Building energy consumption and carbon neutralization analysis system based on big data analysis

Abstract

The invention 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, and is used for generating a carbon neutralization management instruction of a target object according to a carbon neutralization urgency index, performance degradation of a target object along with increase of service life is captured, an analysis unit combines real-time carbon emission data of a power grid, generates a dynamic carbon track slope and reflects a carbon emission change trend in real time, a calculation unit performs segment processing on operation duration based on big data, and a complete-cycle carbon track vector is constructed. A healthy deficit degree and a stepped urgency index are generated through big data analysis of the deficit unit and the judgment unit, and finally a management instruction is output by the management unit, so that the problem of insufficient tracking of full-life-cycle energy consumption attenuation and carbon emission increment of a target object by traditional analysis is effectively solved, and the accuracy of long-term carbon neutralization planning is greatly improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of building energy consumption analysis, and particularly relates to a building energy consumption and carbon neutralization analysis system based on big data analysis. BACKGROUND

[0002] At present, when building energy consumption and carbon neutralization are analyzed, a system usually collects various energy consumption data and environmental parameters through sensors, and analyzes the energy consumption data through a machine learning algorithm, so as to formulate an energy-saving and emission-reducing scheme and achieve the purpose of carbon neutralization.

[0003] However, the current analysis method still has significant defects in a shopping mall, specifically: since the equipment in the shopping mall is usually used for a long time, the energy consumption and carbon emissions of some equipment in the shopping mall, such as large refrigeration equipment, will change with the increase of the service life, and the current analysis method is insufficient in dynamic tracking and analyzing the energy consumption decay and carbon emission increment of the equipment in the whole life cycle when analyzing building energy consumption and carbon neutralization, which affects the accuracy of long-term carbon neutralization planning. SUMMARY

[0004] In view of the deficiencies of the prior art, the present application provides a building energy consumption and carbon neutralization analysis system based on big data analysis, which solves the above problems.

[0005] The above technical purpose of the present application is realized by the following technical scheme:

[0006] A building energy consumption and carbon neutralization analysis system based on big data analysis comprises:

[0007] A collection unit is configured to collect basic operation data and static data of a target object in a shopping mall in real time, analyze the basic operation data and the static data, and obtain an operation degradation value of the target object, wherein the target object is a large refrigeration equipment.

[0008] An analysis unit is configured to obtain real-time electricity carbon emissions of a current power grid, and analyze the operation degradation value, the real-time electricity carbon emissions of the power grid, and a rated power of the target object to obtain a dynamic carbon trajectory slope.

[0009] A calculation unit is configured to calculate the dynamic carbon trajectory slope and a running time of the target object, and generate a full-cycle carbon trajectory vector.

[0010] A deficit unit is configured to analyze the full-cycle carbon trajectory vector and a service life of the target object to obtain a health deficit degree.

[0011] A judgment unit is configured to generate a stepped carbon neutralization urgency index when the health deficit degree exceeds a critical point.

[0012] The carbon neutral management unit is configured to generate carbon neutral management instructions for the target object based on the carbon neutral urgency index.

[0013] Further, the base operation data and the static data are analyzed to obtain an operation degradation value of the target object, including:

[0014] The base operation data and the static data are processed to obtain a data standardization coefficient;

[0015] Based on the data standardization coefficient, the base operation data is compared with the rated reference parameter in the static data to obtain a dynamic deviation degree;

[0016] The time attenuation coefficient is generated by analyzing the factory time, operation time length and service life in the static data;

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

[0018] Further, the operation degradation value, the real-time carbon emission per kilowatt-hour of the power grid and the rated power of the target object are analyzed to obtain a dynamic carbon trajectory slope, including:

[0019] The actual power offset coefficient is generated by analyzing the deviation degree of the operation degradation value and the rated power of the target object;

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

[0021] The degradation carbon sensitivity coefficient is generated by correlating the operation degradation value and the instantaneous carbon intensity factor;

[0022] The actual power offset coefficient, the instantaneous carbon intensity factor and the degradation carbon sensitivity coefficient are calculated to obtain the dynamic carbon trajectory slope.

[0023] Further, the operation degradation value and the instantaneous carbon intensity factor are correlated to generate a degradation carbon sensitivity coefficient, including:

[0024] The feature mapping matrix is generated by extracting the operation degradation value and the instantaneous carbon intensity factor;

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

[0026] The energy efficiency correction coefficient is obtained by mapping the energy efficiency identification level in the static data;

[0027] The dynamic correlation degree sequence and the energy efficiency correction coefficient are fused to obtain a preliminary sensitivity coefficient;

[0028] The preliminary sensitivity coefficient is calibrated according to the environment temperature and humidity in the basic operation data, and a deteriorated carbon sensitivity coefficient is generated.

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

[0030] The operation duration of the target object is naturally segmented, and the dynamic carbon trajectory slope is intercepted based on the natural segmentation to obtain slope values of each period;

[0031] The slope difference of adjacent periods is calculated, and combined with the operation duration of the corresponding period, the slope change rate per unit time is obtained;

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

[0033] The slope values of each period are calibrated according to the operation state correction coefficient to obtain corrected sub-period slope values;

[0034] The corrected sub-period slope values and the operation duration of the corresponding period are calculated to obtain the cumulative carbon trajectory contribution value of each period;

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

[0036] Further, the operation duration of the target object is naturally segmented, and the dynamic carbon trajectory slope is intercepted based on the natural segmentation to obtain slope values of each period, including:

[0037] Based on the start-stop times and the environment temperature and humidity in the basic operation data, the segmentation threshold of the operation duration is determined, and a dynamic segmentation boundary value is generated;

[0038] According to the dynamic segmentation boundary value, the operation duration is divided into continuous periods, and the dynamic carbon trajectory slope in each period is intercepted synchronously to generate a period slope original data set;

[0039] Based on the current and voltage in the basic operation data, the period slope original data set is screened to obtain the slope values of each period.

