A data center energy consumption and carbon emission intelligent monitoring method and system

Abstract

The application discloses a kind of data center energy consumption carbon emission intelligent monitoring method and system, by monitoring the energy consumption monitoring data and environmental data in data center operating environment, using time series analysis to identify energy consumption mode and generate hierarchical quality configuration;Based on energy consumption mode, execute marginal utility evaluation to determine precision configuration boundary, generate carbon source intensity index through influence intensity evaluation and convert into influence coverage area;The change track of environmental data is analyzed to construct carbon propagation network, obtain key propagation path and reconstruct monitoring network;Estimate the abnormal propagation range of influence coverage area to determine monitoring expansion area, build adaptive monitoring configuration and identify emission reduction potential area;Emission reduction potential area is decomposed into spatial monitoring node and utility weight distribution is carried out, through matrix operation of differentiating monitoring sequence to generate energy consumption carbon emission analysis matrix, finally through statistical evaluation to generate monitoring configuration parameter, realize the intelligent monitoring management of data center energy consumption carbon emission.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of energy-saving management, in particular to a data center energy consumption and carbon emission intelligent monitoring method and system. BACKGROUND

[0002] Under the background of accelerating digital transformation, data centers, as the key infrastructure supporting cloud computing, artificial intelligence and big data applications, are showing a trend of large-scale and intensive development. With the vigorous development of global digital economy, the energy consumption and carbon emission intensity of data centers are rising, and their environmental impact has attracted widespread attention from the international community.

[0003] Traditional data center energy consumption and carbon emission monitoring technology mainly relies on fixed monitoring point layout and static threshold control strategy, which has obvious limitations in actual application. Existing monitoring systems usually use a unified monitoring accuracy standard, which cannot be differentiated according to different equipment types and regional importance, resulting in unreasonable allocation of monitoring resources. Traditional analysis methods are mostly based on simple statistical processing of historical data, lacking deep mining of energy consumption change patterns and spatial distribution rules, making it difficult to accurately identify energy consumption anomalies and carbon emission hotspots. Existing technologies are relatively lagging in monitoring network layout optimization, and the configuration of monitoring points is mainly based on experience, lacking scientific layout optimization methods and dynamic adjustment mechanisms, and cannot effectively adapt to changes in data center equipment configuration and business load fluctuations. SUMMARY

[0004] The present application provides a data center energy consumption and carbon emission intelligent monitoring method and system, which analyzes the correlation characteristics of environmental parameters and energy consumption monitoring data, constructs precision configuration boundaries and carbon source intensity analysis systems, generates carbon propagation networks and identifies key propagation paths, realizes dynamic range adjustment and adaptive monitoring configuration of monitoring expansion areas, and finally generates differentiated monitoring sequences and energy consumption and carbon emission analysis results through utility weight optimization and matrix operation analysis of spatial monitoring nodes, providing intelligent and fine-grained energy consumption and carbon emission monitoring and management solutions for data centers.

[0005] The present application provides a data center energy consumption and carbon emission intelligent monitoring method and system, which analyzes the correlation characteristics of environmental parameters and energy consumption monitoring data, constructs precision configuration boundaries and carbon source intensity analysis systems, generates carbon propagation networks and identifies key propagation paths, realizes dynamic range adjustment and adaptive monitoring configuration of monitoring expansion areas, and finally generates differentiated monitoring sequences and energy consumption and carbon emission analysis results through utility weight optimization and matrix operation analysis of spatial monitoring nodes, providing intelligent and fine-grained energy consumption and carbon emission monitoring and management solutions for data centers.

[0006] Monitoring energy consumption monitoring data and environmental parameter data in the operating environment of the data center, performing time series analysis on the energy consumption monitoring data to identify energy consumption patterns, and generating hierarchical quality parameters based on the quality requirements of the energy consumption monitoring data;

[0007] Based on the energy consumption pattern and the hierarchical quality parameter, the marginal utility evaluation is performed to determine the precision configuration boundary, the influence intensity evaluation is performed on the precision configuration boundary to generate the carbon source intensity index, and the spatial mapping processing is used to convert the carbon source intensity index into an influence coverage area.

[0008] The change trajectory of the environmental parameter data is analyzed to generate a carbon propagation network, frequency analysis is performed on the carbon propagation network to obtain a key propagation path, the key propagation path is mapped to a carbon flow liquidity index, and the carbon flow liquidity index is used to reconstruct a monitoring network to form a path optimization table;

[0009] The abnormal propagation range of the influence coverage area is evaluated to determine a monitoring expansion area, dynamic range adjustment is performed on the monitoring expansion area to generate a range adjustment parameter, the range adjustment parameter is fused with the path optimization table to construct an adaptive monitoring configuration, and the adaptive monitoring configuration is used to identify an emission reduction potential area;

[0010] The emission reduction potential area is divided into spatial monitoring nodes, utility weight distribution is performed on the spatial monitoring nodes to generate a differentiated monitoring sequence, matrix operation is performed based on the differentiated monitoring sequence to generate an energy consumption and carbon emission analysis matrix, statistical evaluation is performed on the energy consumption and carbon emission analysis matrix to generate a monitoring configuration parameter, and intelligent monitoring of energy consumption and carbon emission of a data center is completed.

[0011] The second aspect of the present application provides a data center energy consumption and carbon emission intelligent monitoring system, comprising:

[0012] A data acquisition module is configured to monitor energy consumption monitoring data and environmental parameter data in a data center operating environment, perform time series analysis on the energy consumption monitoring data to identify an energy consumption mode, and generate hierarchical quality parameters based on quality requirements of the energy consumption monitoring data;

[0013] A marginal analysis module is configured to perform marginal utility evaluation based on the energy consumption mode and the hierarchical quality parameters to determine an accuracy configuration boundary, perform influence intensity evaluation on the accuracy configuration boundary to generate a carbon source intensity index, and convert the carbon source intensity index into an influence coverage area by using spatial mapping processing.

[0014] A propagation analysis module is configured to analyze the change trajectory of the environmental parameter data to generate a carbon propagation network, perform frequency analysis on the carbon propagation network to obtain a key propagation path, map the key propagation path to a carbon flow liquidity index, and reconstruct a monitoring network based on the carbon flow liquidity index to form a path optimization table.

[0015] A range control module is configured to evaluate the abnormal propagation range of the influence coverage area to determine a monitoring expansion area, perform dynamic range adjustment on the monitoring expansion area to generate a range adjustment parameter, fuse the range adjustment parameter with the path optimization table to construct an adaptive monitoring configuration, and identify an emission reduction potential area based on the adaptive monitoring configuration.

[0016] The matrix processing module is used for decomposing the emission reduction potential into spatial monitoring nodes, performing utility weight distribution on the spatial monitoring nodes to generate a differentiated monitoring sequence, performing matrix operation based on the differentiated monitoring sequence to generate an energy consumption and carbon emission analysis matrix, performing statistical evaluation on the energy consumption and carbon emission analysis matrix to generate monitoring configuration parameters, and completing intelligent monitoring of energy consumption and carbon emission of the data center.

[0017] The beneficial effects of the present application are reflected in the following points: 1. By monitoring energy consumption monitoring data and environmental data, identifying energy consumption patterns and generating a three-level quality configuration system, the differentiated allocation and precise placement of energy consumption monitoring resources are realized, and the monitoring accuracy and actual benefits are quantitatively matched through marginal utility evaluation, effectively improving the cost-benefit ratio and operating efficiency of the monitoring system. 2. Based on the environmental parameter data change trajectory, a carbon propagation network is constructed and the key propagation path is identified, which converts the abstract carbon emission diffusion process into a specific network topology and propagation channel. The carbon liquidity index can quantify the carbon emission propagation activity and influence range of different regions. Through propagation frequency characteristic analysis and low-frequency propagation region identification, intelligent optimization of the monitoring network layout and precise adjustment of the propagation signal are realized, solving the problem that the traditional static monitoring layout cannot adapt to the dynamic changes of carbon emissions. 3. The intelligent expansion of the monitoring boundary is realized by using the abnormal propagation range evaluation and dynamic range adjustment technology. Through the utility weight distribution of the spatial monitoring nodes and the identification of redundant areas, the problem of uneven distribution of monitoring resources and repeated monitoring is solved. A differentiated monitoring sequence is constructed and redundant data is converted into valuable analysis resources. The calculation complexity is reduced through the simplified processing of the matrix operation recursion chain, realizing the transformation from extensive monitoring management to fine-grained intelligent monitoring, and optimizing the system operating efficiency and resource configuration rationality on the basis of ensuring the completeness of the monitoring coverage.

[0018] It should be understood that the above general description and the following detailed description are only exemplary and explanatory, and cannot limit the present application. BRIEF DESCRIPTION OF DRAWINGS

[0019] The drawings herein show specific examples of the technical solutions described in the present application, and constitute part of the specification together with the specific embodiments, for explaining the technical solutions, principles and effects of the present application.

[0020] Unless specifically stated or defined otherwise, the same reference signs in different drawings represent the same or similar technical features. For the same or similar technical features, different reference signs may also be used.

[0021] Figure 1 is a flow diagram of a data center energy consumption and carbon emission intelligent monitoring method of the present application.

[0022] Figure 2is a structural block diagram of an intelligent monitoring system for energy consumption and carbon emission of a data center. DETAILED DESCRIPTION

[0023] In the following description, for purposes of explanation and not limitation, specific details are set forth such as particular architectures, techniques, etc. in order to provide a thorough understanding of the embodiments of the present application. However, it will be apparent to those skilled in the art that the present application can be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known methods, devices, circuits, and

[0024] It is to be understood that the terminology "including", "comprising", "consisting" and "consisting essentially of" used in the specification and the appended claims, indicates the presence of the stated features, integers, steps, operations, elements, and / or components, but does not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof.

[0025] Reference throughout this specification to "one embodiment" or "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the application. Thus, the appearances of the phrases "in one embodiment" or "in an embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment, but can refer to one or more but not all embodiments. The terms "including", "comprising", "consisting essentially of" and "consisting of" are used interchangeably, unless otherwise stated.

[0026] The technical solutions of the embodiments of the present application are introduced as follows.

