time is calculated to obtain the instantaneous carbon intensity factor.

[0021] The degradation value is correlated with the instantaneous carbon intensity factor to generate a degradation carbon sensitivity coefficient;

[0022] The slope of the dynamic carbon trajectory is obtained by calculating the actual power offset coefficient, instantaneous carbon intensity factor, and deteriorated carbon sensitivity coefficient.

[0023] Furthermore, the operational degradation value is correlated with the instantaneous carbon intensity factor to generate a degradation carbon sensitivity coefficient, including:

[0024] The operational degradation value and instantaneous carbon intensity factor are extracted to generate a feature mapping matrix;

[0025] Based on the feature mapping matrix, the dynamic correlation sequence between the running degradation value and the instantaneous carbon intensity factor is calculated;

[0026] The energy efficiency label level in the static data is mapped to obtain the energy efficiency correction coefficient;

[0027] The dynamic correlation series is fused with the energy efficiency correction coefficient to obtain the preliminary sensitivity coefficient;

[0028] The initial sensitivity coefficient is calibrated based on the ambient temperature and humidity data from the basic operation data to generate the deterioration carbon sensitivity coefficient.

[0029] Furthermore, the dynamic carbon trajectory slope and the runtime of the target object are calculated to generate a full-cycle carbon trajectory vector, including:

[0030] The runtime of the target object is naturally segmented, and the slope of the dynamic carbon trajectory is truncated based on the natural segmentation to obtain the slope value of each time period;

[0031] Calculate the slope difference between adjacent time periods, and combine it with the running time of the corresponding time period to obtain the slope change rate per unit time.

[0032] Based on the bearing vibration frequency and voltage, the stability index of the target object is calculated, the correlation between the slope change rate per unit time and the stability index of the target object during that time period is analyzed, and the operation status correction coefficient is generated.

[0033] The slope values ​​for each time period are calibrated based on the operation status correction coefficient to obtain the corrected time-period slope values;

[0034] The cumulative carbon trajectory contribution value for each time period is obtained by calculating the corrected time-segment slope value and the running time of the corresponding time period.

[0035] The slope values ​​of each time period, the corrected slope values ​​of each time period, and the cumulative carbon trajectory contribution value are fused to generate a full-cycle carbon trajectory vector.

[0036] Furthermore, the runtime of the target object is naturally segmented, and the slope of the dynamic carbon trajectory is truncated based on the natural segmentation to obtain the slope value for each time period, including:

[0037] Based on the number of start-stop cycles and ambient temperature and humidity in the basic operation data, the segment threshold of the runtime is determined, and dynamic segment boundary values ​​are generated.

[0038] The runtime is divided into continuous time periods based on dynamic segmentation boundary values. The slope of the dynamic carbon trajectory in each time period is extracted synchronously to generate the original dataset of time period slope.

[0039] Based on the current and voltage data in the basic operation data, the original dataset of slope values ​​for each time period is filtered to obtain the slope values ​​for each time period.

[0040] Furthermore, by analyzing the full-cycle carbon trajectory vector and the lifetime of the target object, the health deficit degree is obtained, including:

[0041] Analyze the temporal matching relationship between the full-cycle carbon trajectory vector and the lifetime of the target object, and generate carbon-time matching coefficients;

[0042] Based on the full-cycle carbon trajectory vector, the cumulative carbon trajectory contribution value of each time period is accumulated to obtain the cumulative carbon emission load of the target object during its service life.

[0043] The cumulative carbon emission load is combined with the lifespan of the target object to generate the lifespan carbon load rate;

[0044] The health deficit degree is obtained by calculating the carbon time matching coefficient and lifetime carbon load rate.

[0045] Furthermore, when the health deficit exceeds a critical point, a tiered carbon neutrality urgency index is generated, including:

[0046] The critical point is calculated based on the full-cycle carbon trajectory vector, the lifespan of the target object, and the energy efficiency label level in static data.

[0047] Based on the critical point, the health deficit is divided into multiple continuous carbon emission intervals, generating different urgency levels for each interval;

[0048] The correlation between the health deficit degree and the full-cycle carbon trajectory vector within each carbon emission interval is analyzed to generate interval characteristic factors;

[0049] Obtain historical carbon emission data of the target object, combine historical carbon emission data with basic operational data, analyze the carbon emission growth trend within each carbon emission interval, and generate trend growth coefficients.

[0050] Based on the exceedance range, range characteristic factor, and trend growth coefficient of each carbon emission range and critical point, different weights are assigned to each carbon emission range to obtain the dynamic weight value of each carbon emission range.

[0051] The urgency level base and dynamic weight value of each carbon emission range are calculated to obtain a stepped carbon neutrality urgency index.

[0052] Furthermore, based on the full-cycle carbon trajectory vector, the target object's lifetime, and the energy efficiency rating in static data, the critical point is calculated, including:

[0053] Using the lifespan of the target object as a time benchmark, and combining the rated power and the grid benchmark carbon emissions per kilowatt-hour in the static data, a benchmark full-cycle carbon trajectory vector is generated.

[0054] The deviation threshold between the full-cycle carbon trajectory vector and the benchmark full-cycle carbon trajectory vector is calculated. The deviation threshold and the energy efficiency correction coefficient are then calculated to obtain the critical initial point.

[0055] The lifespan degradation correction coefficient is determined based on the ratio of runtime to lifespan in static data.

[0056] The critical initial point is dynamically calibrated based on the lifetime decay correction coefficient to obtain the critical point.

[0057] Furthermore, based on the carbon neutrality urgency index, carbon neutrality and management directives for the target entities are generated, including:

[0058] Based on the carbon neutrality urgency index and the basic operational data of the target objects, measure suitability is generated.

[0059] Based on the suitability of the measures and the carbon neutrality urgency index, carbon neutrality and management directives for the target entities are generated.

[0060] In summary, the present invention has the following main beneficial effects:

[0061] By aggregating basic operational and static data of large-scale refrigeration equipment in real time through big data, and standardizing and comparing the two types of data in multiple dimensions, dynamic deviation and time decay coefficients are generated. Finally, accurate operational degradation values ​​are obtained through weighted fusion. This big data-based degradation assessment method breaks through the limitations of traditional methods that rely on manual inspection or single-parameter judgment. It can capture the performance degradation of large-scale refrigeration equipment caused by aging and environmental changes in real time, which facilitates subsequent carbon neutrality analysis and management of large-scale refrigeration equipment.