[0040] Further, the full-cycle carbon trajectory vector and the service life of the target object are analyzed to obtain a health deficit, including:

[0041] The time sequence matching relationship between the full-cycle carbon trajectory vector and the service life of the target object is analyzed to generate a carbon time matching coefficient;

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

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

[0044] The carbon time matching coefficient and the life carbon load rate are calculated to obtain the health deficit degree.

[0045] Further, when the health deficit degree exceeds the critical point, a step-by-step carbon neutralization urgency index is generated, including:

[0046] Based on the full-cycle carbon trajectory vector, the service life of the target object, and the energy efficiency identification level in the static data, the critical point is calculated;

[0047] Taking the critical point as a reference, the health deficit degree is divided into multiple continuous carbon emission intervals to generate different urgency level bases corresponding to each interval;

[0048] Analyze the relevance of the health deficit degree and the full-cycle carbon trajectory vector in each carbon emission interval to generate an interval characteristic factor;

[0049] Obtain the historical carbon emission data of the target object, combine the historical carbon emission data and the basic operation data, and analyze the carbon emission growth trend in each carbon emission interval to generate a trend growth coefficient;

[0050] According to the exceeding range of each carbon emission interval and the 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;

[0051] The urgency level base and the dynamic weight value of each carbon emission interval are calculated to obtain a step-by-step carbon neutralization urgency index.

[0052] Further, based on the full-cycle carbon trajectory vector, the service life of the target object, and the energy efficiency identification level in the static data, the critical point is calculated, including:

[0053] Taking the service life of the target object as a time reference, combining the rated power in the static data and the grid reference degree carbon emission amount, a reference full-cycle carbon trajectory vector is generated;

[0054] The deviation threshold of the full-cycle carbon trajectory vector and the reference full-cycle carbon trajectory vector is calculated, and the deviation threshold and the energy efficiency correction coefficient are calculated to obtain the critical initial point;

[0055] Based on the ratio of the running time in the static data to the service life, a life attenuation correction coefficient is determined;

[0056] According to the life attenuation correction coefficient, the critical initial point is dynamically calibrated to obtain the critical point.

[0057] Further, according to the carbon neutralization urgency index, the carbon neutralization management instruction of the target object is generated, comprising:

[0058] Based on the carbon neutralization urgency index and the basic operation data of the target object, the measure adaptation degree is generated;

[0059] According to the measure adaptation degree and the carbon neutralization urgency index, the carbon neutralization management instruction of the target object is generated.

[0060] In summary, the present application mainly has the following beneficial effects:

[0061] Through real-time aggregation of basic operation data and static data of large refrigeration equipment by big data, and standardization processing and multi-dimensional comparison of the two types of data, dynamic deviation degree and time decay coefficient are generated, and finally precise operation degradation value is obtained through weighted fusion. This degradation evaluation method based on big data breaks through the limitations of traditional manual inspection or single parameter judgment, and can capture the performance degradation of large refrigeration equipment caused by aging and environmental changes in real time, which is convenient for subsequent carbon neutralization analysis and management of large refrigeration equipment.

[0062] Through analysis unit integration of real-time electricity carbon emission of power grid, rated power and operation degradation value of target object and other dynamic data, actual power offset coefficient and instantaneous carbon intensity factor are generated by big data analysis, and then dynamic carbon trajectory slope is constructed. Combined with the natural segmentation of the running time by the calculation unit and the slope calibration, the full-cycle carbon trajectory vector is formed, which completely presents the carbon emission accumulation process of the target object from commissioning to scrap. 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 master the carbon footprint change of the target object in the whole life cycle through the correlation mining of historical data and real-time data.

[0063] Through the big data driven stepwise management mechanism, the precision and timeliness of carbon neutralization measures are improved. Through big data analysis of the deficit unit and the judgment unit, the full-cycle carbon trajectory vector is associated with the service life of the target object to generate a healthy deficit degree, and a stepwise carbon neutralization urgency index is automatically generated when it exceeds the standard. The historical carbon emission trend, environmental parameter change and energy efficiency grade and other multi-dimensional factors are comprehensively considered, different weights are matched for different urgency levels, and the carbon neutralization management unit can output targeted instructions. Compared with the traditional carbon neutralization analysis scheme, this stepwise management scheme based on big data can implement different carbon neutralization management schemes when the carbon emission exceeds the standard, which improves the efficiency of carbon neutralization analysis and management of the mall building. BRIEF DESCRIPTION OF DRAWINGS

[0064] Figure 1 It is a building energy consumption and carbon neutralization analysis system based on big data analysis of the present application. DETAILED DESCRIPTION

[0065] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative work fall within the protection scope of the present application.

[0066] Reference Figure 1 The system comprises:

[0067] The acquisition unit is configured to acquire, in real time, basic operation data and static data of a target object in a shopping mall, analyze the basic operation data and the static data, and obtain an operation degradation value of the target object, wherein the target object is a large refrigeration equipment, the basic operation data includes current, voltage, bearing vibration frequency, start-stop times, refrigerant pressure, environmental temperature and humidity, real-time load, and the static data includes factory time, equipment model, operation duration, service life, rated power, energy efficiency label grade, etc.

[0068] The analysis unit is configured to obtain real-time electricity carbon emission of a current power grid, analyze the operation degradation value, the real-time electricity carbon emission of the power grid, and the rated power of the target object, and obtain a dynamic carbon trajectory slope.

[0069] The calculation unit is configured to calculate the dynamic carbon trajectory slope and the operation duration of the target object, and generate a full-cycle carbon trajectory vector.

[0070] The deficit unit is configured to analyze the full-cycle carbon trajectory vector and the service life of the target object, and obtain a health deficit degree.