[0027] As shown in Figure 1 The embodiments of the present application provide an intelligent monitoring method for energy consumption and carbon emission of a data center, which comprises the following steps S110-S150:

[0028] In step S110, the energy consumption monitoring data and the environmental parameter data in the running environment of the data center are monitored, the energy consumption consumption mode is identified by time series analysis on the energy consumption monitoring data, and the hierarchical quality parameters are generated based on the quality requirements of the energy consumption monitoring data.

[0029] Specifically, the energy consumption monitoring data and environmental parameter data in the data center operating environment are monitored. Intelligent power monitoring devices are deployed in various key areas of the data center to collect power consumption data of the server room, storage area, network equipment area, and refrigeration system in real time. The power monitoring device uses a high-precision power analyzer that can simultaneously measure multiple dimensions of energy consumption indicators such as active power, reactive power, apparent power, and power factor. The sampling frequency is set to 10 seconds to ensure that the details of energy consumption changes are captured. Intelligent PDU devices are installed at the server cabinet level to monitor the real-time power consumption and cumulative power usage of each cabinet. At the same time, a multi-point environmental parameter monitoring network is deployed in the data center, including temperature sensors, humidity sensors, CO2 concentration sensors, and air quality monitoring equipment. Temperature sensors are distributed at the air inlet, air outlet, and hot aisle locations in the server room. Humidity sensors are installed in pairs with temperature sensors. CO2 concentration sensors are mainly deployed in personnel activity areas and key nodes of the ventilation system. Environmental parameter data is collected every minute and transmitted to the data collection center through wired and wireless sensor networks. Energy consumption monitoring nodes are installed in the UPS system, diesel generator set, and power supply chain of the data center to monitor the energy consumption of each link.

[0030] The energy consumption monitoring data is analyzed in time series to identify energy consumption patterns. The collected energy consumption monitoring data is arranged in time series, and the sliding window technique is used to process the time series data in segments. The window length is set to 24 hours, and the step length is set to 1 hour to form continuous energy consumption time series segments. The frequency domain characteristics of the energy consumption time series data are analyzed by fast Fourier transform to identify the periodic components and main frequency components of energy consumption changes. In time domain analysis, the trend decomposition method is used to decompose the energy consumption time series data into three basic components: trend item, periodic item, and random fluctuation item. Typical energy consumption patterns of the data center are identified, including four main types: basic load pattern, business peak pattern, maintenance operation pattern, and emergency response pattern. The basic load pattern corresponds to the minimum energy consumption level of the data center in the minimum operating state. The business peak pattern corresponds to the energy consumption growth during the user access peak period. The maintenance operation pattern corresponds to the energy consumption fluctuation during system maintenance. The emergency response pattern corresponds to the energy consumption surge during a sudden event. Similar energy consumption time series patterns are classified by clustering analysis method to identify the typical characteristics and change rules of each type of pattern. In actual operation, it is observed that the Web service cluster presents a clear business peak energy consumption pattern from 9 am to 6 pm on weekdays, while the storage system shows a maintenance operation energy consumption pattern of data backup from 2 am to 5 am at night.

[0031] The quality requirements of energy consumption monitoring data in different application scenarios are analyzed, and the quality requirements are divided into three levels: accurate monitoring, standard monitoring and rough monitoring. The accurate monitoring level is suitable for key business systems and high energy consumption devices, requiring data collection accuracy within 1%, data integrity exceeding 99.9%, and time delay within 10 seconds. The standard monitoring level is suitable for general business systems and auxiliary devices, requiring data collection accuracy within 5%, data integrity exceeding 99%, and time delay within 1 minute. The rough monitoring level is suitable for backup devices and non-critical systems, requiring data collection accuracy within 10%, data integrity exceeding 95%, and time delay within 5 minutes. According to the demand characteristics of different quality levels, the corresponding hierarchical quality parameter set is generated. The quality parameters of the accurate monitoring level include high sampling frequency, multiple verification mechanism, redundant data backup and real-time anomaly detection function. The quality parameters of the standard monitoring level include medium sampling frequency, basic verification mechanism and periodic data backup function. The quality parameters of the rough monitoring level include low sampling frequency, simplified verification mechanism and batch data storage function. Considering the importance and energy consumption characteristics of different monitoring objects, the corresponding quality level is allocated to each type of device and system. Core servers, main storage systems and key network devices are allocated to the accurate monitoring level, general servers and auxiliary storage systems are allocated to the standard monitoring level, and backup devices and environmental regulation systems are allocated to the rough monitoring level.

[0032] In step S120, based on the energy consumption mode and the hierarchical quality parameter, the marginal utility evaluation is performed to determine the precision configuration boundary, the influence intensity evaluation is performed on the precision configuration boundary to generate the carbon source intensity index, and the carbon source intensity index is converted into the influence coverage area by using the space mapping processing.

[0033] In some embodiments, the marginal utility evaluation based on the energy consumption mode and the hierarchical quality parameter to determine the precision configuration boundary comprises: identifying a consumption intensity peak band for the energy consumption mode; generating a utility association point by associating the utility of the consumption intensity peak band with the hierarchical quality parameter; generating a precision demand domain by stimulating the change of the precision demand distribution using the utility association point; and determining the precision configuration boundary through the precision demand domain.

[0034] The energy consumption pattern is analyzed in the frequency domain to identify the main frequency components of the energy intensity change in each mode. The power spectral density analysis method is used to extract the frequency spectrum characteristics of each consumption mode and identify the frequency range where energy is concentrated. In the basic load mode, energy consumption changes are mainly concentrated in the low frequency band of 0.01-0.05 Hz, reflecting the slow power consumption adjustment process of the device. In the business peak mode, energy fluctuations are mainly distributed in the medium frequency band of 0.1-0.5 Hz, corresponding to the rapid change in user access. In the maintenance operation mode, energy changes show super-low frequency characteristics of 0.005-0.02 Hz, reflecting the long-period characteristics of maintenance tasks. In the emergency response mode, energy surges are concentrated in the high frequency band of 0.5-2 Hz, reflecting the rapid response characteristics of the system. The peak value points in each frequency band are identified by a peak detection algorithm to determine the frequency position of the consumption intensity peak value. The frequency components within a certain bandwidth around the peak frequency are defined as the consumption intensity peak value frequency band. The bandwidth characteristics of the peak value frequency band in different consumption modes are analyzed, and the wider the bandwidth, the more complex the energy consumption changes, requiring higher monitoring accuracy. The consumption intensity peak value frequency band of the data center's storage system during large-scale data migration shows a wide frequency spectrum characteristic, requiring high-precision monitoring to accurately capture the energy consumption changes.

[0035] The utility association points are generated according to the association of the consumption intensity peak value frequency band and the hierarchical quality parameters. The frequency characteristics of the consumption intensity peak value frequency band are matched and analyzed with the monitoring ability of the hierarchical quality parameters. The high sampling frequency of the accurate monitoring level can effectively capture the peak value frequency band changes in the high frequency band, the standard monitoring level is suitable for the monitoring demand of the peak value frequency band in the medium frequency band, and the rough monitoring level can only meet the basic monitoring requirements of the peak value frequency band in the low frequency band. The monitoring utility of each quality level for different peak value frequency bands is determined through band matching degree analysis. When the sampling frequency of the monitoring level is higher than 2 times the upper limit frequency of the peak value frequency band, the monitoring utility reaches the maximum value. When the sampling frequency is lower than the upper limit frequency of the peak value frequency band, the monitoring utility decreases sharply, and spectral aliasing may occur. In the utility association analysis, the peak value frequency band of the database server's business peak mode is 0.2-0.4 Hz, and the accurate monitoring level (sampling frequency 1 Hz) can obtain a monitoring utility of 90%, while the rough monitoring level (sampling frequency 0.1 Hz) can only obtain a monitoring utility of 30%. The key turning points of the utility association are identified through the analysis of the utility function curve, and these turning points correspond to the configuration states where the monitoring utility changes significantly. The turning points of the utility association are marked as utility association points, each association point contains three elements: peak value frequency band information, quality level information, and utility value.

[0036] For example, the step of using the utility correlation points to stimulate changes in the accuracy demand distribution and generate an accuracy demand domain includes: analyzing the accuracy configuration status based on the utility correlation points to generate a configuration margin table; stimulating changes in the accuracy demand distribution based on the configuration margin table to form a density optimization curve; using the density optimization curve to identify configuration paths and generate advantageous configuration paths; and performing spatial mapping on the advantageous configuration paths to generate an accuracy demand domain.

[0037] A configuration margin table is generated based on the accuracy configuration status analysis of utility correlation points. The difference between the current accuracy configuration and the optimal accuracy configuration is extracted from the utility correlation points to quantify the configuration margin of each monitoring point. A positive margin is generated when the current accuracy configuration is lower than the optimal configuration, indicating room for accuracy improvement; a negative margin is generated when the current configuration is higher than the optimal configuration, indicating configuration redundancy. The accuracy configuration margin values ​​of each device and area are measured; a larger margin indicates higher potential for configuration optimization. The current configuration parameters of each monitoring device in the data center are checked one by one using a configuration status analysis algorithm, including key indicators such as sampling frequency, data accuracy, verification method, and storage format. The check results are compared with the optimal configuration parameters determined by the utility correlation points to identify specific mismatches and the degree of deviation. In the video processing server cluster, the current standard monitoring level is used, but the optimal configuration is precise monitoring, resulting in a positive configuration margin of +0.3, indicating a need to improve monitoring accuracy to better capture GPU load changes. In the backup storage system, the current standard monitoring level is used, but the optimal configuration is coarse monitoring, resulting in a negative configuration margin of -0.2, indicating configuration overcapacity that could reduce monitoring costs. The configuration margins of each monitoring point are categorized and organized according to equipment type, spatial location, and temporal characteristics to form a structured configuration margin table.