[0062] By integrating dynamic data such as real-time carbon emissions per kilowatt-hour of the power grid, rated power of the target object, and operational degradation value through the analysis unit, and generating actual power offset coefficient and instantaneous carbon intensity factor through big data analysis, a dynamic carbon trajectory slope is constructed. Combined with the natural segmentation of the operating time and slope calibration by the calculation unit, a full-cycle carbon trajectory vector is formed, which fully presents the carbon emission accumulation process of the target object from its commissioning to its scrapping. This dynamic trajectory analysis based on big data can not only reflect the current carbon emission intensity of the target object in real time, but also clearly grasp the changes in the carbon footprint of the target object throughout its entire life cycle through correlation mining of historical data and real-time data.

[0063] By employing a big data-driven tiered management mechanism, the accuracy and timeliness of carbon neutrality measures are improved. Through big data analysis of deficit and judgment units, the full-cycle carbon trajectory vector is correlated with the lifespan of the target object to generate a health deficit level. When the deficit exceeds the limit, a tiered carbon neutrality urgency index is automatically generated. This index comprehensively considers multiple dimensions such as the target object's historical carbon emission trends, changes in environmental parameters, and energy efficiency levels, matching differentiated weights to different urgency levels. This ensures that the carbon neutrality and management units can output targeted instructions. Compared to traditional carbon neutrality analysis schemes, this big data-based tiered management scheme can implement different carbon neutrality and management schemes when carbon emissions exceed the limit, improving the efficiency of carbon neutrality analysis and management for buildings such as shopping malls. Attached Figure Description

[0064] Figure 1 This is a schematic diagram of the building energy consumption and carbon neutrality analysis system based on big data analysis of the present invention. Detailed Implementation

[0065] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0066] refer to Figure 1 A building energy consumption and carbon neutrality analysis system based on big data analytics, comprising:

[0067] The data acquisition unit is used to collect basic operational data and static data of the target object in the shopping mall in real time. It analyzes the basic operational data and static data to obtain the operational degradation value of the target object, which is a large refrigeration equipment. The basic operational data includes: current, voltage, bearing vibration frequency, number of start-stop cycles, refrigerant pressure, ambient temperature and humidity, real-time load, etc. The static data includes: manufacturing date, equipment model, running time, service life, rated power, energy efficiency label level, etc.

[0068] The analysis unit is used to obtain the real-time carbon emissions per kilowatt-hour of the current power grid, analyze the operating degradation value, the real-time carbon emissions per kilowatt-hour of the power grid and the rated power of the target object, and obtain the dynamic carbon trajectory slope.

[0069] The computing unit is used to calculate the dynamic carbon trajectory slope and the runtime of the target object, and generate a full-cycle carbon trajectory vector.

[0070] The deficit unit is used to analyze the full-cycle carbon trajectory vector and the lifetime of the target object to obtain the health deficit degree.

[0071] The judgment unit is used to generate a step-by-step carbon neutrality urgency index when the health deficit exceeds a critical point;

[0072] The carbon neutrality and management unit is used to generate carbon neutrality and management instructions for target entities based on the carbon neutrality urgency index.

[0073] By integrating real-time operating data and static parameters of large-scale refrigeration equipment in shopping malls using big data technology, a full-cycle carbon trajectory vector is constructed. This enables dynamic tracking of energy consumption decay and carbon emission increments throughout the entire life cycle of large-scale refrigeration equipment. Big data analytics capabilities can process massive amounts of sensor data in real time, accurately capturing the energy consumption characteristics of large-scale refrigeration equipment as its service life changes. Combined with real-time carbon emission data from the power grid, a dynamic carbon trajectory slope is generated, overcoming the shortcomings of traditional analysis in tracking long-term dynamic changes. This provides accurate data support for long-term carbon neutrality planning and improves the accuracy of planning.

[0074] By conducting in-depth analysis of the full-cycle carbon trajectory vector and the health deficit of the target object, a tiered carbon neutrality urgency index can be generated when the health deficit of the target object exceeds the limit. The tiered carbon neutrality urgency index relies on the comprehensive calculation of multi-dimensional parameters by big data, which can trigger targeted carbon neutrality and management instructions, realize strategies to optimize the energy consumption management of the target object, effectively reduce the increase in carbon emissions caused by the aging of the target object, and improve the accuracy and efficiency of long-term carbon neutrality and management of buildings such as shopping malls.

[0075] In one embodiment, basic operational data and static data are analyzed to obtain the operational degradation value of the target object, including:

[0076] The basic operational data and static data are processed to obtain data standardization coefficients. Specifically, this includes: obtaining the standard range of parameters in the preprocessed basic operational data and the standard value of parameters in the preprocessed static data. The standard range and standard value are the reasonable range of these parameters when the target object is running normally. The deviation of the actual value of each parameter in the basic operational data from the standard range is calculated. The difference between the actual value and the standard value of each parameter in the static data is calculated, and this difference is taken as the deviation degree. A weight is assigned to each parameter based on the deviation degree. The greater the deviation degree, the greater the weight, and the smaller the deviation degree, the smaller the weight. The deviation degree of each parameter is multiplied by its corresponding weight to obtain the weighted deviation value of each parameter. All weighted deviation values ​​are then added together, and the sum is divided by the total number of parameters to obtain the data standardization coefficient.

[0077] Based on the data standardization coefficient, the dynamic deviation is obtained by comparing the rated benchmark parameters in the basic operating data and the static data. Specifically, this includes: classifying the parameters in the basic operating data and the static data according to the following criteria: if a parameter is higher, it is better for the target object, so it is classified as a positive indicator (e.g., energy efficiency level); if a parameter is lower, it is better for the target object, so it is classified as a negative indicator (e.g., vibration frequency). For positive indicators, the adjusted deviation value is obtained by subtracting the data standardization coefficient from 1 and then multiplying it by the deviation degree. For positive indicators, the adjusted deviation value is obtained by adding the data standardization coefficient to 1 and then multiplying it by the deviation degree. The mean of the adjusted deviation values ​​of all parameters is calculated, and this mean is the dynamic deviation.

[0078] Analyzing the manufacturing date, runtime, and lifespan in static data generates a time decay coefficient. Specifically, this involves: determining a base decay ratio based on the target object's manufacturing date: 0.3 for manufacturing dates greater than 10 years, 0.2 for manufacturing dates between 5 and 10 years, and 0.1 for manufacturing dates less than 5 years; calculating the ratio of runtime to lifespan and multiplying by 100 to obtain a percentage; increasing the base decay ratio by 0.1 when the percentage is below 30% (indicating a relatively new target object); increasing it by 0.3 when the percentage is between 30% and 70% (indicating the target object has been used for some time); and increasing it by 0.5 when the percentage exceeds 70% (indicating the target object is old); finally, summing all the base decay ratios gives the time decay coefficient.