[0071] The judgment unit is configured to generate a step-by-step carbon neutralization urgency index when the health deficit degree exceeds a critical point.

[0072] The carbon neutralization management unit is configured to generate a carbon neutralization management instruction of the target object according to the carbon neutralization urgency index.

[0073] The real-time operation data and static parameters of the large refrigeration equipment in the shopping mall are integrated by the big data technology, the full-cycle carbon trajectory vector is constructed, the dynamic tracking of the energy consumption attenuation and the carbon emission increment of the large refrigeration equipment in the whole life cycle is realized, the big data analysis capability can process massive sensor data in real time, the energy consumption characteristics of the large refrigeration equipment changing with the service life are accurately captured, the dynamic carbon trajectory slope is generated combined with the real-time carbon emission data of the power grid, the defects of the traditional analysis in the long-term dynamic change tracking are made up, the accurate data support is provided for the long-term carbon neutralization planning, and the planning accuracy is improved.

[0074] Through in-depth analysis of the full-cycle carbon trajectory vector and the health deficit degree of the target object, a stepped carbon neutralization urgency index can be generated when the health deficit degree of the target object exceeds the limit. The stepped carbon neutralization urgency index relies on big data to comprehensively operate multidimensional parameters, can trigger targeted carbon neutralization management instructions, realizes the strategy of optimizing the energy consumption management of the target object, effectively reduces the carbon emission increment caused by the aging of the target object, and improves the accuracy and efficiency of long-term carbon neutralization management of the mall building.

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

[0076] The base operation data and the static data are processed to obtain a data standardization coefficient, specifically including: obtaining the standard range of parameters in the preprocessed base operation data and the standard value of parameters in the preprocessed static data, the standard range and the standard value being the reasonable range of the parameters when the target object is normally operated, calculating the deviation degree of the actual value of each parameter in the base operation data from the standard range; calculating the difference between the actual value of each parameter in the static data and the standard value, and taking the difference as the deviation degree; assigning a weight 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, multiplying the deviation degree of each parameter by its corresponding weight to obtain the weighted deviation value of each parameter, and then adding all the weighted deviation values and dividing the sum by the total number of parameters to obtain the data standardization coefficient;

[0077] Based on the data standardization coefficient, the rated reference parameters in the base operation data and the static data are compared to obtain a dynamic deviation degree, specifically including: dividing the parameters of the base operation data and the static data, the division basis being: if the parameter is higher, it is better for the target object, then it is divided into a positive index (such as energy efficiency level), if the parameter is lower, it is better for the target object, then it is divided into a negative index (such as vibration frequency); for the positive index, subtract the data standardization coefficient from 1 and then multiply by the deviation degree to obtain the adjusted deviation value; for the positive index, add the data standardization coefficient to 1 and then multiply by the deviation degree to obtain the adjusted deviation value; calculating the mean of the adjusted deviation values of all parameters, and the mean is the dynamic deviation degree;

[0078] The factory time, running time and service life in the static data are analyzed to generate a time attenuation coefficient, specifically including: determining a basic attenuation ratio based on the factory time of the target object: when the factory time is greater than 10 years, the basic attenuation ratio is 0.3, when 5≤factory time<10 years, the basic attenuation ratio is 0.2, and when the factory time is less than 5 years, the basic attenuation ratio is 0.1; calculating the ratio of the running time of the target object to its service life and multiplying it by 100 to obtain a percentage; when the percentage is less than 30% (representing that the target object is relatively new), the basic attenuation ratio+0.1, when the percentage is between 30%-70% (representing that the target object has been used for a period of time), the basic attenuation ratio+0.3; when the percentage is more than 70% (representing that the target object is already old), the basic attenuation ratio+0.5; finally, adding all the basic attenuation ratios to obtain the total sum, which is the time attenuation coefficient;

[0079] The dynamic deviation degree, time attenuation coefficient and data standardization coefficient are weighted and fused to obtain a running degradation value, specifically including: setting the weights of the dynamic deviation degree, time attenuation coefficient and standardization coefficient as 0.4, 0.4 and 0.2 respectively, multiplying the dynamic deviation degree, time attenuation coefficient and standardization coefficient by the corresponding weights and adding them to obtain the running degradation value, wherein the dynamic deviation degree directly reflects the deviation of the current running parameter of the target object from the standard range, and is used to reflect the real-time degradation state, so the weight is relatively high, which is 0.4; the time attenuation coefficient reflects the aging accumulation of the target object with the use time, and is the core factor of long-term degradation, so the weight is relatively high, which is 0.4; and the standardization coefficient is a basic adjustment factor for data processing, mainly for auxiliary correction of deviation, so the weight is relatively low, which is 0.2.

[0080] Through big data integration of multi-dimensional data of large refrigeration equipment, through standardized processing and dynamic deviation degree calculation, the running degradation value of large refrigeration equipment is accurately quantified, and combined with the time attenuation coefficient, the aging law of large refrigeration equipment with the increase of years is captured, the dynamic tracking of the energy consumption attenuation and the carbon emission increment in the whole life cycle is realized, compared with the traditional way, the limitation of insufficient long-term dynamic change analysis is broken through, more accurate data support is provided for long-term carbon neutral planning, at the same time, through the deep analysis of multiple parameters such as running degradation value, dynamic carbon trajectory and health deficit degree are generated, and when the limit is exceeded, the step-by-step carbon neutral management is triggered, based on the multi-dimensional data integration capability of big data, the carbon neutral management is more targeted and time-effective, through real-time adjustment strategy, the energy consumption of large refrigeration equipment is effectively optimized, the carbon emission increment is reduced, and the efficiency of long-term carbon neutral management of the mall is significantly improved.