[0038] A density optimization curve is generated based on the changes in accuracy requirement distribution triggered by the configuration margin table. A kernel density estimation method is used to fit the probability density distribution of the configuration margin, identifying concentrated and sparse regions of configuration requirement. Hotspots of accuracy requirement are determined by the peak positions of the density distribution curve; higher peaks indicate more urgent configuration optimization needs in those regions. The gradient of the density curve is analyzed; regions with larger gradients indicate drastic changes in accuracy requirement, requiring close attention and rapid response. The density distribution is visualized in three dimensions according to spatial coordinates, forming a three-dimensional distribution image of accuracy requirement. In areas with higher temperatures in the data center, due to increased equipment power consumption fluctuations and cooling demands, the accuracy requirement density curve exhibits a distinct peak characteristic. In the core switch area of ​​the data center, due to frequent changes in network traffic, the accuracy requirement density curve shows a multi-peak distribution, reflecting complex monitoring demand patterns. A density gradient analysis method is introduced to identify the boundary locations and transition regions where accuracy requirement changes drastically. The fitted density distribution curve is used as the density optimization curve; the shape and parameters of the curve reflect the spatial distribution pattern and optimization direction of accuracy requirement.

[0039] The density optimization curve is used to identify the optimal configuration path. The gradient direction of the density optimization curve is used to identify the optimal adjustment path of the precision configuration. The gradient rising direction corresponds to the adjustment path of the enhanced precision requirement, and the gradient descending direction corresponds to the adjustment path of the weakened precision requirement. The shortest path connecting the current configuration state and the target configuration state is searched on the density optimization curve through a path search algorithm. The A* search algorithm is used for path planning, and the heuristic function of the algorithm combines multiple factors such as path length, adjustment cost and implementation difficulty. The constraint conditions of configuration adjustment are considered, including hardware capability limitation, cost control requirement, implementation complexity constraint and time window limitation. The influence degree of various constraint conditions on path selection is analyzed. The hard constraint condition must be strictly satisfied, and the soft constraint condition can be relaxed within a certain range. In the path selection process, the path option with moderate configuration adjustment amplitude, low implementation difficulty and controllable risk is preferentially selected. The path evaluation index system includes adjustment effect index, cost benefit index, implementation feasibility index and risk assessment index. The advantage path is identified among multiple candidate configuration paths. The advantage path should have the comprehensive characteristics of significant adjustment effect, less resource consumption, controllable implementation risk and low maintenance cost.

[0040] The advantage configuration path is spatially mapped to generate the precision requirement domain. The coordinate transformation algorithm is used to convert the abstract configuration parameters into specific device positions and spatial coordinates. According to the device positions involved in the configuration path and the influence range, the corresponding spatial area is marked on the data center plan. The physical connection relationship and functional dependence relationship between devices are considered to expand the spatial range affected by the path. The multiple scattered spatial areas involved in the same advantage configuration path are connected and merged, and the spatial clustering algorithm is used to form a continuous precision requirement domain. The shape optimization of irregular spatial areas is performed by applying morphological operations to generate regular areas convenient for management and implementation. The boundary of the precision requirement domain is determined by the influence range of the configuration path and the device distribution density. All monitoring points in the domain need to be adjusted according to the path. In the storage array area of the data center, due to the dense distribution of disk devices, the precision requirement domain presents a long strip feature, extending along the arrangement direction of the storage cabinet. The geometric characteristics of the precision requirement domain are analyzed, including area, boundary perimeter, shape complexity and spatial connectivity.

[0041] The precision configuration boundary is determined by the precision requirement domain. The boundary information and geometric characteristics of the precision requirement domain are used to delineate the regional boundary of different precision configuration requirements in the data center. The outer boundary of the precision requirement domain is used as the initial candidate line of the precision configuration boundary, and the candidate line is geometrically adjusted and topologically optimized by the boundary optimization algorithm. The boundary smoothing algorithm is used to eliminate the jagged irregular features of the boundary line, and a smooth and continuous configuration boundary is generated. Considering the continuity and consistency requirements of precision configuration, the large difference in precision level between adjacent regions is avoided, which leads to uncoordinated configuration and complex management. In the boundary delineation process, the physical layout characteristics of the data center are fully considered, and a transition buffer zone is set between the network equipment area and the server area. The medium precision configuration is used in the buffer zone to ensure the smooth transition of the configuration level. In the cooling system area of the data center, due to the relatively slow change of environmental parameters, the configuration boundary can be set relatively loose, and the rough monitoring level can meet the requirements. The trigger conditions for boundary adjustment include significant changes in business requirements or significant updates to device configuration. Considering the practical operability of boundary management, the complex irregular boundary is simplified into a regular geometric shape that is easy to identify and manage. The determined precision configuration boundary is superimposed with the data center equipment distribution map, network topology map and management area map to generate a complete precision configuration boundary management map.

[0042] The influence intensity of the precision configuration boundary is evaluated to generate a carbon source intensity index. The influence degree of the energy consumption characteristics of each monitoring point in the precision configuration boundary on carbon emissions is analyzed, and the carbon source intensity of different devices and regions is quantified. The power consumption of each monitoring point is converted into corresponding carbon emissions by using the energy consumption-carbon emission conversion coefficient. Considering the carbon emission factor of the power grid in the region where the data center is located, the conversion coefficient increases when the local proportion of thermal power is high, and decreases when the proportion of renewable energy is high. The variation law of the carbon emission factor of the power grid in different time periods is analyzed. The carbon emission factor of the power grid is usually high during the night peak period of electricity consumption, and relatively low when the solar power generation is sufficient during the day. Combined with the operation characteristics and energy consumption intensity of the device, a carbon source intensity weight is assigned to each monitoring point. High-power GPU computing clusters and large storage arrays are assigned a higher carbon source intensity weight, and low-power network switching devices and environmental monitoring systems are assigned a lower weight. The instantaneous carbon source intensity value of each monitoring point is generated by multiplying the carbon source intensity weight by the actual energy consumption data. The instantaneous carbon source intensity value is time-averaged to obtain a stable carbon source intensity index. In the AI training area of the data center, the carbon source intensity index of the GPU cluster is usually 3-5 times that of the ordinary server area due to the long-time high-load operation of the GPU cluster.

[0043] In some embodiments, the converting the carbon source intensity index into an influence coverage area using the spatial mapping processing includes: identifying dense mapping areas and sparse mapping areas according to the spatial mapping of the carbon source intensity index; performing influence range inference processing using the sparse mapping areas to form an extended inference area; performing spatial fusion of the extended inference area and the dense mapping areas to generate a complete coverage segment; and performing spatial optimization on the complete coverage segment to construct the influence coverage area.

[0044] According to the spatial mapping of the carbon source intensity index, dense mapping areas and sparse mapping areas are identified. The carbon source intensity index is grid processed according to spatial coordinates, and each grid cell corresponds to a physical area of 5m x 5m in the data center. The discrete carbon source intensity index is mapped to a continuous grid space through a spatial interpolation method, and an inverse distance weighted interpolation algorithm is used to ensure the smoothness of the mapping result. The density distribution of the carbon source intensity index in each grid cell is analyzed. A grid with high density indicates that the area concentrates multiple high-intensity carbon sources. When the density of the carbon source intensity index in the grid exceeds 1.5 times the average density, the grid is marked as a dense mapping area. When the density of the carbon source intensity index in the grid is less than 0.5 times the average density, the grid is marked as a sparse mapping area. The dense mapping area usually corresponds to the high-power device concentration area in the data center, such as the GPU computing cluster and large storage array area. The sparse mapping area usually corresponds to the area with low device density, such as the aisle, rest area, and device maintenance space. In the AI training area of the data center, due to the high-density deployment of GPU servers, this area is almost entirely marked as a dense mapping area, and the density of the carbon source intensity index reaches 4-6 times the average value. Through connected component analysis, adjacent dense mapping grids are merged into continuous dense mapping areas, and adjacent sparse mapping grids are merged into continuous sparse mapping areas.

[0045] The sparse mapping area is used to infer the influence range to form an extended inference area. The spatial adjacency relationship between the sparse mapping area and the dense mapping area is analyzed to identify the connection channel that may be affected by the diffusion of carbon sources. Considering the air flow pattern in the data center, the sparse mapping area along the cold aisle and hot aisle direction is more likely to be affected by the diffusion of adjacent dense mapping areas. The diffusion propagation simulation is used to predict the propagation degree of carbon source intensity from the dense mapping area to the sparse mapping area, and the propagation intensity decreases exponentially with the increase of distance. When the distance between the sparse mapping area and the dense mapping area is less than 10 meters and located in the downwind direction, the sparse mapping area is marked as a high-influence extended candidate area. The boundary range of the extended inference area is determined by analyzing the influence propagation path, and the boundary range is affected by factors such as air flow rate, temperature gradient and obstacle distribution. In the server room of the data center, the sparse mapping area near the air supply outlet has a relatively large range of the extended inference area due to air flow disturbance, which can extend to a range of 15 meters away from the dense area. In the battery energy storage area of the data center, although the equipment density is not high and is marked as a sparse mapping area, due to the heat diffusion during the charging and discharging process of the battery, the surrounding 5-meter range is also included in the extended inference area. The inferred influence extension area is marked as an extended inference area, and the influence source and expected influence intensity of the inference area are recorded.

[0046] The extended inference area and the dense mapping area are spatially fused to generate a complete coverage segment. The boundary of the dense mapping area is extended outward through spatial union operation to cover the related extended inference area. In the fusion process, the weight difference of different influence areas is considered, and the influence weight of the dense mapping area is set to 1.0, and the influence weight of the extended inference area is set to 0.3-0.7 according to the distance decay characteristics. The influence intensity of the overlapping area is processed by using the weighted fusion method, and the comprehensive influence intensity of the overlapping area is equal to the weighted sum of the influence weight of each influence source. The boundary of the fused coverage area is smoothed to eliminate the jagged irregular shape of the boundary. The connectivity characteristics of the complete coverage segment are analyzed to identify independent coverage segments and interconnected coverage segment groups. In the network core area of the data center, due to the relatively dispersed distribution of switch and router devices, multiple independent complete coverage segments are formed after fusion, and each segment covers an area of about 50-100 square meters. The coverage segments with similar shapes and close distances are further merged to generate larger complete coverage segments. Considering the functional correlation between coverage segments, the devices serving the same business system are preferentially merged.