[0079] The dynamic deviation, time decay coefficient, and data standardization coefficient are weighted and fused to obtain the operational degradation value. Specifically, the weights of the dynamic deviation, time decay coefficient, and standardization coefficient are set to 0.4, 0.4, and 0.2, respectively. The dynamic deviation, time decay coefficient, and standardization coefficient are multiplied by their respective weights and then summed to obtain the operational degradation value. Among them, the dynamic deviation directly reflects the deviation of the target object's current operating parameters from the standard range and is used to reflect the real-time degradation status, so it is relatively critical and has a high weight of 0.4. The time decay coefficient reflects the aging accumulation of the target object over time and is the core factor of long-term degradation, so it has a high weight of 0.4. The standardization coefficient is a basic adjustment factor for data processing and mainly assists in correcting deviations, so it has a low weight of 0.2.

[0080] By integrating multi-dimensional data from large-scale refrigeration equipment through big data, and through standardized processing and dynamic deviation calculation, the operational degradation value of large-scale refrigeration equipment is accurately quantified. Combined with the time decay coefficient, the aging pattern of large-scale refrigeration equipment with the years is captured, realizing dynamic tracking of energy consumption degradation and carbon emission increments throughout the entire life cycle. Compared with traditional methods, this overcomes the limitations of insufficient analysis of long-term dynamic changes, providing more accurate data support for long-term carbon neutrality planning. At the same time, in-depth analysis of multiple parameters such as operational degradation value generates dynamic carbon trajectory and health deficit degree, triggering tiered carbon neutrality and management when limits are exceeded. Based on the multi-dimensional data integration capabilities of big data, carbon neutrality and management become more targeted and timely. By adjusting strategies in real time, the energy consumption of large-scale refrigeration equipment is effectively optimized, carbon emission increments are reduced, and the efficiency of long-term carbon neutrality and management in shopping malls is significantly improved.

[0081] In one embodiment, the dynamic carbon trajectory slope is obtained by analyzing the operational degradation value, the real-time carbon emissions per kilowatt-hour of the power grid, and the rated power of the target object, including:

[0082] The analysis of the deviation between the operational degradation value and the rated power of the target object generates an actual power deviation coefficient. Specifically, this includes: using the rated power of the target object as the benchmark value, using the operational degradation value as the correction coefficient, the actual power equals the rated power multiplied by (1 + operational degradation value), calculating the difference between the actual power and the rated power to obtain the absolute deviation, dividing the absolute deviation by the rated power to obtain the actual power deviation coefficient, which is used to quantify the degree of power deviation caused by operational degradation.

[0083] Based on the actual power offset coefficient and the real-time carbon emissions per kilowatt-hour of the power grid, the real-time change in carbon emissions per unit time is calculated to obtain the instantaneous carbon intensity factor. Specifically, this includes: multiplying the rated power by the real-time carbon emissions per kilowatt-hour of the power grid to obtain the baseline carbon emissions of the target object per unit time; then multiplying the rated power by (1 + actual power offset coefficient) by the real-time carbon emissions per kilowatt-hour of the power grid to obtain the actual carbon emissions of the target object per unit time under the current deterioration state; subtracting the baseline carbon emissions from the actual carbon emissions and then dividing by the baseline carbon emissions to obtain the real-time rate of change of carbon emissions; and adding 1 to the real-time rate of change to obtain the instantaneous carbon intensity factor.

[0084] The degradation value is correlated with the instantaneous carbon intensity factor to generate a degradation carbon sensitivity coefficient;

[0085] The slope of the dynamic carbon trajectory is obtained by calculating the actual power offset coefficient, instantaneous carbon intensity factor, and deteriorated carbon sensitivity coefficient. Specifically, the actual power offset coefficient, instantaneous carbon intensity factor, and deteriorated carbon sensitivity coefficient are each assigned an equal weight, and the sum of the weight values ​​is 1. The slope of the dynamic carbon trajectory is obtained by multiplying the actual power offset coefficient, instantaneous carbon intensity factor, and deteriorated carbon sensitivity coefficient by their corresponding weights and adding them together.

[0086] By integrating multi-source data such as operational degradation values, real-time carbon emissions from the power grid, and rated power through big data analysis, the system calculates parameters such as actual power offset coefficient and instantaneous carbon intensity factor to generate a dynamic carbon trajectory slope. This accurately captures the dynamic carbon emissions of large-scale refrigeration equipment as its service life changes, overcoming the limitations of traditional analysis in tracking the incremental carbon emissions throughout the entire life cycle, improving planning accuracy. Furthermore, the weighted fusion of parameters related to the dynamic carbon trajectory slope quantifies the correlation between the degradation of large-scale refrigeration equipment and carbon emissions. Based on the multi-dimensional analysis capabilities of big data, the dynamic carbon trajectory slope can reflect the current and long-term carbon emission trends of large-scale refrigeration equipment in real time, thereby enabling earlier detection of carbon emission anomalies, optimization of carbon neutrality and management strategies, and improvement of the long-term carbon neutrality and management efficiency of the market.

[0087] In one embodiment, the operational degradation value is correlated with the instantaneous carbon intensity factor to generate a degradation carbon sensitivity coefficient, including:

[0088] The operating degradation value and instantaneous carbon intensity factor are extracted to generate a feature mapping matrix. Specifically, the operating degradation value and instantaneous carbon intensity factor are used as feature dimensions. The real-time values ​​of the two are extracted in the order of collection time. The operating degradation value at each time point is used as the first column element, and the instantaneous carbon intensity factor at the corresponding time point is used as the second column element, forming a rectangular data array composed of rows (time dimension) and columns (feature dimension). This array is the feature mapping matrix.