[0081] In one case of the embodiment, the running degradation value, the real-time degree carbon emission of the power grid and the rated power of the target object are analyzed to obtain a dynamic carbon trajectory slope, including:

[0082] The running degradation value is analyzed to deviate from the rated power of the target object, and an actual power offset coefficient is generated. Specifically, the rated power of the target object is taken as a reference value, and the running degradation value is taken as a correction coefficient. The actual power is equal to the rated power multiplied by (1+running degradation value). The difference between the actual power and the rated power is calculated to obtain an absolute deviation amount. The absolute deviation amount is divided by the rated power to obtain the actual power offset coefficient. The actual power offset coefficient is used to quantify the power deviation caused by the running degradation.

[0083] Based on the actual power offset coefficient and the real-time carbon emission per kilowatt-hour of the power grid, the real-time change range of carbon emission per unit time is calculated to obtain an instantaneous carbon intensity factor. Specifically, the rated power is multiplied by the real-time carbon emission per kilowatt-hour of the power grid to obtain a baseline carbon emission per unit time of the target object. Then, the rated power multiplied by (1+actual power offset coefficient) multiplied by the real-time carbon emission per kilowatt-hour of the power grid is obtained to obtain the actual carbon emission per unit time of the target object under the current degradation state. The actual carbon emission is subtracted from the baseline carbon emission, and then divided by the baseline carbon emission to obtain a real-time change rate of carbon emission. The real-time change rate is added to 1 to obtain the instantaneous carbon intensity factor.

[0084] The running degradation value is associated with the instantaneous carbon intensity factor to generate a degradation carbon sensitivity coefficient.

[0085] The actual power offset coefficient, the instantaneous carbon intensity factor, and the degradation carbon sensitivity coefficient are calculated to obtain a dynamic carbon trajectory slope. Specifically, the actual power offset coefficient, the instantaneous carbon intensity factor, and the degradation carbon sensitivity coefficient are respectively assigned equal weights, and the sum of the weights is 1. The actual power offset coefficient, the instantaneous carbon intensity factor, and the degradation carbon sensitivity coefficient are respectively multiplied by their corresponding weights and added to obtain the dynamic carbon trajectory slope.

[0086] Through big data integration of running degradation values, real-time carbon emissions of the power grid, rated power, and other multi-source data, parameters such as the actual power offset coefficient and the instantaneous carbon intensity factor are calculated to generate the dynamic carbon trajectory slope. The dynamic carbon trajectory slope accurately captures the carbon emission dynamics of large refrigeration equipment as the service life changes, breaks through the limitations of traditional analysis of tracking the incremental carbon emissions of the whole life cycle, improves the planning accuracy, and through the weighted fusion of related parameters of the dynamic carbon trajectory slope, the correlation between the degradation of large refrigeration equipment and carbon emissions can be quantified. Based on the multi-dimensional analysis capability of big data, the dynamic carbon trajectory slope can reflect the current and long-term carbon emission trend of large refrigeration equipment in real time, so as to discover carbon emission abnormalities earlier, optimize carbon neutral management strategies, and improve the long-term carbon neutral management efficiency of shopping malls.

[0087] In one case of the embodiment, the running degradation value is associated with the instantaneous carbon intensity factor to generate a degradation carbon sensitivity coefficient, including:

[0088] The operation degradation value and the instantaneous carbon intensity factor are extracted to generate a feature mapping matrix, specifically including: taking the operation degradation value and the instantaneous carbon intensity factor as feature dimensions, extracting real-time values of the two according to time sequence, taking the operation degradation value at each time point as the first column element, and taking the instantaneous carbon intensity factor at the corresponding time point as the second column element, forming a rectangular data array composed of rows (time dimension) and columns (feature dimension), and the array is the feature mapping matrix;

[0089] Based on the feature mapping matrix, a dynamic correlation degree sequence between the operation degradation value and the instantaneous carbon intensity factor is calculated, specifically including: standardizing the data in the column of the operation degradation value and the column of the instantaneous carbon intensity factor in the feature mapping matrix respectively to eliminate the influence of different dimensions, and then dividing the two columns of standardized data into a plurality of overlapping sliding windows according to time sequence, each window containing the same number of continuous time point data, for each sliding window, calculating the local correlation degree of the operation degradation value and the instantaneous carbon intensity factor in the window: subtracting the mean value of the operation degradation value in the window from each data of the operation degradation value in the window to obtain the deviation of each operation degradation value, and then subtracting the mean value of the instantaneous carbon intensity factor in the window from each data of the instantaneous carbon intensity factor in the window to obtain the deviation of each instantaneous carbon intensity factor, multiplying the two deviations at the corresponding time point and then adding them to obtain the total sum of deviation products; calculating the sum of squares of all deviations of the operation degradation value and the sum of squares of all deviations of the instantaneous carbon intensity factor, multiplying the two sums of squares and taking the square root; dividing the total sum of deviations by the square root to obtain the local correlation degree of the window; arranging the local correlation degrees of all windows in time sequence to form a sequence, which is the dynamic correlation degree sequence;

[0090] The energy efficiency identification level in the static data is mapped to obtain an energy efficiency correction coefficient, specifically including: sorting the energy efficiency identification levels from high to low, when the energy efficiency identification level is the highest, the energy efficiency correction coefficient is 1.0, and the energy efficiency correction coefficient decreases by 0.1 for each level decrease, and when the energy efficiency identification level is the lowest, the energy efficiency correction coefficient is 0.5;

[0091] The dynamic correlation degree sequence and the energy efficiency correction coefficient are fused to obtain a preliminary sensitive coefficient, specifically including: 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; calculating the average value of all corrected correlation degrees to obtain the preliminary sensitive coefficient;

[0092] The preliminary sensitivity coefficient is calibrated according to the environment temperature and humidity in the basic operation data, and a degradation carbon sensitivity coefficient is generated, specifically including: taking the midpoint of the standard interval of the environment temperature and humidity as a standard central value; calculating the difference between the actual temperature and humidity value and the standard central value to obtain a temperature and humidity deviation; multiplying the temperature and humidity deviation by 0.02 to obtain an adjusted calibration coefficient; multiplying the preliminary sensitivity coefficient by the adjusted calibration coefficient to obtain the degradation carbon sensitivity coefficient.