[0047] Spatial optimization is performed to construct the impact coverage area for complete coverage segments. Geometric optimization is applied to the generated complete coverage segments to improve the regularity and usability of the coverage area. A convex hull algorithm is used to regularize the shape of irregular coverage segments, generating regular-shaped coverage areas that are easy to manage and control. Considering the actual situation of the channel layout and equipment distribution within the data center, the coverage area boundaries are adjusted to adapt to physical constraints. The coverage area boundaries are aligned with the main channels, equipment boundaries, and security areas within the computer room to ensure the actual operability of the coverage area. Coverage segments that are too small are merged or deleted to avoid generating too many unmanageable tiny coverage areas. A minimum area threshold of 25 square meters is set for the coverage area; segments smaller than this threshold are merged with adjacent larger segments. In the UPS power supply area of ​​the data center, due to the large size and relatively fixed distribution of the equipment, the optimized impact coverage area presents a regular rectangular shape, facilitating identification and management by maintenance personnel. The optimized coverage segments are classified according to their impact intensity, with different identification and management strategies applied to strong-impact, medium-impact, and weak-impact coverage areas.

[0048] Step S130: Analyze the change trajectory of environmental parameter data to generate a carbon propagation network, perform frequency analysis on the carbon propagation network to obtain key propagation paths, map the key propagation paths to carbon mobility indicators, and reconstruct the monitoring network based on the carbon mobility indicators to form a path optimization table.

[0049] Specifically, a carbon propagation network is generated by analyzing the changing trajectories of environmental parameter data. Environmental parameter data such as temperature, humidity, CO2 concentration, and air quality collected in step S110 are arranged in time series, and the changing trajectories of each parameter at different spatial locations are analyzed. Gradient analysis of the time series data identifies the direction and rate of change of environmental parameters; regions with large gradients indicate drastic changes in environmental parameters, corresponding to active carbon emission propagation areas. Correlation analysis is used to identify the degree of correlation between environmental parameter changes at different monitoring points; when the CO2 concentration changes at two monitoring points show a high correlation, it indicates a carbon emission propagation link. Monitoring points with strong correlations are connected to form the basic network structure for carbon propagation. In the server room of the data center, the CO2 concentration changes in the GPU cluster area and the CO2 concentration changes at the downstream hot aisle outlet show a significant time-lag correlation, indicating that carbon emissions propagate along the airflow direction. The physical path of carbon propagation is determined through airflow analysis, and the main carbon propagation directions are identified by combining the supply and return air patterns of the ventilation system. The propagation paths are weighted according to propagation intensity, which is determined by both the correlation coefficient and the time-lag characteristics. The importance of nodes in a network is determined by both connectivity and propagation influence. Important nodes typically correspond to key sources or accumulation points of carbon emissions. In data center cooling rooms, due to the convergence of waste heat from multiple heat aisles, this area becomes a significant accumulation node in the network.

[0050] In some embodiments, the frequency analysis on the carbon propagation network obtains the key propagation path, including: obtaining the propagation frequency characteristics of the carbon propagation network; identifying a low-frequency propagation region according to the propagation frequency characteristics to generate a low-frequency region parameter; and performing passive adjustment of the propagation signal using the low-frequency region parameter to generate an adjusted propagation signal; and identifying the key propagation path through the adjusted propagation signal.

[0051] The propagation frequency characteristics of the carbon propagation network are obtained. The frequency domain analysis is performed on the activation mode of each propagation path in the carbon propagation network to identify the frequency distribution characteristics of the propagation activity. The fast Fourier transform method is used to convert the time domain activation signal of the propagation path into a frequency domain signal, and the frequency spectrum characteristics of each path are extracted. The distribution range of the propagation frequency is analyzed. The frequency of the carbon propagation in the data center is usually distributed in the low-frequency band of 0.001 Hz-0.1 Hz, which corresponds to the propagation period of several minutes to several hours. On the main propagation path of the server room, the propagation frequency is mainly concentrated in the frequency band of 0.01 Hz-0.05 Hz, which corresponds to the propagation period of 20 seconds to 100 seconds, reflecting the carbon emission fluctuation caused by the change of server load. The dominant frequency component of different propagation paths is identified, which corresponds to the most active propagation mode of the path. The energy distribution of each frequency component is determined through power spectrum density analysis, and the frequency band with concentrated energy represents the main characteristic frequency of the propagation activity. The time stability of the propagation frequency is analyzed. The stable frequency component reflects the inherent propagation characteristics of the system, and the unstable frequency component may be related to external interference or abnormal events.

[0052] The low-frequency propagation region is identified according to the propagation frequency characteristics to generate a low-frequency region parameter. The propagation paths and nodes with a propagation frequency lower than 0.005 Hz in the carbon propagation network are scanned, which correspond to the regions with relatively slow and stable carbon propagation activity. The spatial distribution characteristics of the low-frequency propagation region are analyzed to identify the aggregation mode and dispersion mode of the low-frequency region. The low-frequency propagation region usually corresponds to the data center region with stable device operation and slow environmental change, such as backup device region, battery energy storage region and device maintenance region. The propagation delay characteristics of the low-frequency propagation region are measured. The propagation delay of the low-frequency region is usually long, and it takes a long time for the carbon emission concentration to propagate to the downstream monitoring point. In the archive storage area of the data center, since the tape library device has low frequency of use and stable power consumption, this area is identified as a typical low-frequency propagation region with a propagation frequency of only 0.001 Hz. The low-frequency region parameter includes four core indicators: average propagation frequency, propagation delay time, region coverage area and node connectivity. The average propagation frequency reflects the overall frequency level of the propagation activity in the region, the propagation delay time reflects the time lag characteristics of the propagation signal, the region coverage area reflects the spatial range of the low-frequency region, and the node connectivity reflects the connection degree of the region with other regions.

[0053] The adjusted propagation signal is generated by passively adjusting the propagation signal using low-frequency region parameters. The propagation signal is passively adjusted based on the characteristics of the low-frequency region parameters. Passive adjustment refers to not actively changing the physical characteristics of the propagation path, but optimizing the quality of the propagation signal through signal processing technology. The weak propagation signal in the low-frequency propagation region is enhanced using signal amplification technology. The amplification factor is determined according to the frequency characteristics of the region. The lower the frequency, the higher the amplification factor. High-frequency noise components in the propagation signal are removed through filtering technology, and the effective signal characteristics of low-frequency propagation are retained. In the isolation area of the machine room of the data center, the propagation signal quality is high due to the relatively closed environment and less interference. Only slight signal adjustment is needed. The propagation delay effect of the low-frequency propagation region is corrected using signal delay compensation technology. Delay compensation enables the propagation signals of different regions to remain synchronized in time. The detection threshold of the propagation signal is dynamically adjusted through adaptive threshold adjustment technology. The threshold adjustment is optimized according to the propagation intensity and noise level of the region. The adjusted propagation signal is compared and analyzed with the original propagation signal to evaluate the effect and improvement of signal adjustment. In the ventilation duct area of the data center, the propagation signal in this area needs strong filtering and noise reduction processing due to the noise generated by air flow disturbance. The generated adjusted propagation signal has higher signal-to-noise ratio and more stable propagation characteristics, and can more accurately reflect the true state of carbon propagation.

[0054] The key propagation path is identified by adjusting the propagation signal. The adjusted propagation signal is used to re-analyze the path importance and propagation efficiency of the carbon propagation network. Strong signal paths in the adjusted propagation signal are identified through signal intensity analysis. Strong signal paths correspond to propagation channels with high activity and good signal quality. The correlation between the adjusted propagation signals is identified using signal correlation analysis. High correlation between signal paths indicates a close propagation relationship. The complete propagation path is identified along the propagation direction of the adjusted propagation signal through path tracking algorithm. Path tracking starts from the propagation source and extends to the propagation endpoint along the signal intensity gradient direction. The propagation distance and propagation speed characteristics of the adjusted propagation signal are analyzed. Paths with longer propagation distance and faster propagation speed have higher propagation efficiency. In the core machine room area of the data center, the propagation path from the GPU computing cluster to the precise air conditioner return air inlet shows strong propagation signal characteristics after signal adjustment and is identified as a first-level key propagation path. The importance ranking of the key propagation path is determined through multi-path comparison analysis. The ranking is based on comprehensive indicators such as adjusted signal intensity, propagation range, propagation frequency, and propagation stability. Key nodes in the key propagation path are identified. Key nodes are usually located at the intersection of multiple paths or the turning point of propagation.

[0055] The critical propagation paths are mapped to carbon flow mobility indicators. Based on the characteristic parameters of critical propagation paths, such as activation frequency, propagation intensity and path length, a representation system of carbon flow mobility indicators is constructed. Carbon flow mobility indicators reflect the active degree and propagation efficiency of carbon emissions in a specific path. A weighted comprehensive method is used to integrate multiple path characteristic parameters into a single flow mobility indicator value. The weight distribution is determined according to the importance of each parameter to carbon propagation. The weight of activation frequency is set to 0.4, reflecting the frequency of path usage. The weight of propagation intensity is set to 0.3, reflecting the effectiveness of path propagation. The weight of path length is set to 0.2, reflecting the distance characteristics of propagation. The weight of other characteristic parameters is set to 0.1. Through normalization processing, the numerical range of carbon flow mobility indicators is unified to 0-1, which is convenient for comparative analysis between different paths. In the network equipment area of the data center, due to the relatively stable and uniform distribution of power consumption of switches and routers, the carbon flow mobility indicators in this area present a moderate level of uniform distribution characteristics. By analyzing the spatial distribution pattern of carbon flow mobility indicators, three levels of high flow mobility area, medium flow mobility area and low flow mobility area are identified. High flow mobility areas usually correspond to areas with active carbon emissions and dense propagation paths, which need to be monitored and controlled. The carbon flow mobility indicators are associated and mapped with the positions of the monitoring points, and each monitoring point is assigned a corresponding flow mobility indicator value.