[0089] Based on the feature mapping matrix, the dynamic correlation sequence between running degradation values ​​and instantaneous carbon intensity factors is calculated. Specifically, this involves: standardizing the data in the columns containing running degradation values ​​and instantaneous carbon intensity factors in the feature mapping matrix to eliminate the influence of different dimensions; then, dividing the standardized data into multiple overlapping sliding windows in chronological order, with each window containing the same number of continuous time points; and for each sliding window, calculating the local correlation between running degradation values ​​and instantaneous carbon intensity factors within that window: subtracting the mean value of running degradation values ​​within that window from each data point of running degradation values ​​within that window. The deviation of each operational degradation value is obtained. Then, the mean value of the instantaneous carbon intensity factor within the window is subtracted from each data point of the instantaneous carbon intensity factor within the window to obtain the deviation of each instantaneous carbon intensity factor. The two deviations at corresponding time points are multiplied and added together to obtain the sum of deviation products. The sum of squares of all deviations of operational degradation values ​​and the sum of squares of all deviations of instantaneous carbon intensity factors are calculated separately. The two sums of squares are multiplied and the square root is taken. The sum of deviation products is divided by the square root to obtain the local correlation degree of the window. The local correlation degrees of all windows are arranged in order of their corresponding time to form the dynamic correlation degree sequence.

[0090] Mapping the energy efficiency label levels in static data to obtain energy efficiency correction coefficients involves sorting the energy efficiency label levels from highest to lowest. When the energy efficiency label level is the highest, the energy efficiency correction coefficient is 1.0. For each level decrease, the energy efficiency correction coefficient decreases by 0.1, and when the energy efficiency label level is the lowest, the energy efficiency correction coefficient is 0.5.

[0091] The dynamic correlation degree sequence is fused with the energy efficiency correction coefficient to obtain the preliminary sensitivity coefficient. Specifically, this involves multiplying each local correlation degree in the dynamic correlation degree sequence by the energy efficiency correction coefficient to obtain the corrected correlation degree corresponding to each time window; and calculating the average value of all corrected correlation degrees to obtain the preliminary sensitivity coefficient.

[0092] The preliminary sensitivity coefficient is calibrated based on the ambient temperature and humidity data in the basic operation data to generate the deterioration carbon sensitivity coefficient. Specifically, this includes: taking the midpoint of the standard range of ambient temperature and humidity as the standard center value; calculating the difference between the actual temperature and humidity value and the standard center value to obtain the temperature and humidity deviation; multiplying the temperature and humidity deviation by 0.02 to obtain the adjusted calibration coefficient; and multiplying the preliminary sensitivity coefficient by the adjusted calibration coefficient to obtain the deterioration carbon sensitivity coefficient.

[0093] By using big data technology to process operational degradation values ​​and instantaneous carbon intensity factors from multiple dimensions, a feature mapping matrix and a dynamic correlation sequence are generated. Big data can efficiently integrate massive amounts of feature data over time, accurately capturing the dynamic correlation between the two as the target object changes with its service life. Combined with energy efficiency correction coefficients and environmental temperature and humidity calibration, the generated degradation carbon sensitivity coefficient can quantify the sensitivity of the target object's degradation to carbon emissions, making up for the ambiguity in the correlation between target object characteristics and carbon emissions in traditional analysis. Furthermore, the degradation carbon sensitivity coefficient can dynamically adapt to the state changes of the target object throughout its entire life cycle, clearly reflecting the intensity of carbon emission response to degradation during the aging process of the target object, quickly identifying abnormal carbon emission trends, and improving the accuracy of long-term carbon neutrality planning.

[0094] In one embodiment, the dynamic carbon trajectory slope and the runtime of the target object are calculated to generate a full-cycle carbon trajectory vector, including:

[0095] The runtime of the target object is naturally segmented, and the slope of the dynamic carbon trajectory is truncated based on the natural segmentation to obtain the slope value of each time period;

[0096] Calculate the slope difference between adjacent time periods and combine it with the running time of the corresponding time periods to obtain the slope change rate per unit time. Specifically, this includes: subtracting the slope value of the previous time period from the slope value of the later time period to obtain the slope difference between the two adjacent time periods; calculating the total running time of the two adjacent time periods and dividing the slope difference by the total running time to obtain the slope change rate per unit time.

[0097] Based on bearing vibration frequency and voltage, the stability index of the target object is calculated. The correlation between the slope change rate per unit time and the stability index of the target object during that period is analyzed, and an operating state correction coefficient is generated. Specifically, this includes: calculating the absolute deviation between the actual values ​​of bearing vibration frequency and voltage and the center value of the standard range, and then dividing the absolute deviation by the width of the standard range (the difference between the upper and lower limits of the standard interval) to obtain the vibration deviation rate and voltage deviation rate; multiplying the vibration deviation rate by 0.6 and adding the voltage deviation rate by 0.4 to obtain the stability index of the target object; and dividing the slope change rate per unit time by the stability index to obtain the operating state correction coefficient.

[0098] The slope values ​​for each time period are calibrated based on the operational status correction coefficient to obtain the corrected time-period slope values. Specifically, the operational status correction coefficient for each time period is multiplied by the slope value for that time period, and the result is the corrected time-period slope value. This is used to calibrate the impact of the target object's operational stability on the carbon trajectory slope, ensuring that the slope values ​​for each time period can truly reflect the changes in the carbon emission trajectory.

[0099] The corrected time-segment slope values ​​and the corresponding running time of each time period are calculated to obtain the cumulative carbon trajectory contribution value for each time period. Specifically, for each time period, the corrected time-segment slope value is multiplied by the running time of that time period to obtain the basic contribution value for that time period; then the basic contribution value is multiplied by the average real-time carbon emissions per kilowatt-hour for that time period to obtain the cumulative carbon trajectory contribution value for each time period.

[0100] The slope values ​​of each time period, the corrected slope values ​​of each time period, and the cumulative carbon trajectory contribution value are fused to generate a full-cycle carbon trajectory vector. Specifically, this involves: taking the slope value of each time period, the corrected slope value of each time period, and the cumulative carbon trajectory contribution value as the three-dimensional elements of the vector in chronological order to form a three-dimensional array; and combining the three-dimensional arrays of all time periods in chronological order to form the sequence that constitutes the full-cycle carbon trajectory vector, which fully presents the multi-dimensional temporal characteristics of the carbon trajectory.