[0093] The operation degradation value and the instantaneous carbon intensity factor are processed in multiple dimensions through big data technology to generate a feature mapping matrix and a dynamic correlation sequence. Big data can efficiently integrate massive feature data in the time dimension, accurately capture the dynamic correlation law of the two with the service life of the target object, and combine the energy efficiency correction coefficient and the environment temperature and humidity calibration to generate a degradation carbon sensitivity coefficient that can quantify the sensitivity of the target object to the influence of degradation on carbon emissions, making up for the defects of the traditional analysis of the correlation between the characteristics of the target object and carbon emissions. The degradation carbon sensitivity coefficient can dynamically adapt to the state changes of the target object throughout its life cycle, clearly reflecting the response strength of carbon emissions to degradation in the aging process of the target object, quickly identifying abnormal trends in carbon emissions, and improving the accuracy of long-term carbon neutralization planning.

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

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

[0096] The slope difference value of adjacent periods is calculated, and the unit time slope change rate is obtained in combination with the operation time of the corresponding period, specifically including: subtracting the slope value of the previous period from the slope value of the next period of the two adjacent periods to obtain the slope difference value of the two adjacent periods; calculating the total operation time of the two adjacent periods, and dividing the slope difference value by the total operation time to obtain the unit time slope change rate;

[0097] The target object stability index is calculated based on the bearing vibration frequency and voltage, the correlation between the unit time slope change rate and the target object stability index of the period is analyzed, and an operation state correction coefficient is generated, specifically including: calculating the absolute deviation of the actual value of the bearing vibration frequency and voltage from the standard range center value, and then dividing the absolute deviation by the standard range width (the difference between the upper limit value and the lower limit value of the standard interval) respectively, i.e. the vibration deviation rate and the voltage deviation rate; multiplying the vibration deviation rate by 0.6 and adding the voltage deviation rate multiplied by 0.4, i.e. the target object stability index; dividing the unit time slope change rate by the stability index to obtain the operation state correction coefficient;

[0098] The slope value of each period is calibrated according to the operation state correction coefficient to obtain a corrected sub-period slope value, specifically including: multiplying the operation state correction coefficient corresponding to each period by the slope value of the period, and the result obtained is the corrected sub-period slope value, so as to calibrate the influence of the running stability of the target object on the carbon trajectory slope, and ensure that the slope value of each period can truly reflect the change of the carbon emission trajectory;

[0099] The corrected sub-period slope value and the operation time length of the corresponding period are calculated to obtain the cumulative carbon trajectory contribution value of each period, specifically including: for each period, multiplying the corrected sub-period slope value by the operation time length of the period to obtain the basic contribution value of the period; and then multiplying the basic contribution value by the average real-time degree carbon emission of the period to obtain the cumulative carbon trajectory contribution value of each period;

[0100] The slope value of each period, the corrected sub-period slope value and the cumulative carbon trajectory contribution value are fused to generate a full-cycle carbon trajectory vector, specifically including: in time sequence, the slope value, the corrected sub-period slope value and the cumulative carbon trajectory contribution value of each period are sequentially taken as three-dimensional elements of the vector to form a three-dimensional array; all the three-dimensional arrays of the periods are sorted and combined according to time to form a sequence, which is the full-cycle carbon trajectory vector, and the multi-dimensional time sequence characteristics of the carbon trajectory are completely presented.

[0101] By integrating the multi-period slope value, the corrected slope value and the cumulative carbon trajectory contribution value of the target object in the full cycle through big data technology, a full-cycle carbon trajectory vector is generated, the time sequence characteristics of the carbon trajectory are presented through multi-dimensional fusion, the dynamic carbon emission of the target object with the change of the service life is accurately captured, the influence of the bearing vibration frequency, voltage and other parameters on the carbon trajectory is analyzed in real time, the operation state correction coefficient is generated, the problem of insufficient dynamic tracking of the full-cycle carbon emission trajectory in traditional analysis is solved, and the dynamic carbon trajectory slope and the operation time length are deeply calculated by using big data, the cumulative carbon trajectory contribution value of each period is generated, the stability of the target object, the carbon emission of the power grid and other factors are included in the carbon trajectory vector, the change law of the full-cycle carbon emission is completely restored, and then the system can accurately identify the carbon emission increment trend of the target object in the aging process, and the defects of insufficient correlation tracking of the long-term energy consumption decay and carbon emission of the target object in traditional analysis are solved.

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

[0103] The segmentation threshold of the running time is determined based on the start-stop times and the environmental temperature and humidity in the basic running data, and a dynamic segmentation boundary value is generated, specifically including: dividing the daily average start-stop times of the target object by the reasonable daily average start-stop times corresponding to the target object to obtain a start-stop influence coefficient; dividing the difference between the actual temperature and humidity and the standard center value by the width of the standard range of the environmental temperature and humidity (upper limit - lower limit) to obtain a temperature and humidity deviation rate; adding the temperature and humidity deviation rates and dividing by 2 to obtain an environmental influence coefficient; weighting and summing the start-stop influence coefficient (weight 0.6) and the environmental influence coefficient (weight 0.4) to obtain a comprehensive segmentation factor; when the comprehensive segmentation factor is ≤0.3, the running time is segmented by 24 hours; when 0.3< the comprehensive segmentation factor ≤0.7, the running time is segmented by 12 hours; and when the comprehensive segmentation factor is >0.7, the running time is segmented by 6 hours, and the start and end times of the corresponding period are the dynamic segmentation boundary values.

