A carbon emission information data analysis method and system based on CIM

Abstract

The present application relates to the field of smart city environment management, and more particularly to a carbon emission information data analysis method and system based on CIM, which overcomes the drawback of ambiguous spatial location of emission data in traditional methods by implementing precise coding matching and binding of CIM spatial units and carbon emission data. The above spatial characteristics and real-time dynamic data are input into a strength analysis model based on machine learning. Personalized intelligent evaluation is achieved. After identifying high-emission units, the method can use the established precise spatial mapping for reverse intelligent tracing, quickly locating and quantifying specific activities and sources causing high emissions, and achieving precise diagnosis. Based on the clear diagnosis conclusion, further combined with the knowledge base and optimization algorithm, customized and quantifiable emission reduction strategies are automatically generated. Finally, differentiated results are pushed to different roles.

Description

Technical Field

[0001] This application relates to the field of smart city environmental management technology, and in particular to a carbon emission information data analysis method and system based on CIM. Background Technology

[0002] CIM carbon emission analysis refers to the technical approach of using city information models as a spatial data integration and visualization carrier to initially integrate and display carbon emission-related data.

[0003] Current mainstream carbon emission analysis methods have significant shortcomings in achieving precise and dynamic management. First, the data foundation is relatively weak. Multi-source emission data, such as energy consumption, production, and transportation, are loosely correlated with geographical location information, making it difficult to accurately locate and visualize emission sources at the micro-scale of buildings and facilities, resulting in data uncertainty regarding their origin. Second, the analysis methods themselves are relatively rigid, typically employing static emission coefficients or simple statistical models, which are ill-suited to the dynamic characteristics and complex relationships of different regions and types of emission units, leading to limited computational accuracy. Furthermore, existing schemes primarily rely on historical statistics and post-hoc accounting, lacking the ability to scientifically predict future emission trends and quantitatively simulate and evaluate the effects of various emission reduction measures, thus failing to support forward-looking and differentiated emission reduction decision-making.

[0004] When faced with more complex scenarios in actual management, the limitations of existing technological frameworks become even more apparent. On the one hand, when it is necessary to conduct in-depth tracing and responsibility delineation of emissions that are linked across spaces and systems, such as indirect emissions caused by the operation of adjacent facilities, existing methods struggle to clarify multi-source contributions and identify key driving factors. On the other hand, with the deepening of carbon management practices, emerging data such as carbon capture and removal (CCD) and direct green power supply have appeared, whose carbon flow direction is opposite to or has offsetting properties with traditional emission data. Existing systems are mainly designed for positive emission activities and lack effective identification of such special carbon flow data, which may lead to distorted accounting results and affect the integrity of management. Summary of the Invention

[0005] In order to solve one or more problems in the prior art, the main objective of this application is to provide a carbon emission information data analysis method and system based on CIM.

[0006] To achieve the aforementioned objectives, this application proposes a carbon emission information data analysis method based on CIM, the method comprising:

[0007] Real-time acquisition of multi-source heterogeneous carbon emission data, preprocessing of the multi-source heterogeneous carbon emission data to obtain a structured carbon emission dataset;

[0008] Based on the processing results, each data point in the carbon emission dataset is associated with a spatial unit in the CIM 3D model to determine the mapping relationship between carbon emission data and geographic spatial location.

[0009] Based on the association and binding results, the attribute parameters of the spatial unit are extracted from the CIM 3D model, and the attribute parameters are used as spatial characteristic parameters corresponding to carbon emissions.

[0010] The spatial characteristic parameters are input into a preset intensity analysis model, and the carbon emission intensity data of each space are calculated through the intensity analysis model.

[0011] Based on the mapping relationship, carbon emission sources are traced for the spatial units corresponding to the carbon emission intensity data;

[0012] Based on the carbon emission source tracing and carbon emission intensity data, an emission reduction strategy is generated for each spatial unit;

[0013] The emission reduction strategy and carbon emission intensity data are sent to the corresponding target information platform through a difference interface.

[0014] This application also provides a carbon emission information data analysis system based on CIM, including:

[0015] The acquisition module is used to acquire multi-source heterogeneous carbon emission data in real time, and preprocess the multi-source heterogeneous carbon emission data to obtain a structured carbon emission dataset.

[0016] The association module is used to associate each data point in the carbon emission dataset with a spatial unit in the CIM 3D model based on the processing results, and to determine the mapping relationship between carbon emission data and geographic spatial location.

[0017] The extraction module is used to extract the attribute parameters of the spatial unit from the CIM 3D model based on the association binding result, and use the attribute parameters as spatial feature parameters corresponding to carbon emissions;

[0018] The calculation module is used to input the spatial characteristic parameters into a preset intensity analysis model and calculate the carbon emission intensity data of each space through the intensity analysis model.