[0101] By integrating slope values, corrected slope values, and cumulative carbon trajectory contribution values ​​across multiple time periods throughout the entire lifecycle of a target object using big data technology, a full-cycle carbon trajectory vector is generated. This vector presents the temporal characteristics of the carbon trajectory through multi-dimensional fusion, accurately capturing the dynamic changes in carbon emissions of the target object over its service life. It also analyzes the impact of parameters such as bearing vibration frequency and voltage on the carbon trajectory in real time, generating operational status correction coefficients. This addresses the shortcomings of traditional analysis in dynamically tracking the carbon emission trajectory throughout the entire lifecycle. Furthermore, by utilizing big data to perform in-depth calculations on the dynamic carbon trajectory slope and operating time, it generates cumulative carbon trajectory contribution values ​​for each time period. By incorporating factors such as target object stability and grid carbon emissions into the carbon trajectory vector, it fully reconstructs the full-cycle carbon emission change pattern, enabling the system to accurately identify the incremental trend of carbon emissions during the aging process of the target object. This solves the deficiency of traditional analysis in insufficiently tracking the correlation between long-term energy consumption degradation and carbon emissions of the target object.

[0102] In one embodiment, the runtime of the target object is naturally segmented, and the slope of the dynamic carbon trajectory is truncated based on the natural segmentation to obtain the slope value for each time period, including:

[0103] Based on the start / stop count and ambient temperature and humidity data in the basic operation data, the segmentation thresholds for the operating time are determined, and dynamic segmentation boundary values ​​are generated. Specifically, this includes: dividing the average daily start / stop count of the target object by the reasonable average daily start / stop count corresponding to the target object to obtain the start / stop impact coefficient; dividing the difference between the actual temperature and humidity and the standard center value by the width of the ambient temperature and humidity standard range (upper limit - lower limit) to obtain the temperature and humidity deviation rate; adding the temperature and humidity deviation rates and dividing by 2 to obtain the environmental impact coefficient; and weighted summing the start / stop impact coefficient (weight 0.6) and the environmental impact coefficient (weight 0.4) to obtain the comprehensive segmentation factor. When the comprehensive segmentation factor ≤ 0.3, the operating time is segmented in 24-hour segments; when 0.3 < comprehensive segmentation factor ≤ 0.7, the operating time is segmented in 12-hour segments; and when the comprehensive segmentation factor > 0.7, the operating time is segmented in 6-hour segments. The start and end times of the corresponding time periods are the dynamic segmentation boundary values.

[0104] The runtime is divided into continuous time periods based on dynamic segmentation boundary values. The slope of the dynamic carbon trajectory within each time period is extracted synchronously to generate a raw dataset of time period slopes. Specifically, this includes: dividing continuous time periods according to the time order of the dynamic segmentation boundary values, starting from the start time of the target object, using the boundary values ​​as nodes to ensure that the time periods do not overlap and cover the entire runtime; extracting the collection timestamp of the dynamic carbon trajectory slope within each time period and comparing it with the start and end times of the time period; filtering out the dynamic carbon trajectory slopes that fall within the time period; and aggregating the dynamic carbon trajectory slopes according to the time period number to form a raw dataset of time period slopes containing time period identifiers and corresponding slope sequences.

[0105] Based on the current and voltage data in the basic operational data, the original dataset of slope values ​​for each time period is filtered to obtain the slope values ​​for each time period. Specifically, this includes: determining the standard range of current and voltage (the reasonable range during normal operation of the target object), taking the midpoint of the standard range as the standard center value, and the difference between the upper and lower limits of the reasonable range as the standard range width. For each slope data in the original dataset of slope values ​​for each time period, the actual current value and actual voltage value at the corresponding time are subtracted from the standard center values ​​of current and voltage, respectively, to obtain the difference between current and voltage. Then, the difference between current and voltage is divided by the standard range width of current and the standard range width of voltage, respectively, to obtain the deviation rate of current and voltage. A deviation rate threshold of 0.1 is set. If the current deviation rate or voltage deviation rate of the slope data exceeds 0.1, the data is removed. For all the remaining slope data that are not removed, their values ​​are added together and then divided by the total number of data to obtain the slope value for each time period.

[0106] By analyzing real-time data such as the start-up and shutdown frequency and ambient temperature and humidity of large-scale refrigeration equipment in shopping malls using big data, dynamic segmentation boundary values ​​are generated to achieve precise and natural segmentation of operating time. The segmentation granularity is dynamically adjusted by comprehensive segmentation factors to avoid the limitations of traditional fixed segmentation. At the same time, relying on big data to quickly screen massive amounts of current and voltage data, abnormal slope data can be eliminated to ensure that the slope values ​​of each time period are true and reliable, thereby improving the accuracy of long-term carbon emission dynamic tracking. The segmentation and screening of dynamic carbon trajectory slope can be combined with the real-time operating status of large-scale refrigeration equipment to optimize data collection methods, providing a more accurate analytical basis for long-term carbon neutrality planning based on the actual operating status of large-scale refrigeration equipment, and ensuring the accuracy of management.

[0107] In one embodiment, the health deficit is obtained by analyzing the full-cycle carbon trajectory vector and the lifetime of the target object, including:

[0108] The analysis of the temporal matching relationship between the full-cycle carbon trajectory vector and the lifespan of the target object generates a carbon-time matching coefficient. Specifically, this involves: dividing the lifespan of the target object into an equal number of lifespan periods based on the same division method for runtime, ensuring a one-to-one correspondence between the time periods corresponding to the full-cycle carbon trajectory vector and the lifespan periods; calculating the proportion of the cumulative contribution value of each time period corresponding to the full-cycle carbon trajectory vector to the total cumulative contribution value of the entire cycle, thus obtaining the carbon contribution proportion; calculating the proportion of the runtime of each time period to the total lifespan of the target object, thus obtaining the time proportion; for each time period, dividing the carbon contribution proportion by the time proportion to obtain the single-time period matching coefficient; and weighting and summing the single-time period matching coefficients using the proportion of the runtime of each time period to the total runtime as weights, thus obtaining the carbon-time matching coefficient. The closer the carbon-time matching coefficient is to 1, the higher the temporal matching degree between the carbon trajectory change and the lifespan consumption of the target object.

[0109] Based on the full-cycle carbon trajectory vector, the cumulative carbon trajectory contribution value of each time period is accumulated to obtain the cumulative carbon emission load of the target object during its service life. Specifically, this includes: based on the full-cycle carbon trajectory vector, extracting the cumulative carbon trajectory contribution value of each time period, and adding the cumulative carbon trajectory contribution values ​​of all time periods in chronological order. The sum obtained is the cumulative carbon emission load of the target object during its service life.