[0104] The running time is divided into continuous periods according to the dynamic segmentation boundary values, and the dynamic carbon trajectory slope in each period is synchronously intercepted to generate a period slope original data set, specifically including: according to the time sequence of the dynamic segmentation boundary values, dividing the continuous periods with the boundary values as nodes from the starting time of the target object, ensuring that the periods are non-overlapping and cover the entire running time, extracting the collection time stamp of the dynamic carbon trajectory slope in each period, and comparing it with the start and end time of the period, screening out the dynamic carbon trajectory slope falling within the period, collecting the dynamic carbon trajectory slope according to the period number, and forming a period slope original data set containing the period identifier and the corresponding slope sequence.

[0105] The period slope original data set is screened based on the current and voltage in the basic running data to obtain the slope value of each period, specifically including: determining the standard range of the current and voltage (the reasonable range when the target object is normally running), 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 period slope original data set, subtracting the current standard center value and the voltage standard center value from the actual current value and the actual voltage value at the corresponding time respectively to obtain the difference values of the current and voltage, and then dividing the difference values of the current and voltage by the current standard range width and the voltage standard range width respectively to obtain the deviation rates of the current and voltage; setting the deviation rate threshold to 0.1, if the current deviation rate or the voltage deviation rate of the slope data exceeds 0.1, the data is rejected, and for all the remaining slope data that has not been rejected, the sum of the values is divided by the total number of data to obtain the slope value of each period.

[0106] Through big data analysis of the start-stop times of large refrigeration equipment in the shopping mall, real-time data such as environmental temperature and humidity, dynamic segmented boundary values are generated to realize accurate and natural segmentation of the running time, and the segmentation granularity is dynamically adjusted through comprehensive segmentation factors to avoid the limitations of traditional fixed segmentation. At the same time, relying on big data for rapid screening of massive current and voltage data, abnormal slope data can be eliminated to ensure that the slope values of each period are real and reliable, improving the accuracy of long-term carbon emission dynamic tracking. The segmentation and screening of the dynamic carbon trajectory slope can optimize the data collection method in combination with the real-time running state of large refrigeration equipment, providing more analysis basis for long-term carbon neutral planning that is more in line with the actual running state of large refrigeration equipment, and ensuring the accuracy of management.

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

[0108] The time sequence matching relationship between the full-cycle carbon trajectory vector and the service life of the target object is analyzed to generate a carbon time matching coefficient, specifically including: based on the same division method of the running time, the service life of the target object is divided into an equal number of life periods to ensure that the periods corresponding to the full-cycle carbon trajectory vector and the life periods correspond one by one, the proportion of the cumulative contribution value of each period corresponding to the full-cycle carbon trajectory vector to the total cumulative contribution value is calculated to obtain a carbon contribution proportion; the proportion of the running time of each period to the total service life of the target object is calculated to obtain a time proportion; for each period, the carbon contribution proportion is divided by the time proportion to obtain a single-period matching coefficient; the single-period matching coefficients are weighted and summed with the proportion of the running time of each period to the total running time as the weight to obtain the carbon time matching coefficient, wherein the closer the carbon time matching coefficient is to 1, the higher the time sequence matching degree of the carbon trajectory change and the life consumption of the target object is;

[0109] Based on the full-cycle carbon trajectory vector, the cumulative carbon trajectory contribution values of each period are accumulated to obtain the cumulative carbon emission load of the target object within the service life, specifically including: based on the full-cycle carbon trajectory vector, the cumulative carbon trajectory contribution values of each period are extracted, and all the cumulative carbon trajectory contribution values of the periods are sequentially added in time sequence, and the sum obtained is the cumulative carbon emission load of the target object within the service life;

[0110] The accumulated carbon emission load is combined with the service life of the target object to generate a life carbon load rate, specifically including: dividing the accumulated carbon emission load of the target object within the service life by the total service life to obtain the carbon emission load per unit life length, and dividing the carbon emission load per unit life length by the unit life reference carbon load corresponding to the rated power of the target object, so as to obtain the life carbon load rate, and then the matching relationship between the carbon emission load and the life of the target object can be quantified; wherein, the calculation process of the unit life reference carbon load corresponding to the rated power of the target object is as follows: the corresponding reference carbon emission intensity (i.e. the carbon emission amount per unit power per unit time of the target object under standard working conditions) is obtained according to the model of the target object, the reference carbon emission intensity is multiplied by the rated power of the target object to obtain the reference carbon emission amount per unit time, the reference carbon emission amount per unit time is multiplied by the total service life of the target object to obtain the total life reference carbon load, and then the total life reference carbon load is divided by the total service life, which is the unit life reference carbon load;

[0111] The carbon time matching coefficient and the life carbon load rate are calculated to obtain a health deficit degree, specifically including: subtracting the absolute value of the carbon time matching coefficient from 1 to obtain a time sequence matching deviation value, subtracting 1 from the life carbon load rate to obtain a load exceeding standard deviation value, multiplying the time sequence matching deviation value by 0.3 and adding the load exceeding standard deviation value multiplied by 0.7 to obtain the health deficit degree. The greater the health deficit degree, the more serious the imbalance between the health and carbon emission of the target object.

[0112] The carbon time matching coefficient accurately quantifies the time sequence matching degree of carbon trajectory change and life consumption, and based on big data, the massive time period accumulated carbon contribution value and life segmented data are efficiently processed, the life carbon load rate is calculated combined with multi-dimensional parameters, and the correlation between the carbon emission load and the life of the target object is clearly presented. This comprehensive analysis based on big data breaks through the limitations of traditional methods in dynamically tracking the correlation between full-cycle energy consumption and carbon emission, provides data support for health deficit degree evaluation, and the deep integration of the carbon time matching coefficient and the life carbon load rate generates a health deficit degree that can intuitively reflect the imbalance between the health and carbon emission of the target object. The time sequence matching deviation and the load exceeding standard deviation are integrated according to the weight, accurately capturing the carbon emission anomaly of the target object as the service life increases, making up for the shortcomings of traditional analysis in dynamically tracking the carbon emission increment of the target object throughout its life cycle, and ensuring the effectiveness of long-term carbon neutral management.