[0056] The monitoring network is reconstructed according to the carbon flow mobility indicators to form a path optimization table. The numerical distribution of carbon flow mobility indicators is used to re-evaluate the rationality of the layout of the existing monitoring network and the monitoring effect. The matching degree of monitoring point density and carbon flow mobility indicators is analyzed. Higher density of monitoring points should be configured in high flow mobility areas, and the density of monitoring points can be appropriately reduced in low flow mobility areas. The spatial configuration of monitoring points is adjusted through a monitoring network optimization algorithm, and the optimization goal is to maximize the monitoring coverage effect under the constraint of monitoring cost. Redundant monitoring points in the monitoring network are identified. When multiple monitoring points are located in low flow mobility areas and the monitoring ranges overlap, some monitoring points can be considered to be merged or removed. In the office area of the data center, due to less personnel activity and low equipment density, the carbon flow mobility indicators in this area are generally low, and the number of monitoring points can be reduced or the monitoring frequency can be reduced. Blank areas in the monitoring network are identified. When high flow mobility areas lack sufficient monitoring coverage, monitoring points need to be added or the positions of existing monitoring points need to be adjusted. A new monitoring network topology is generated through the reconfiguration of monitoring points, and the new network should better capture the dynamic characteristics of carbon propagation. The adjustment scheme of the monitoring network is arranged into a path optimization table, which contains fields such as monitoring point identification, original position, target position, adjustment type, flow mobility indicator and implementation priority. The path optimization table is sorted according to the implementation priority, and the adjustment scheme with high priority is implemented first, and the scheme with low priority can be executed later according to the resource situation. In the UPS power supply room of the data center, due to the stable operation of equipment and the airtight environment, the adjustment priority of monitoring points in this area is set to a low level, and the adjustment can be implemented uniformly during system maintenance.

[0057] In step S140, the monitoring expansion area is determined according to the abnormal propagation range of the influence coverage area, dynamic range adjustment is performed on the monitoring expansion area to generate a range adjustment parameter, the range adjustment parameter is fused with the path optimization table to construct an adaptive monitoring configuration, and a potential emission reduction area is identified based on the adaptive monitoring configuration.

[0058] In some embodiments, the monitoring expansion area is determined according to the abnormal propagation range of the influence coverage area, including: identifying a low abnormal area according to the abnormal propagation range measurement of the influence coverage area; forming a reference quality distribution by using the low abnormal area as a stability reference; generating a derived range matrix by using the reference quality distribution for range value derivation; and determining the monitoring expansion area by spatial mapping of the derived range matrix.

[0059] A low abnormal area is identified according to the abnormal propagation range measurement of the influence coverage area. The carbon emission propagation data of each monitoring point in the influence coverage area output by step S120 is evaluated for abnormality degree, and the degree of deviation of each area from the normal propagation mode is quantified. Statistical analysis is used to determine the normal range benchmark of carbon emission propagation, and the benchmark range is determined by the statistical characteristics of historical data, including mean, standard deviation, and coefficient of variation. The abnormal degree of each area is measured by an abnormality measurement formula, and the abnormal degree is equal to the deviation degree of the actual propagation range from the normal benchmark range divided by the standard deviation of the benchmark range. When the abnormal degree is less than 1 standard deviation, the area is marked as a low abnormal area, indicating that the carbon emission propagation behavior of the area is relatively stable and predictable. In the UPS power supply area of the data center, the carbon emission propagation range of this area changes very little due to the stable operation of the UPS equipment and the airtight environment, and the abnormal degree is only 0.3 standard deviations, which is identified as a typical low abnormal area. The spatial distribution characteristics of the low abnormal area are analyzed to identify the aggregation mode and distribution law of the low abnormal area. The low abnormal area usually corresponds to a data center area with stable equipment operation, consistent environmental conditions, and less human interference. The adjacent low abnormal areas are merged into a continuous stable propagation area by connected domain analysis, and the merged area has stronger statistical representativeness. In the optical fiber wiring area of the data center, the optical fiber equipment basically does not generate heat and has no mechanical moving parts, and this area has remained in a low abnormal state for more than 30 days, becoming the most stable low abnormal area.

[0060] A reference quality distribution is formed using low abnormal areas as a stability reference. The identified low abnormal areas are used as a reference benchmark for stable propagation, and the carbon emission propagation quality characteristics and distribution patterns of these areas are analyzed. The propagation quality parameters of the low abnormal areas are extracted, including propagation consistency, propagation predictability, propagation stability, and propagation efficiency. Propagation consistency reflects the similarity of propagation behavior at different times, propagation predictability reflects the regularity of propagation behavior, propagation stability reflects the fluctuation amplitude of propagation parameters, and propagation efficiency reflects the energy utilization efficiency of the propagation process. The probability density estimation method is used to fit the distribution characteristics of the low abnormal area propagation quality parameters, and the probability density function of the reference quality distribution is generated. The reference quality distribution reflects the normal distribution range and distribution pattern of the quality parameters under stable propagation conditions. In the monitoring room area of the data center, the power consumption of the monitoring equipment is low and the operation is continuous and stable, so the propagation quality parameters of this area show a highly concentrated normal distribution characteristic, with small distribution variance, becoming an ideal source of reference quality distribution. The statistical characteristics of the reference quality distribution are analyzed, including the mean, variance, skewness, and kurtosis parameters, which are used for subsequent quality evaluation and range derivation. Through the fusion processing of multiple area reference quality distributions, a more representative comprehensive reference quality distribution is generated.

[0061] A derived range matrix is generated using the reference quality distribution to derive the range values. Based on the statistical characteristics and distribution parameters of the reference quality distribution, the reasonable propagation range values of other areas are derived. The quality similarity matching method is used to compare the propagation quality parameters of the area to be evaluated with the reference quality distribution, and the quality similarity level is determined. When the quality parameters of the area to be evaluated fall within 1 standard deviation of the reference quality distribution, the area can use similar propagation range configuration as the low abnormal area. Through linear interpolation and nonlinear fitting methods, the reasonable propagation range values of the area to be evaluated are derived according to the quality similarity. The higher the quality similarity of an area, the closer its derived range value is to the range value of the reference area. Areas with lower quality similarity need to be adjusted appropriately. In the standby server area of the data center, the equipment is in standby state and the environmental conditions are similar to the reference area, so the reasonable propagation range of this area is derived through quality distribution matching, which is 12-18 meters, close to the reference area's 15-meter benchmark value. The derived range values are organized into a matrix form according to the spatial coordinates and equipment types. The row coordinates of the matrix correspond to the spatial position, and the column coordinates correspond to the range parameter type. The derived range matrix includes multiple parameter columns such as the minimum propagation range, the maximum propagation range, the average propagation range, and the range confidence. Through numerical analysis of the matrix elements, the spatial distribution pattern and variation gradient of the range values are identified. Areas with larger gradients indicate that the propagation range changes dramatically and require more detailed monitoring configuration.

[0062] The monitoring expansion area is determined by spatial mapping based on the derived range matrix. The range values in the derived range matrix are mapped from the parameter space to the physical space of the data center, generating the specific monitoring expansion area boundary. A spatial interpolation algorithm is used to extend the discrete range values to a continuous spatial range field, and the radial basis function interpolation method is used to ensure spatial continuity. According to the maximum propagation range value in the derived range matrix, the expansion monitoring boundary of each monitoring point is determined, and the expansion boundary should cover the possible maximum propagation influence range of the point. Through the buffer zone analysis method, the monitoring expansion area in the form of a circle or an ellipse is generated with each monitoring point as the center and the derived propagation range as the radius. Considering the influence of obstacles and boundary constraints in the data center, the theoretical expansion area is geometrically modified and boundary cropped. Near the walls of the machine room in the data center, the monitoring expansion area is limited by the physical boundary, and the expansion range needs to be adjusted according to the position of the wall to avoid expanding to the invalid area outside the machine room. The expansion areas of multiple monitoring points are merged through spatial set operation to generate a complete monitoring expansion area.

[0063] The range adjustment parameters are generated by performing dynamic range adjustment on the monitoring expansion area. For the determined monitoring expansion area, the demand characteristics and adjustment strategies of dynamic range adjustment are analyzed. Through time series analysis, the dynamic change pattern of carbon emission concentration in the monitoring expansion area is studied, and the peak period and stable period of concentration change are identified. In the peak period, the monitoring expansion area needs higher monitoring density and faster response speed, and in the stable period, the monitoring intensity can be reduced to save resources. An adaptive threshold adjustment method is used to dynamically adjust the boundary range of the monitoring expansion area. When the carbon emission activity in the expansion area increases, the monitoring range is expanded, and when the activity decreases, the monitoring range is reduced. Key factors affecting range adjustment are analyzed, including device load change, environmental temperature fluctuation, ventilation system operating state, and personnel activity intensity. In the storage array area of the data center, the read-write load of disk devices increases significantly during night backup, and the monitoring expansion area of this area needs to expand by 20% at night to cover the enhanced carbon emission propagation. The range adjustment parameters include four core parameters: basic monitoring radius, peak expansion coefficient, adjustment response time, and adjustment frequency limit. The basic monitoring radius determines the minimum coverage range of the expansion area, the peak expansion coefficient determines the range amplification multiple in the peak period, the adjustment response time determines the execution speed of range adjustment, and the adjustment frequency limit avoids excessive frequent range adjustment.

[0064] The range adjustment parameter is fused with the path optimization table to construct an adaptive monitoring configuration. The corresponding relationship between the monitoring points in the path optimization table and the monitoring expansion area is identified through spatial matching analysis to determine which monitoring points need to be configured and adjusted according to the range adjustment parameter. The dynamic adjustment requirement in the range adjustment parameter is converted into specific monitoring point configuration parameters, including the activation state of the monitoring point, the monitoring frequency, the data transmission mode, and the alarm threshold, etc. When the range of the monitoring expansion area is reduced, the edge monitoring point can be switched to a low-power monitoring mode, and when the range is expanded, the dormant monitoring point needs to be activated and switched to a high-frequency monitoring mode. In the network core area of the data center, due to the relatively stable power consumption of the switch device, the adaptive monitoring configuration in this area adopts a hybrid mode of fixed basic configuration plus dynamic adjustment, and the basic configuration guarantees the basic monitoring requirement, and the dynamic adjustment responds to the sudden load change. The configuration conflict of multiple monitoring expansion areas is handled through configuration priority management, and when the configuration requirements of adjacent expansion areas conflict, the requirements of the area with higher importance level are preferentially met. A parameter table of adaptive monitoring configuration is constructed, which includes fields such as monitoring point identifier, basic configuration parameter, dynamic adjustment parameter, adaptive rule, and execution condition, etc.