[0110] The cumulative carbon emission load is combined with the lifespan of the target object to generate the lifespan carbon load rate. Specifically, this involves dividing the cumulative carbon emission load of the target object over its lifespan by its total lifespan to obtain the carbon emission load per unit lifespan. Then, the carbon emission load per unit lifespan is divided by the benchmark carbon load per unit lifespan corresponding to the rated power of the target object to obtain the lifespan carbon load rate. This allows for the quantification of the matching relationship between carbon emission load and the lifespan of the target object. The calculation process for the benchmark carbon load per unit lifespan corresponding to the rated power of the target object is as follows: Based on the model of the target object, the corresponding benchmark carbon emission intensity (i.e., the carbon emission per unit power per unit time under standard operating conditions) is obtained. The benchmark carbon emission intensity is multiplied by the rated power of the target object to obtain the benchmark carbon emission per unit time. The benchmark carbon emission per unit time is multiplied by the total lifespan of the target object to obtain the total lifespan benchmark carbon load. Finally, the total lifespan benchmark carbon load is divided by the total lifespan to obtain the benchmark carbon load per unit lifespan.

[0111] The health deficit is calculated by subtracting the absolute value of the carbon-time matching coefficient from 1 to obtain the time-series matching deviation value, subtracting 1 from the life-series carbon load rate to obtain the load exceedance deviation value, and multiplying the time-series matching deviation value by 0.3 and adding the load exceedance deviation value multiplied by 0.7 to obtain the health deficit. The larger the health deficit, the more serious the imbalance between the health and carbon emissions of the target object.

[0112] By accurately quantifying the temporal matching degree between carbon trajectory changes and lifespan consumption through carbon-time matching coefficients, and based on big data, this approach efficiently processes massive amounts of cumulative carbon contribution values ​​and lifespan segment data over various time periods. Combined with multi-dimensional parameters, it calculates the lifespan carbon load rate, clearly presenting the correlation between carbon emission load and the lifespan of the target object. This comprehensive analysis based on big data breaks through the limitations of traditional methods in dynamically tracking the correlation between energy consumption and carbon emissions throughout the entire life cycle, providing data support for health deficit assessment. The deep integration of carbon-time matching coefficients and lifespan carbon load rates generates a health deficit that intuitively reflects the degree of imbalance between the target object's health and carbon emissions. Furthermore, by integrating time-series matching deviations and load exceedance deviations according to weights, it accurately captures carbon emission anomalies that increase with the target object's service life, compensating for the shortcomings of traditional analysis in dynamically tracking the incremental carbon emissions throughout the target object's entire life cycle, and ensuring the effectiveness of long-term carbon neutrality and management.

[0113] In one embodiment, when the health deficit exceeds a critical point, a step-wise carbon neutrality urgency index is generated, including:

[0114] The critical point is calculated based on the full-cycle carbon trajectory vector, the lifespan of the target object, and the energy efficiency label level in static data.

[0115] Using the critical point as a benchmark, the health deficit is divided into multiple continuous carbon emission intervals, generating different urgency level base values ​​for each interval. Specifically, this includes: using the critical point as a benchmark value, calculating the difference between the target object's historical maximum health deficit and the benchmark value, and dividing this difference into three equal parts as interval values; the range from the benchmark value to the benchmark value + 1 interval value is the slightly exceeding interval, corresponding to an urgency level base value of 1; the range from the benchmark value + 1 interval value to the benchmark value + 2 interval values ​​is the moderate exceeding interval, corresponding to an urgency level base value of 2; and the range from the benchmark value + 2 interval values ​​to above the benchmark value + 2 interval values ​​is the severely exceeding interval, corresponding to an urgency level base value of 3. These intervals are continuous and non-overlapping, covering all carbon emission intervals.

[0116] The correlation between the health deficit degree and the full-cycle carbon trajectory vector within each carbon emission interval is analyzed to generate interval characteristic factors. Specifically, this includes: calculating the average of all health deficit degrees within each carbon emission interval to obtain the interval deficit mean; calculating the average of the cumulative carbon trajectory contribution values ​​in the full-cycle carbon trajectory vector for the corresponding period to obtain the interval contribution mean; dividing the interval deficit mean by the interval contribution mean, and then multiplying it by 0.5 times the urgency level base of the interval to obtain the interval characteristic factor.

[0117] The process involves acquiring historical carbon emission data for the target object, combining this historical data with basic operational data, and analyzing the carbon emission growth trend within each carbon emission interval to generate a trend growth coefficient. Specifically, this includes: dividing historical carbon emission data into intervals based on the criteria of light, moderate, and heavy carbon emission levels; extracting real-time load data from the basic operational data within each interval; calculating the difference in cumulative carbon trajectory contribution values ​​between the first and last periods of each carbon emission interval to obtain the total carbon emission growth; dividing the total operating time within the carbon emission interval (the end time of the last period minus the start time of the first period) by the total carbon emission growth to obtain the average carbon emission growth rate; calculating the difference in real-time load between the first and last periods of the carbon emission interval to obtain the total load growth; dividing the total operating time of the carbon emission interval by the total load growth to obtain the average load growth rate; and multiplying the average carbon emission growth rate by the average load growth rate, and then multiplying by 0.3 times the urgency level base for the corresponding carbon emission interval to obtain the trend growth coefficient for that carbon emission interval.

[0118] Based on the exceedance magnitude of each carbon emission interval and critical point, the interval characteristic factor, and the trend growth coefficient, different weights are assigned to each carbon emission interval to obtain the dynamic weight value of each carbon emission interval. Specifically, this includes: calculating the exceedance magnitude of each carbon emission interval and critical point: subtracting the critical point from the midpoint value of the carbon emission interval and then dividing by the historical maximum exceedance magnitude of the carbon emission interval to obtain the standardized exceedance magnitude; multiplying the standardized exceedance magnitude, the interval characteristic factor, and the trend growth coefficient by 0.4, 0.3, and 0.3 respectively and then adding them together to obtain the dynamic weight value of the carbon emission interval.

[0119] The urgency level base value and dynamic weight value of each carbon emission interval are calculated to obtain a stepped carbon neutrality urgency index. Specifically, for each carbon emission interval, its urgency level base value is multiplied by the dynamic weight value to obtain the urgency index component of the carbon emission interval. The components are arranged in the order of light, moderate and heavy intervals to form the stepped carbon neutrality urgency index. The carbon neutrality urgency index increases with the level of the carbon emission interval and increases in a stepped manner.