[0113] In one case of the embodiment, when the health deficit degree exceeds a critical point, a stepwise carbon neutral urgency index is generated, including:

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

[0115] The health deficit is divided into a plurality of continuous carbon emission intervals based on the critical point, and different urgency level bases corresponding to each interval are generated, specifically including: taking the critical point as a reference value, calculating the difference between the historical maximum health deficit of the target object and the reference value, and dividing the difference into three equal parts as interval values; the range from the reference value to the reference value + 1 interval value is the mild over-standard interval, and the corresponding urgency level base is 1; the range from the reference value + 1 interval value to the reference value + 2 interval value is the moderate over-standard interval, and the corresponding urgency level base is 2; the range from the reference value + 2 interval value and above is the severe over-standard interval, and the corresponding urgency level base is 3, wherein each interval is continuous and has no overlap, covering all carbon emission intervals;

[0116] The correlation between the health deficit and the full-cycle carbon trajectory vector in each carbon emission interval is analyzed to generate an interval characteristic factor, specifically including: in each carbon emission interval, calculating the average of all health deficits in the interval to obtain an interval deficit average; calculating the average of the cumulative carbon trajectory contribution values in the full-cycle carbon trajectory vector in the corresponding period to obtain an interval contribution average; dividing the interval deficit average by the interval contribution average, and multiplying by 0.5 times the urgency level base of the interval to obtain the interval characteristic factor;

[0117] The historical carbon emission data of the target object is obtained, the historical carbon emission data and the basic operation data are combined, and the carbon emission growth trend in each carbon emission interval is analyzed to generate a trend growth coefficient, specifically including: dividing the historical carbon emission data into the corresponding interval according to the division of the carbon emission interval and the division standard of the mild, moderate and severe carbon emission interval, and extracting the real-time load data in the basic operation data in each interval; for each carbon emission interval, calculate the difference between the cumulative carbon trajectory contribution values in the first and last time intervals in the interval to obtain the total carbon emission growth; divide the total running time (the end time of the last time interval minus the start time of the first time interval) in the carbon emission interval by the total carbon emission growth to obtain the average carbon emission growth rate; calculate the difference between the real-time loads at the beginning and end of the carbon emission interval to obtain the total load growth; divide the total running time of the carbon emission interval by the total load growth to obtain the average load growth rate; multiply the average carbon emission growth rate by the average load growth rate, and multiply by 0.3 times the urgency level base corresponding to the carbon emission interval to obtain the trend growth coefficient of the carbon emission interval;

[0118] According to the exceeding amplitude of each carbon emission interval and the critical point, the interval characteristic factor and the trend growth coefficient, different weights are given to each carbon emission interval to obtain the dynamic weight value of each carbon emission interval, which specifically includes: calculating the exceeding amplitude of each carbon emission interval and the critical point: subtracting the critical point from the midpoint value of the carbon emission interval and then dividing by the historical maximum exceeding amplitude of the carbon emission interval to obtain the standardized exceeding amplitude, adding the standardized exceeding amplitude, the interval characteristic factor and the trend growth coefficient multiplied by 0.4, 0.3 and 0.3 respectively to obtain the dynamic weight value of the carbon emission interval;

[0119] The base number of the urgency level of each carbon emission interval and the dynamic weight value are calculated to obtain a ladder type carbon neutralization urgency index, which specifically includes: for each carbon emission interval, multiplying the base number of the urgency level by the dynamic weight value to obtain the urgency index component of the carbon emission interval, arranging the components in order according to the order of mild, moderate and severe intervals, and the sequence formed is the ladder type carbon neutralization urgency index, wherein the carbon neutralization urgency index increases with the level of the carbon emission interval and the overall is ladder type increasing.

[0120] The ladder type carbon neutralization urgency index is generated by multi-dimensional calculation, and the mild, moderate and severe carbon emission intervals are accurately divided. The weight is dynamically adjusted combined with the interval characteristic factor and the trend growth coefficient, which breaks through the limitation of traditional single threshold evaluation, and then quantifies the exceeding amplitude and carbon emission trend of different intervals, provides a clear urgency index when the health deficit exceeds the limit, improves the response accuracy of long-term carbon neutralization planning to the aging carbon emission change of the target object, and based on the deep analysis of the correlation and growth trend of the carbon emission interval by big data, the ladder type urgency index is more suitable for the whole life cycle characteristics of the target object, and the carbon neutralization management instruction is more targeted.

[0121] In one case of the embodiment, the critical point is calculated based on the whole cycle carbon trajectory vector, the service life of the target object and the energy efficiency identification level in the static data, including:

[0122] Taking the service life of the target object as the time reference, the rated power in the static data and the grid reference degree electric carbon emission amount are combined to generate a reference whole cycle carbon trajectory vector, which specifically includes: taking the service life of the target object as the total time, dividing the total time into reference time periods equal to the dynamic segmentation boundary value, multiplying the rated power of the target object by the grid reference degree electric carbon emission amount to obtain a reference slope value; multiplying the reference slope value by 1 to obtain a modified sub-period slope value; multiplying the reference slope value by the reference time period length (the duration of each reference time period itself) and then multiplying by the grid reference degree electric carbon emission amount to obtain the cumulative carbon trajectory contribution value of each reference time period; the reference slope value, the modified slope value and the cumulative carbon trajectory contribution value of each reference time period are combined as an array in order to generate a reference whole cycle carbon trajectory vector;