[0065] The adaptive monitoring configuration is used to identify the emission reduction potential area. High-emission-density areas are identified through carbon emission density analysis, which usually correspond to target areas with the greatest emission reduction potential. The efficiency characteristics of carbon emissions are analyzed to identify inefficient areas that produce excess carbon emissions per unit of power consumption, which can achieve significant emission reduction effects through device upgrade, optimized configuration, or operation mode adjustment. In the old server area of the data center, due to the low energy efficiency of the devices and the low efficiency of the cooling system, this area is identified as a high-potential emission reduction area, and the carbon emission intensity can be reduced by 30% through device replacement. The peak-valley difference of carbon emissions is identified through time pattern analysis, and areas with large peak-valley differences indicate the potential for load optimization and time scheduling. The relationship between device utilization and carbon emission efficiency is analyzed to identify waste areas with low device utilization but high carbon emission intensity. Through spatial clustering analysis, areas with similar emission reduction characteristics are classified to form a classification system of emission reduction potential areas. The identified emission reduction potential areas are prioritized according to the emission reduction effect and implementation difficulty to generate an implementation roadmap for the emission reduction potential areas.

[0066] In step S150, the emission reduction potential area is decomposed into spatial monitoring nodes, the utility weight is allocated to the spatial monitoring nodes to generate a differentiated monitoring sequence, the matrix operation is performed based on the differentiated monitoring sequence to generate an energy consumption and carbon emission analysis matrix, the statistical evaluation is performed on the energy consumption and carbon emission analysis matrix to generate monitoring configuration parameters, and the intelligent monitoring of data center energy consumption and carbon emission is completed.

[0067] Specifically, the emission reduction potential is divided into spatial monitoring nodes. The emission reduction potential area output by the S140 step is grid-decomposed according to the spatial resolution, and each grid unit is converted into a spatial monitoring node. According to the physical layout characteristics of the data center, the monitoring nodes are set differently according to the equipment density and importance. The node spacing in the high-density equipment area is set to 3 meters, the medium-density area is set to 5 meters, and the low-density area is set to 8 meters. In the GPU computing cluster area of the data center, since the equipment has high power consumption and large carbon emission intensity, this area is divided into a dense monitoring node network, and a total of 24 monitoring nodes are set to cover the entire cluster area. Analyze the functional characteristics and monitoring responsibilities of each spatial monitoring node. The core monitoring node is responsible for direct monitoring of key equipment, the auxiliary monitoring node is responsible for supplementary collection of environmental parameters, and the boundary monitoring node is responsible for boundary detection of the propagation range. According to the spatial position and equipment association relationship of the monitoring nodes, a unique spatial identifier and function code are assigned to each node. In the storage array area of the data center, since the disk equipment is arranged regularly and the monitoring requirements are similar, the monitoring nodes in this area adopt a regular grid layout, which is convenient for unified management and maintenance. The spatial monitoring nodes are grouped according to the type of monitoring objects, and the server monitoring node group, the storage monitoring node group, the network equipment monitoring node group and the environmental monitoring node group undertake different monitoring tasks respectively.

[0068] In some embodiments, the utility weight allocation to the spatial monitoring nodes generates a differentiated monitoring sequence, including: identifying a weight redundant area for the utility weight allocation to the spatial monitoring nodes; determining an adjustable margin according to the weight redundant area to form a margin allocation table; using the margin allocation table to perform monitoring path optimization to form an optimized conduction path; and performing sequence calibration on the optimized conduction path to build a differentiated monitoring sequence.

[0069] The utility weight distribution of each spatial monitoring node is analyzed, and the area with uneven weight distribution is identified through weight density analysis. When the weight values of multiple monitoring nodes in a certain area are significantly higher than the average level and the monitoring functions overlap, the area is marked as a weight redundant area. The spatial clustering analysis method is used to identify the boundary and scope of the weight redundant area. The clustering algorithm groups the monitoring nodes according to the weight similarity and spatial proximity. The weight redundancy degree is measured by the redundancy quantification method, and the redundancy degree is equal to the ratio of the total weight in the area to the theoretical optimal weight distribution. In the database server area of the data center, the importance level of multiple servers is high, and the six monitoring nodes in this area are allocated high weight, with a total weight value of 4.2, while the theoretical optimal allocation is only 2.8, forming obvious weight redundancy. The causes of the weight redundant area are analyzed, including the factors of high importance evaluation, overlapping monitoring range and improper resource allocation. The scope and intensity of the redundancy influence are determined through spatial analysis of the redundant area, and the area with high redundancy intensity has greater potential for weight adjustment. The identified weight redundant area is classified according to the redundancy degree, and different adjustment strategies are adopted for high, medium and slight redundancy areas. In the standby server area of the data center, although the weight of a single node is not high, the total weight of the area exceeds the standard due to the high node density, and it is identified as a medium redundancy area.

[0070] For example, the weight redundant area determines the adjustable margin to form a margin allocation table, including: based on the weight redundant area, the margin allocation value is evaluated to determine the allocation intensity, the margin allocation value includes the resource importance level of the redundant area, the adjustable degree and the optimization degree of the surrounding nodes; according to the allocation intensity, the margin weight of the surrounding monitoring nodes is redistributed to form a margin weight distribution; the resource allocation execution sequence is formulated by using the margin weight distribution; and the margin allocation table is formed by the resource allocation execution sequence.

[0071] The value of the weight redundancy region is determined by the weight redundancy region evaluation. The value of the weight redundancy region is evaluated by considering the three key factors: the importance level of the resource, the degree of weight adjustment, and the optimization effect on the surrounding nodes. The importance level of the resource reflects the importance of the monitoring resource in the overall monitoring effect. The higher the importance level, the more cautious the adjustment. The degree of weight adjustment reflects the flexibility of the weight adjustment, which is affected by factors such as device fixation, monitoring necessity, and technical constraints. The optimization effect on the surrounding nodes reflects the optimization effect of the adjustment operation on the adjacent monitoring nodes. The more obvious the optimization effect, the higher the value of the adjustment scheme. In the GPU computing cluster area of the data center, the resource importance level is high, but the degree of weight adjustment is relatively low. The general server area around it has a high degree of weight adjustment and a significant optimization effect. The contribution of each factor is quantified by the value evaluation model. The weight of the resource importance level is 0.4, the weight of the degree of weight adjustment is 0.35, and the weight of the optimization effect on the surrounding nodes is 0.25. The adjustment intensity level is determined by the comprehensive value evaluation results. The adjustment intensity is divided into three levels: strong adjustment, medium adjustment, and slight adjustment. Strong adjustment is suitable for areas with high redundancy and high adjustment value. Medium adjustment is suitable for areas with medium redundancy and controllable adjustment risk. Slight adjustment is suitable for areas with slight redundancy and fine adjustment.

[0072] The residual weight of the surrounding monitoring nodes is redistributed according to the adjustment intensity, forming the residual weight distribution. The adjustment intensity level is used to guide the redistribution process of the weight. The redundant weight of the strong adjustment area can be transferred to the low weight nodes around it. The weight transfer algorithm is used to distribute the redundant weight of the redundant area to the low weight nodes around it according to a certain proportion. The transfer proportion is determined by the spatial distance and functional similarity between nodes. In the network device area of the data center, part of the redundant weight of the core switch node is transferred to the edge switch node, making the weight distribution of the entire network device area more balanced. The influence of weight transfer on monitoring effect is considered to ensure that weight adjustment does not reduce the monitoring quality of the key area. The weight distribution scheme is adjusted gradually by the iterative optimization method. The adjustment effect is evaluated and fine-tuned in each iteration. The redistributed weight results are organized into a residual weight distribution table, which includes node identification, original weight, adjusted weight, maximum weight, and adjustment source fields.

[0073] The resource allocation execution sequence is formulated using the residual weight distribution. Based on the allocation results in the residual weight distribution table, the weight transfer task is converted into a specific execution operation sequence. According to the complexity and influence range of weight allocation, the allocation tasks are sorted by execution difficulty, and the allocation tasks with simple and low risk are executed preferentially. The complexity of single-node weight adjustment operation is low, the complexity of multi-node collaborative allocation operation is high, and the complexity of cross-region weight transfer operation is the highest. Considering the resource constraints and time window limitations in the allocation process, a reasonable execution time arrangement is made to avoid the impact of allocation operations on normal monitoring business. In the storage system area of the data center, since the storage devices usually perform data backup and synchronization at night, the weight allocation of this area is arranged to be executed during the night low load period. The output results of the weight transfer algorithm are sorted according to the time priority: the priority of core node weight adjustment is the highest, the priority of auxiliary node weight adjustment is the second, and the priority of boundary node weight adjustment is the last. The mutual dependence between allocation tasks is analyzed to determine the execution order and parallelism of the allocation.

[0074] The residual allocation table is formed by the resource allocation execution sequence. The formulated resource allocation execution sequence is arranged into a structured residual allocation table, which records the execution information of each allocation task in detail. The residual allocation table includes key fields such as allocation task number, source node identifier, target node identifier, allocation weight amount, execution time, execution state, and completion flag. According to the execution time, the allocation tasks are time-ordered to form an allocation execution plan in time sequence. In the refrigeration system area of the data center, since the allocation of precision air conditioning equipment involves environmental parameter changes, the allocation tasks in this area are arranged to be executed during the system maintenance window, and the specific maintenance time window is marked in the allocation table. The integrity and consistency of the residual allocation table are analyzed to ensure that all weight redundancies are reasonably allocated and all weight-deficient nodes are properly supplemented. The residual allocation table is integrated with the resource management module of the monitoring system to support the automatic execution and state tracking of the allocation tasks.