[0120] By generating a tiered carbon neutrality urgency index through multi-dimensional calculations and accurately dividing carbon emission zones into light, moderate, and heavy categories, and dynamically adjusting weights by combining zone characteristic factors and trend growth coefficients, this approach breaks through the limitations of traditional single-threshold assessments. It quantifies the extent of exceedance and carbon emission trends in different zones, providing a clearly tiered urgency index for when health deficits exceed limits. This improves the responsiveness of long-term carbon neutrality planning to changes in carbon emissions due to aging of target entities. Based on in-depth analysis of the correlation and growth trends of carbon emission zones using big data, the tiered urgency index can better align with the full life-cycle characteristics of target entities, making carbon neutrality and management directives more targeted.

[0121] In one embodiment, the critical point is calculated based on the full-cycle carbon trajectory vector, the lifetime of the target object, and the energy efficiency rating in static data, including:

[0122] Using the lifespan of the target object as the time benchmark, and combining the rated power and grid benchmark carbon emissions per kilowatt-hour in the static data, a benchmark full-cycle carbon trajectory vector is generated. Specifically, this includes: using the lifespan of the target object as the total duration, dividing the total duration into benchmark time periods equal to the dynamic segment boundary values; multiplying the rated power of the target object by the grid benchmark carbon emissions per kilowatt-hour to obtain the benchmark slope value; multiplying the benchmark slope value by 1 to obtain the corrected time-segment slope value; multiplying the benchmark slope value by the duration of the benchmark time period (the duration of each benchmark time period itself) and then multiplying it by the grid benchmark carbon emissions per kilowatt-hour to obtain the cumulative carbon trajectory contribution value for each benchmark time period; and combining the benchmark slope value, the corrected slope value, and the cumulative carbon trajectory contribution value for each benchmark time period as an array in sequence to generate the benchmark full-cycle carbon trajectory vector.

[0123] The deviation threshold between the full-cycle carbon trajectory vector and the benchmark full-cycle carbon trajectory vector is calculated. The deviation threshold and the energy efficiency correction coefficient are calculated. Specifically, the absolute difference between the three elements of the full-cycle carbon trajectory vector and the benchmark full-cycle carbon trajectory vector for the corresponding time period is calculated. The three differences for each time period are added together to obtain the total deviation for the time period. The total deviation for all time periods is then added together and divided by the total number of time periods to obtain the average deviation threshold. The average deviation threshold is multiplied by the energy efficiency correction coefficient to obtain the critical initial point.

[0124] Based on the ratio of runtime to lifespan in static data, a lifespan degradation correction coefficient is determined. Specifically, this involves calculating the ratio of runtime to lifespan in static data to obtain the usage percentage. When the usage percentage is ≤30%, the lifespan degradation correction coefficient is 1.0; when 30% < usage percentage ≤70%, the lifespan degradation correction coefficient is 1.3; and when the usage percentage >70%, the lifespan degradation correction coefficient is 1.6. This coefficient increases with the increase of the target object's usage time, thereby quantifying the impact of lifespan degradation on the critical value.

[0125] The critical initial point is dynamically calibrated based on the lifetime decay correction factor to obtain the critical point. Specifically, the critical initial point is multiplied by the lifetime decay correction factor to obtain the critical point, and then the critical point is adjusted according to the aging degree of the target object.

[0126] By integrating the full-cycle carbon trajectory vector, the baseline carbon trajectory vector, and static parameters using big data technology, the critical point is accurately calculated. At the same time, the critical value is dynamically calibrated by combining the energy efficiency correction coefficient and the lifespan decay correction coefficient, so that it adapts to the aging degree of the target object. This multi-dimensional fusion calculation based on big data breaks through the limitations of traditional fixed critical values. It can accurately capture the dynamic changes in energy consumption decay and carbon emission increment throughout the entire life cycle of the target object, and improve the accuracy of long-term carbon neutrality planning in response to the aging trend of the target object.

[0127] In one embodiment, generating carbon neutrality and management instructions for a target object based on a carbon neutrality urgency index includes:

[0128] Based on the carbon neutrality urgency index and the basic operational data of the target, the measure suitability is generated. Specifically, this includes: calculating the deviation rate (the deviation of the actual value from the standard range divided by the width of the standard range) of current, voltage, vibration frequency, and real-time load in the basic operational data; multiplying current, voltage, vibration frequency, and real-time load by their corresponding weights of 0.2, 0.2, 0.3, and 0.3 respectively, and then summing them to obtain the operational deviation; multiplying the carbon neutrality urgency index of each carbon emission range by the operational deviation and then by 0.5 to obtain the measure suitability.

[0129] Based on the suitability of the measures and the carbon neutrality urgency index, carbon neutrality and management instructions for the target object are generated. Specifically, when the carbon neutrality urgency index is ≤2: if the suitability of the measures is ≤0.3, a first instruction is generated, which is to adjust the refrigerant pressure of the target object; if the suitability of the measures is >0.3, a second instruction is generated, which is to optimize the load rate of the target object.

[0130] When 2 < carbon neutrality urgency index ≤ 5: If the measure fit is ≤ 0.5, a third instruction is generated, which is: calibrate the vibration of the target object; if the measure fit is > 0.5, a fourth instruction is generated, which is: replace the bearing of the target object;

[0131] When the carbon neutrality urgency index is greater than 5, a fifth instruction is generated, which is: replace the target object.

[0132] By integrating the carbon neutrality urgency index with the basic operational data of target objects through big data technology, targeted management instructions are generated by calculating the suitability of measures. The operational deviation of target objects is accurately quantified, and management strategies at different levels are matched with a tiered urgency index. From adjusting refrigerant pressure to replacing target objects, instructions are dynamically upgraded as carbon emissions change due to the aging of target objects. This makes up for the shortcomings of traditional solutions in dynamically tracking energy consumption and carbon emissions throughout the entire life cycle, making carbon neutrality management more precise and efficient.