[0123] The deviation threshold of the full-cycle carbon trajectory vector from the reference full-cycle carbon trajectory vector is calculated, and the energy efficiency correction coefficient is calculated, specifically including: calculating the absolute difference of the three elements of the corresponding period in the full-cycle carbon trajectory vector and the reference full-cycle carbon trajectory vector, adding the 3 difference values of each period to obtain the total deviation of the period, and then dividing the total deviation of all periods by the total number of periods to obtain the average deviation threshold; multiplying the average deviation threshold by the energy efficiency correction coefficient to obtain the critical initial point;

[0124] Based on the ratio of the running time to the service life in the static data, a life attenuation correction coefficient is determined, specifically including: calculating the ratio of the running time to the service life in the static data to obtain the use proportion, when the use proportion ≤ 30%, the life attenuation correction coefficient is 1.0; when 30% < use proportion ≤ 70%, the life attenuation correction coefficient is 1.3; when the use proportion > 70%, the life attenuation correction coefficient is 1.6, wherein the coefficient increases with the increase of the use time of the target object, and then the influence of life attenuation on the critical value can be quantified;

[0125] The critical initial point is dynamically calibrated according to the life attenuation correction coefficient to obtain the critical point, specifically including: multiplying the critical initial point by the life attenuation correction coefficient to obtain the critical point, and then adjusting the critical point according to the aging degree of the target object.

[0126] Through the big data technology, the full-cycle carbon trajectory vector, the reference carbon trajectory vector and the static parameters are integrated to accurately calculate the critical point, and the energy efficiency correction coefficient and the life attenuation correction coefficient are dynamically calibrated to make the critical value adaptively adjust with the aging degree of the target object. This multi-dimensional fusion calculation based on big data breaks through the limitation of traditional fixed critical value, can accurately capture the dynamic changes of energy consumption attenuation and carbon emission increment of the target object in the whole life cycle, and improves the response accuracy of long-term carbon neutral planning to the aging trend of the target object.

[0127] In one case of the embodiment, according to the carbon neutral urgency index, the carbon neutral management instruction of the target object is generated, including:

[0128] Based on the carbon neutral urgency index and the basic operation data of the target object, a measure adaptation degree is generated, specifically including: calculating the deviation rate (deviation of actual value from standard range divided by width of standard range) of current, voltage, vibration frequency and real-time load in the basic operation data, adding the current, voltage, vibration frequency and real-time load after multiplying by the corresponding weights of 0.2, 0.2, 0.3 and 0.3 respectively to obtain the operation deviation degree; multiplying the carbon neutral urgency index of each carbon emission interval by the operation deviation degree and then multiplying by 0.5 to obtain the measure adaptation degree;

[0129] According to the measure adaptation degree and the carbon neutralization urgency index, a carbon neutralization management instruction of the target object is generated, specifically including: when the carbon neutralization urgency index is less than or equal to 2: if the measure adaptation degree is less than or equal to 0.3, a first instruction is generated, the first instruction is to adjust the refrigerant pressure of the target object; if the measure adaptation degree is greater than 0.3, a second instruction is generated, the second instruction is to optimize the load rate of the target object;

[0130] When 2<carbon neutralization urgency index≤5: if the measure adaptation degree is less than or equal to 0.5, a third instruction is generated, the third instruction is to calibrate the vibration of the target object; if the measure adaptation degree is greater than 0.5, a fourth instruction is generated, the fourth instruction is to replace the bearing of the target object;

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

[0132] Through big data technology, the carbon neutralization urgency index and the basic operation data of the target object are integrated, the targeted management instruction is generated by calculating the measure adaptation degree, the operation deviation degree of the target object is accurately quantified, different levels of management strategies are matched by combining the step-by-step urgency index, from adjusting the refrigerant pressure to replacing the target object, the instruction is dynamically upgraded according to the change of carbon emission caused by the aging of the target object, the defects of the traditional scheme in dynamic tracking of the whole cycle energy consumption and carbon emission are made up, and the carbon neutralization management is more accurate and efficient.

[0133] Although the embodiments of the present application have been shown and described, it can be understood by those skilled in the art that various changes, modifications, replacements and variations can be made to the embodiments without departing from the principles and spirits of the present application, the scope of the present application 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 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. 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. 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. The judgment unit is used to generate a step-by-step carbon neutrality urgency index when the health deficit exceeds a critical point; 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, Analyzing basic operational and static data yields operational degradation values ​​for the target object, 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.

3. The building energy consumption and carbon neutrality analysis system based on big data analysis according to claim 2, characterized in that, An analysis of operational degradation values, real-time carbon emissions per kilowatt-hour of the power grid, and the rated power of the target object yields the dynamic carbon trajectory slope, including: Analyze the deviation between the operational degradation value and the rated power of the target object, and generate 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.

4. The building energy consumption and carbon neutrality analysis system based on big data analysis according to claim 3, 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.

5. The building energy consumption and carbon neutrality analysis system based on big data analysis according to claim 3, 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.

6. The building energy consumption and carbon neutrality analysis system based on big data analysis according to claim 5, 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.

7. The building energy consumption and carbon neutrality analysis system based on big data analysis according to claim 5, characterized in that, Analyzing the full-cycle carbon trajectory vector and the lifetime of the target object yields the health deficit, including: 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; 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.

8. The building energy consumption and carbon neutrality analysis system based on big data analysis according to claim 7, characterized in that, When the health deficit exceeds a critical point, a ladder-like carbon neutrality urgency index is generated, including: 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. 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 base and dynamic weight value of each carbon emission range are calculated to obtain a stepped carbon neutrality urgency index.

9. A building energy consumption and carbon neutrality analysis system based on big data analysis according to claim 8, 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.

10. A building energy consumption and carbon neutrality analysis system based on big data analysis according to claim 9, 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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