[0075] An optimized conduction path is formed by monitoring path optimization using the residual allocation table. Based on the allocation results of the residual allocation table, the connection relationship between the monitoring nodes and the data conduction path are reanalyzed. Through the network topology optimization method, the optimal data conduction path after weight allocation is identified, and the optimization goal is to minimize the data transmission cost on the premise of ensuring the monitoring effect. The shortest path algorithm and the maximum flow algorithm are combined to find the optimal conduction path from the data source node to the data processing center. Considering the influence of weight allocation on the capacity of the conduction path, high-weight nodes generate more monitoring data and require more transmission bandwidth support. In the server cluster area of the data center, the weight of some edge server nodes increases after weight allocation, and the corresponding data conduction path needs to be expanded to support the increased data flow. The load balancing characteristics of the conduction path are analyzed to avoid the situation that some paths are overloaded while others are idle. Through path redundancy analysis, standby conduction paths are added to improve the reliability and fault tolerance of the monitoring network. The optimized conduction path is classified according to priority, and the main conduction path undertakes the main data transmission task, and the standby conduction path is enabled when the main path fails.

[0076] A differentiated monitoring sequence is constructed by performing sequence calibration on the optimized conduction path. The topology and transmission characteristics of the optimized conduction path are used to re-calibrate and sort the monitoring sequence. The importance level of each monitoring node in the conduction network is determined through path analysis, and nodes located on critical conduction paths have higher sequence priority. The differentiated monitoring sequence is divided into three levels according to the importance of the nodes: critical node sequence (monitoring interval 10 seconds, full parameter monitoring), important node sequence (monitoring interval 30 seconds, key parameter monitoring), and ordinary node sequence (monitoring interval 60 seconds, threshold monitoring). According to the data flow direction and transmission delay characteristics of the conduction path, the activation timing and data acquisition order of the monitoring nodes are determined. In the core network area of the data center, since the core switch node is the convergence point of multiple conduction paths, this node is designated as the first monitoring node in the sequence, with the highest priority. Considering the cooperation relationship and data dependency relationship between monitoring nodes, nodes with strong cooperation relationship are organized into continuous segments of the monitoring sequence. Each node in the monitoring sequence is assigned a specific monitoring time window through time calibration, and the time window allocation considers factors such as node weight, data processing capacity, and transmission delay. The calibrated monitoring nodes are organized into a differentiated monitoring sequence according to priority and time order, and the sequence reflects differences in monitoring frequency, parameter range, and response speed.

[0077] In some embodiments, the performing matrix operation based on the differential monitoring sequence to generate the energy consumption carbon emission analysis matrix comprises: identifying data redundant elements according to the differential monitoring sequence; performing redundancy utilization analysis on the data redundant elements to obtain a redundancy value coefficient; obtaining a simplified processing recursive chain according to the redundancy value coefficient; and performing matrix operation based on the simplified processing recursive chain to generate the energy consumption carbon emission analysis matrix.

[0078] The data redundant elements are identified according to the differential monitoring sequence. The data features generated by each monitoring node in the differential monitoring sequence are analyzed to identify data elements with information overlap and content redundancy. The data contents of different monitoring nodes are compared by a data similarity analysis method, and when the data correlation coefficient of two nodes exceeds 0.8, it is considered that there is data redundancy. An information entropy analysis method is used to quantify the information content of data, and data elements with low information entropy usually contain more redundant information. In the storage array area of the data center, the monitoring data in this area presents high similarity because the running modes of multiple disk cabinets are similar and the environmental conditions are consistent, and the information entropy values are generally low. Time series analysis is used to identify the repetitive patterns and periodic components in the data, the repetitive patterns correspond to the temporal redundancy of the data, and the periodic components correspond to the structural redundancy of the data. The spatial distribution characteristics of the data redundant elements are analyzed, and adjacent monitoring nodes are more likely to produce spatial redundant data due to similar environmental conditions. A clustering analysis method is used to group data elements with similar redundant characteristics, and the data elements in each redundant group have replaceability. In the network equipment area of the data center, the network traffic data and power consumption data generated by multiple monitoring nodes in this area have obvious redundant characteristics because the functions of the switch devices are similar. The identified data redundant elements are classified according to the redundancy degree, high redundant elements can be greatly simplified, medium redundant elements need to be selectively retained, and low redundant elements are basically kept unchanged.

[0079] The redundancy utilization analysis is performed on the data redundant elements to obtain a redundancy value coefficient. The identified data redundant elements are evaluated for value, and the potential utilization value of these redundant data in monitoring analysis is analyzed. The reliability and accuracy of the redundant data are determined through data quality evaluation, and high-quality redundant data has higher utilization value. The applicability of redundant data in different application scenarios is analyzed, and some redundant data may have important role in other analysis tasks although the value is low in current analysis. In the GPU computing cluster of the data center, the redundant load data of multiple GPU servers can be used for load prediction and resource scheduling optimization due to the high similarity of the load patterns. The representativeness of the redundant data is determined through statistical characteristic analysis, and the redundant data with high representativeness can be used as typical samples of the same type of data. The information contribution degree of the redundant data is quantified by using an information value evaluation method, and the information contribution degree reflects the influence degree of the redundant data on the overall analysis result. The calculation formula of the redundancy value coefficient is V = 0.3 × Q + 0.25 × R + 0.25 × A + 0.2 × I, wherein V is the redundancy value coefficient, Q is the data quality score, R is the representativeness, A is the applicability score, and I is the information contribution degree. The value range of the redundancy value coefficient is 0-1, and the higher the coefficient, the greater the utilization value of the redundant data. In the data center refrigeration system area, although there is redundancy in the operation data of multiple precision air conditioners, the redundancy value coefficient of these data is set to a high level because these data are important for temperature control analysis.

[0080] A simplified processing recursive chain is obtained according to the redundancy value coefficient. According to the size of the redundancy value coefficient, a corresponding data simplification processing strategy and recursive processing flow are formulated. For data elements with high redundancy value coefficients, a data fusion method is used to combine multiple redundant data into representative data. For data elements with low redundancy value coefficients, a data sampling method is used to select part of the representative samples for reservation. The data simplification process is processed by using a recursive algorithm, and the recursive chain starts from the data with the highest redundancy degree and gradually advances to the data with lower redundancy degree. In the recursive processing process, the simplification operation of each step will affect the processing decision of the next step, forming a processing chain in layers. In the server room area of the data center, for similar monitoring data of multiple Web servers, the recursive chain first processes the most similar data pairs, and then gradually expands to data combinations with lower similarity. Through the iterative processing of the recursive chain, the redundancy degree of the data is gradually reduced and the core information features are reserved.

[0081] The energy consumption and carbon emission analysis matrix is generated by performing matrix operations on the simplified processing recursive chain. The output results of the simplified processing recursive chain are used as the input data of the matrix operation, ensuring the calculation efficiency and result accuracy of the matrix operation. The simplified monitoring data is organized into a standard matrix format according to the spatial position and parameter type, with the rows corresponding to the monitoring nodes and the columns corresponding to the monitoring parameters. The data fusion and analysis are realized through basic matrix operation operations, including matrix addition for data aggregation, matrix multiplication for weight calculation, and matrix transpose for dimension transformation. The block matrix operation method is used to process large-scale monitoring data, which decomposes the large matrix into several small matrices for parallel calculation, improving the operation efficiency. In the hybrid computing area of the data center, which contains different types of devices such as CPU servers, GPU servers, and storage devices, the block matrix method is used to process monitoring data of different device types. The main features and correlation patterns in the data are extracted through eigenvalue decomposition and singular value decomposition of the matrix, identifying the dominant relationship between energy consumption and carbon emission. The processing results are organized into an energy consumption and carbon emission analysis matrix, which includes energy intensity sub-matrix, carbon emission density sub-matrix, efficiency evaluation sub-matrix, and correlation analysis sub-matrix.

[0082] The monitoring configuration parameters are generated by statistical evaluation of the energy consumption and carbon emission analysis matrix. The average energy consumption level of each area in the data center is determined through mean analysis of the energy intensity sub-matrix, identifying high-energy consumption areas and low-energy consumption areas. The stability and variability of carbon emissions in each area are evaluated through variance analysis of the carbon emission density sub-matrix, with larger variance indicating more volatile carbon emissions requiring more frequent monitoring. In the Web server cluster of the data center, the carbon emission variance of this area is significantly higher than that of the storage area, requiring high-frequency monitoring configuration. The areas with low energy utilization efficiency are identified through statistical analysis of the efficiency evaluation sub-matrix, which need to be configured with emission reduction optimization monitoring mode. The correlation analysis sub-matrix is used for correlation analysis to identify the correlation degree and propagation relationship of carbon emissions between different areas. According to the statistical evaluation results of the four sub-matrices, monitoring configuration parameters are generated, including monitoring frequency configuration, data transmission configuration, storage strategy configuration, and alarm threshold configuration. The monitoring frequency configuration considers the change frequency of the energy intensity sub-matrix and the carbon emission density sub-matrix to determine the sampling interval. The data transmission configuration determines the transmission priority based on the evaluation results of the efficiency evaluation sub-matrix, with real-time transmission for areas with high efficiency evaluation value and batch transmission for areas with low efficiency evaluation value. The generated monitoring configuration parameters are applied to the actual monitoring system, and the intelligent monitoring of data center energy consumption and carbon emission is finally completed.

[0083] In order to perform the above-mentioned method embodiment corresponding to a kind of data center energy consumption and carbon emission intelligent monitoring method, to realize corresponding function and technical effect. Referring to Figure 2 , Figure 2A structural block diagram of the data center energy consumption and carbon emission intelligent monitoring system 200 is shown. For ease of illustration, only the parts related to the present embodiment are shown. The data center energy consumption and carbon emission intelligent monitoring system 200 provided by the present embodiment includes:

[0084] The data acquisition module 201 is configured to monitor energy consumption monitoring data and environmental parameter data in the data center operating environment, perform time series analysis on the energy consumption monitoring data to identify energy consumption patterns, and generate hierarchical quality parameters based on quality requirements of the energy consumption monitoring data.

[0085] The marginal analysis module 202 is configured to perform marginal utility evaluation based on the energy consumption patterns and the hierarchical quality parameters to determine precision configuration boundaries, perform influence intensity evaluation on the precision configuration boundaries to generate carbon source intensity indexes, and convert the carbon source intensity indexes into influence coverage areas using spatial mapping processing.