[0133] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A building energy consumption and carbon neutrality analysis system based on big data analytics, characterized in that, include: The data acquisition unit is used to collect basic operational and static data of target objects within the shopping mall in real time, analyze the basic operational and static data to obtain the operational degradation value of the target objects, including: The basic operational data and static data are processed to obtain the data standardization coefficient; Based on the data standardization coefficient, the dynamic deviation is obtained by comparing the basic operating data with the rated benchmark parameters in the static data. Analyze the manufacturing time, running time, and service life in static data to generate a time decay coefficient; The dynamic deviation, time decay coefficient, and data standardization coefficient are weighted and fused to obtain the operational degradation value; The target object is a large-scale refrigeration equipment; The analysis unit is used to obtain the real-time carbon emissions per kilowatt-hour of the current power grid, analyze the operating degradation value, the real-time carbon emissions per kilowatt-hour of the power grid and the rated power of the target object, and obtain the dynamic carbon trajectory slope, including: analyzing the degree of deviation between the operating degradation value and the rated power of the target object, and generating the actual power deviation coefficient; Based on the actual power offset coefficient and the real-time carbon emissions per kilowatt-hour of the power grid, the real-time change in carbon emissions per unit time is calculated to obtain the instantaneous carbon intensity factor. The degradation value is correlated with the instantaneous carbon intensity factor to generate a degradation carbon sensitivity coefficient; The slope of the dynamic carbon trajectory is obtained by calculating the actual power offset coefficient, instantaneous carbon intensity factor, and deteriorated carbon sensitivity coefficient. The computing unit is used to calculate the dynamic carbon trajectory slope and the runtime of the target object, and generate a full-cycle carbon trajectory vector. The deficit unit is used to analyze the full-cycle carbon trajectory vector and the lifetime of the target object to obtain the health deficit degree, including: analyzing the time-series matching relationship between the full-cycle carbon trajectory vector and the lifetime of the target object, and generating the carbon-time matching coefficient; Based on the full-cycle carbon trajectory vector, the cumulative carbon trajectory contribution value of each time period is accumulated to obtain the cumulative carbon emission load of the target object during its service life. The cumulative carbon emission load is combined with the lifespan of the target object to generate the lifespan carbon load rate; The health deficit degree is obtained by calculating the carbon time matching coefficient and lifetime carbon load rate; The judgment unit is used to generate a step-by-step carbon neutrality urgency index when the health deficit exceeds the critical point, including: calculating the critical point based on the full-cycle carbon trajectory vector, the lifespan of the target object and the energy efficiency label level in static data; Based on the critical point, the health deficit is divided into multiple continuous carbon emission intervals, generating different urgency levels for each interval; The correlation between the health deficit degree and the full-cycle carbon trajectory vector within each carbon emission interval is analyzed to generate interval characteristic factors; Obtain historical carbon emission data of the target object, combine historical carbon emission data with basic operational data, analyze the carbon emission growth trend within each carbon emission interval, and generate trend growth coefficients. Based on the exceedance range, range characteristic factor, and trend growth coefficient of each carbon emission range and critical point, different weights are assigned to each carbon emission range to obtain the dynamic weight value of each carbon emission range. The urgency level baseline and dynamic weight value of each carbon emission range are calculated to obtain a stepped carbon neutrality urgency index. The carbon neutrality and management unit is used to generate carbon neutrality and management instructions for target entities based on the carbon neutrality urgency index.

2. The building energy consumption and carbon neutrality analysis system based on big data analysis according to claim 1, characterized in that, The degradation value is correlated with the instantaneous carbon intensity factor to generate a degradation carbon sensitivity coefficient, including: The operational degradation value and instantaneous carbon intensity factor are extracted to generate a feature mapping matrix; Based on the feature mapping matrix, the dynamic correlation sequence between the running degradation value and the instantaneous carbon intensity factor is calculated; The energy efficiency label level in the static data is mapped to obtain the energy efficiency correction coefficient; The dynamic correlation series is fused with the energy efficiency correction coefficient to obtain the preliminary sensitivity coefficient; The initial sensitivity coefficient is calibrated based on the ambient temperature and humidity data from the basic operation data to generate the deterioration carbon sensitivity coefficient.

3. The building energy consumption and carbon neutrality analysis system based on big data analysis according to claim 1, characterized in that, The dynamic carbon trajectory slope and the runtime of the target object are calculated to generate a full-cycle carbon trajectory vector, including: The runtime of the target object is naturally segmented, and the slope of the dynamic carbon trajectory is truncated based on the natural segmentation to obtain the slope value of each time period; Calculate the slope difference between adjacent time periods, and combine it with the running time of the corresponding time period to obtain the slope change rate per unit time. Based on the bearing vibration frequency and voltage, the stability index of the target object is calculated, the correlation between the slope change rate per unit time and the stability index of the target object during that time period is analyzed, and the operation status correction coefficient is generated. The slope values ​​for each time period are calibrated based on the operation status correction coefficient to obtain the corrected time-period slope values; The cumulative carbon trajectory contribution value for each time period is obtained by calculating the corrected time-segment slope value and the running time of the corresponding time period. The slope values ​​of each time period, the corrected slope values ​​of each time period, and the cumulative carbon trajectory contribution value are fused to generate a full-cycle carbon trajectory vector.

4. The building energy consumption and carbon neutrality analysis system based on big data analysis according to claim 3, characterized in that, The runtime of the target object is naturally segmented, and the slope of the dynamic carbon trajectory is truncated based on the natural segmentation to obtain the slope value for each time period, including: Based on the number of start-stop cycles and ambient temperature and humidity in the basic operation data, the segment threshold of the runtime is determined, and dynamic segment boundary values ​​are generated. The runtime is divided into continuous time periods based on dynamic segmentation boundary values. The slope of the dynamic carbon trajectory in each time period is extracted synchronously to generate the original dataset of time period slope. Based on the current and voltage data in the basic operation data, the original dataset of slope values ​​for each time period is filtered to obtain the slope values ​​for each time period.

5. The building energy consumption and carbon neutrality analysis system based on big data analysis according to claim 4, characterized in that, Based on the full-cycle carbon trajectory vector, the target object's lifetime, and the energy efficiency rating in static data, the critical point is calculated, including: Using the lifespan of the target object as a time benchmark, and combining the rated power and the grid benchmark carbon emissions per kilowatt-hour in the static data, a benchmark full-cycle carbon trajectory vector is generated. The deviation threshold between the full-cycle carbon trajectory vector and the benchmark full-cycle carbon trajectory vector is calculated. The deviation threshold and the energy efficiency correction coefficient are then calculated to obtain the critical initial point. The lifespan degradation correction coefficient is determined based on the ratio of runtime to lifespan in static data. The critical initial point is dynamically calibrated based on the lifetime decay correction coefficient to obtain the critical point.

6. The building energy consumption and carbon neutrality analysis system based on big data analysis according to claim 5, characterized in that, Based on the carbon neutrality urgency index, generate carbon neutrality and management instructions for the target entities, including: Based on the carbon neutrality urgency index and the basic operational data of the target objects, measure suitability is generated. Based on the suitability of the measures and the carbon neutrality urgency index, carbon neutrality and management directives for the target entities are generated.

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