[0086] The propagation analysis module 203 is configured to analyze the change trajectory of the environmental parameter data to generate a carbon propagation network, perform frequency analysis on the carbon propagation network to obtain key propagation paths, map the key propagation paths into carbon flow mobility indexes, and reconstruct a monitoring network based on the carbon flow mobility indexes to form a path optimization table.

[0087] The range control module 204 is configured to evaluate the abnormal propagation range of the influence coverage area to determine a monitoring expansion area, perform dynamic range adjustment on the monitoring expansion area to generate range adjustment parameters, fuse the range adjustment parameters with the path optimization table to construct an adaptive monitoring configuration, and identify an emission reduction potential area based on the adaptive monitoring configuration.

[0088] The matrix processing module 205 is configured to decompose the emission reduction potential area into spatial monitoring nodes, perform utility weight distribution on the spatial monitoring nodes to generate a differentiated monitoring sequence, perform matrix operations based on the differentiated monitoring sequence to generate an energy consumption and carbon emission analysis matrix, perform statistical evaluation on the energy consumption and carbon emission analysis matrix to generate monitoring configuration parameters, and complete data center energy consumption and carbon emission intelligent monitoring.

[0089] The data center energy consumption and carbon emission intelligent monitoring system 200 described above can implement the data center energy consumption and carbon emission intelligent monitoring method of the method embodiment described above. The optional items in the method embodiment described above are also applicable to the present embodiment, and will not be described in detail here. The remaining content of the present embodiment can refer to the content of the method embodiment described above, and will not be described in detail in the present embodiment.

[0090] The above examples are intended to illustrate and deduce the technical solutions of the present application, and to completely describe the technical solutions, objects and effects of the present application. The purpose is to make the public more thoroughly and comprehensively understand the disclosed content of the present application, and does not limit the protection scope of the present application.

[0091] The above examples are not based on an exhaustive enumeration of the present application, and there can be many other unlisted embodiments. Any substitutions and improvements made without violating the concept of the present application are within the scope of protection of the present application.

Claims

1. A data center energy consumption carbon emission intelligent monitoring method, characterized in that, The method comprises the following steps: monitoring energy consumption monitoring data and environmental parameter data in the operation environment of the data center, performing time series analysis on the energy consumption monitoring data to identify energy consumption patterns, including: segmenting and analyzing the energy consumption monitoring data in the frequency domain, decomposing the energy consumption time series data into trend items, periodic items and random fluctuation items, identifying four energy consumption patterns of basic load pattern, business peak pattern, maintenance operation pattern and emergency response pattern through clustering analysis, generating hierarchical quality parameters based on the quality requirements of the energy consumption monitoring data, including: dividing the quality requirements into three levels of accurate monitoring, standard monitoring and rough monitoring, generating corresponding hierarchical quality parameters according to data acquisition accuracy, data integrity and time delay requirements; based on the energy consumption patterns and the hierarchical quality parameters, performing marginal utility evaluation to determine the precision configuration boundary, including: identifying the consumption intensity peak band for the energy consumption patterns; generating utility correlation points according to the correlation between the consumption intensity peak band and the hierarchical quality parameters; generating precision demand domain by stimulating the change of precision demand distribution using the utility correlation points; determining the precision configuration boundary through the precision demand domain, generating carbon source intensity index by evaluating the influence intensity of the precision configuration boundary, and converting the carbon source intensity index into an influence coverage area by using spatial mapping processing; analyzing the change trajectory of the environmental parameter data to generate a carbon propagation network, performing frequency analysis on the carbon propagation network to obtain key propagation paths, mapping the key propagation paths to carbon flow mobility indicators, and reconstructing the monitoring network to form a path optimization table according to the carbon flow mobility indicators; evaluate the abnormal propagation range of the influence coverage area to determine the monitoring expansion area, perform dynamic range adjustment on the monitoring expansion area to generate range adjustment parameters, fuse the range adjustment parameters with the path optimization table to build an adaptive monitoring configuration, and identify the emission reduction potential area based on the adaptive monitoring configuration; decompose the emission reduction potential area into spatial monitoring nodes, assign utility weights to the spatial monitoring nodes to generate a differentiated monitoring sequence, perform matrix operation based on the differentiated monitoring sequence to generate an energy consumption carbon emission analysis matrix, perform statistical evaluation on the energy consumption carbon emission analysis matrix to generate monitoring configuration parameters, and complete intelligent monitoring of data center energy consumption carbon emission.

2. The method of claim 1, wherein, The method of converting the carbon source intensity index into an influence coverage area by using spatial mapping processing comprises the following steps: According to the carbon source intensity index, identify the mapping density difference to generate dense mapping areas and sparse mapping areas; use the sparse mapping area to infer the influence range to form an expanded inference area; adopt the expanded inference area and the dense mapping area to perform spatial fusion to generate a complete coverage segment; perform spatial optimization on the complete coverage segment to build an influence coverage area.

3. The method of claim 1, wherein, The method of performing frequency analysis on the carbon propagation network to obtain key propagation paths comprises the following steps: obtain the propagation frequency characteristics of the carbon propagation network; According to the propagation frequency characteristics, identify the low-frequency propagation area to generate a low-frequency area parameter; use the low-frequency area parameter to passively adjust the propagation signal to generate an adjusted propagation signal; identify the key propagation path through the adjusted propagation signal.

4. The method of claim 1, wherein, The evaluation of the abnormal propagation range of the influence coverage determines a monitoring expansion area, comprising: According to the propagation range anomaly measurement of the influence coverage, a low anomaly area is identified; Using the low anomaly area as a stability reference, a reference quality distribution is formed; Using the reference quality distribution, a range value derivation is performed to generate a derived range matrix; According to the derived range matrix, spatial mapping is determined to determine the monitoring expansion area.

5. The method of claim 1, wherein, The utility weight distribution of the spatial monitoring node generates a differentiated monitoring sequence, comprising: According to the utility weight distribution of the spatial monitoring node, a weight redundancy area is identified; According to the weight redundancy area, a deployable margin is determined to form a margin deployment table; Using the margin deployment table, a monitoring path optimization is formed to form an optimized conduction path; According to the optimized conduction path, a sequence calibration is performed to build a differentiated monitoring sequence.

6. The method of claim 1, wherein, The matrix operation based on the differentiated monitoring sequence generates an energy consumption carbon emission analysis matrix, comprising: According to the differentiated monitoring sequence, data redundancy elements are identified; According to the data redundancy elements, a redundancy utilization analysis is performed to obtain a redundancy value coefficient; According to the redundancy value coefficient, a simplified processing recursive chain is obtained; Through the simplified processing recursive chain, the matrix operation is performed to generate the energy consumption carbon emission analysis matrix.

7. The method of claim 1, wherein, Using the utility correlation point to generate precision demand domain by stimulating precision demand distribution change, comprising: According to the utility correlation point, the precision configuration state is analyzed to generate a configuration margin table; According to the configuration margin table, a precision demand distribution change is stimulated to form a density optimization curve; Using the density optimization curve, a configuration path is identified to generate an advantageous configuration path; According to the advantageous configuration path, a spatial mapping is performed to generate a precision demand domain.

8. The method of claim 5, wherein, According to the weight redundancy area, a deployable margin is determined to form a margin deployment table, comprising: Based on the weight redundancy area, the margin deployment value is evaluated to determine the deployment strength, and the margin deployment value includes the resource importance level of the redundancy area, the deployable degree and the optimization degree of the surrounding nodes; According to the deployment strength, the margin weight of the surrounding monitoring nodes is redistributed to form a margin weight distribution; Using the margin weight distribution, a resource deployment execution sequence is developed; Through the resource deployment execution sequence, a margin deployment table is formed.

9. A data center energy consumption and carbon emission intelligent monitoring system, characterized in that, Comprising: The data acquisition module is used for monitoring the energy consumption monitoring data and environmental parameter data in the data center running environment, and performing time series analysis on the energy consumption monitoring data to identify energy consumption patterns, including: segmenting and frequency domain analyzing the energy consumption monitoring data, decomposing the energy consumption time series data into trend items, periodic items and random fluctuation items, identifying four energy consumption patterns through clustering analysis, including basic load pattern, business peak pattern, maintenance operation pattern and emergency response pattern, generating hierarchical quality parameters based on the quality demand of the energy consumption monitoring data, including: dividing the quality demand into three levels of accurate monitoring, standard monitoring and rough monitoring, and generating corresponding hierarchical quality parameters according to data acquisition accuracy, data integrity and time delay demand; The marginal analysis module is configured to determine the precision configuration boundary based on the energy consumption pattern and the hierarchical quality parameter, including: identifying a consumption intensity peak band for the energy consumption pattern; generating an utility association point by associating the consumption intensity peak band with the hierarchical quality parameter; generating a precision demand domain by stimulating a precision demand distribution change using the utility association point; determining a precision configuration boundary by the precision demand domain; generating a carbon source intensity index by evaluating the influence intensity of the precision configuration boundary; and converting the carbon source intensity index into an influence coverage area by spatial mapping processing; The propagation analysis module is configured to analyze the change trajectory of the environmental parameter data to generate a carbon propagation network, perform frequency analysis on the carbon propagation network to obtain a key propagation path, map the key propagation path into a carbon flow liquidity index, and reconstruct a monitoring network based on the carbon flow liquidity index to form a path optimization table; The range control module is configured to evaluate the abnormal propagation range of the influence coverage area to determine a monitoring expansion area, perform dynamic range adjustment on the monitoring expansion area to generate a range adjustment parameter, fuse the range adjustment parameter with the path optimization table to build an adaptive monitoring configuration, and identify an emission reduction potential area based on the adaptive monitoring configuration; The matrix processing module is configured to decompose the emission reduction potential area into spatial monitoring nodes, assign utility weights to the spatial monitoring nodes to generate a differentiated monitoring sequence, perform matrix operation based on the differentiated monitoring sequence to generate an energy consumption carbon emission analysis matrix, perform statistical evaluation on the energy consumption carbon emission analysis matrix to generate a monitoring configuration parameter, and complete intelligent monitoring of energy consumption carbon emissions in the data center.

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