[0019] The source tracing module is used to trace the source of carbon emissions for the spatial units corresponding to the carbon emission intensity data based on the mapping relationship.

[0020] The strategy generation module is used to generate an emission reduction strategy for each spatial unit based on the carbon emission source tracing and carbon emission intensity data.

[0021] The sending module is used to send the emission reduction strategy and carbon emission intensity data to the corresponding target information platform through the difference interface.

[0022] This application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of any of the methods described above.

[0023] This application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of any of the methods described above.

[0024] The carbon emission information data analysis method and system based on CIM in this application overcomes the shortcomings of ambiguous spatial location of emission data in traditional methods by implementing precise coding matching and binding between CIM spatial units and carbon emission data, enabling each data set to obtain digital coordinates accurate to the specific building or facility. Building upon this, the method goes beyond simple data visualization, deeply exploring the value of the CIM model and automatically extracting spatial feature parameters related to carbon emission mechanisms, injecting rich spatial contextual information into the analysis. Subsequently, by inputting the aforementioned spatial features and real-time dynamic data into a machine learning-based intensity analysis model, personalized intelligent assessment is achieved. Once high-emission units are identified, this method can utilize the established precise spatial mapping for reverse intelligent source tracing, quickly locating and quantifying the specific activities and sources leading to excessive emissions, achieving accurate diagnosis. Based on clear diagnostic conclusions, further combining a knowledge base and optimization algorithms, customized and quantifiable emission reduction strategies are automatically generated. Finally, differentiated results are pushed to different roles. Attached Figure Description

[0025] Figure 1 This is a flowchart illustrating a carbon emission information data analysis method based on CIM according to an embodiment of this application;

[0026] Figure 2 This is a flowchart illustrating a carbon emission information data analysis method based on CIM according to an embodiment of this application;

[0027] Figure 3 This is a schematic block diagram of a CIM-based carbon emission information data analysis system according to an embodiment of this application;

[0028] Figure 4 This is a schematic block diagram of the structure of a computer device according to an embodiment of this application.

[0029] The realization of the purpose, functional features and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0030] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0031] Reference Figure 1 This application provides a carbon emission information data analysis method based on CIM, the method comprising:

[0032] S1. Acquire multi-source heterogeneous carbon emission data in real time, preprocess the multi-source heterogeneous carbon emission data, and obtain a structured carbon emission dataset.

[0033] S2. Based on the processing results, associate each data point in the carbon emission dataset with a spatial unit in the CIM 3D model to determine the mapping relationship between carbon emission data and geographic spatial location.

[0034] S3. Based on the association and binding results, extract the attribute parameters of the spatial unit from the CIM three-dimensional model, and use the attribute parameters as spatial characteristic parameters corresponding to carbon emissions;

[0035] S4. Input the spatial characteristic parameters into the preset intensity analysis model, and calculate the carbon emission intensity data of each space through the intensity analysis model;

[0036] S5. Based on the mapping relationship, trace the source of carbon emissions for the spatial units corresponding to the carbon emission intensity data;

[0037] S6. Based on the carbon emission source tracing and carbon emission intensity data, generate emission reduction strategies for each spatial unit;

[0038] S7. Send the emission reduction strategy and carbon emission intensity data to the corresponding target information platform through the difference interface.

[0039] As described in steps S1-S3 above, step one automatically connects data streams from different sources via a pre-configured standard application programming interface and data acquisition protocol. These data streams include: readings from smart meters and gas meters in buildings aggregated through an IoT gateway, online data of industrial waste gas emissions from environmental protection monitoring networks, and real-time meteorological information accessed through service interfaces. These data naturally differ in format, frequency, and communication protocols, i.e., "multi-source heterogeneous". A built-in data preprocessing engine first parses the incoming raw data, converting it into an internally unified intermediate format. Next, it executes data cleaning rules, such as removing outliers exceeding physical limits and filling in short-term data gaps caused by communication interruptions. Finally, it standardizes the units of measurement for all data and assigns a unique and timestamped data identifier to each valid data point. After this series of operations, the originally chaotic raw data is transformed into a well-organized, clean, and computationally usable structured dataset. Step two is crucial for connecting the information space and the physical space. First, each entity with practical significance in the CIM 3D model, such as an independent office building, a specific industrial workshop, or a section of urban road, is assigned a globally unique spatial code. This code is like the entity's "ID number" in the digital world. The core principle is "code matching." When preprocessed structured carbon emission data flows in, the system extracts the location information contained in each data point. This location information may be a specific building number or a set of geographic coordinates. Based on this information, a target spatial code is matched to the data entry. For example, an energy consumption data point from "Meter 001, 10th Floor, Block B, Building A" will be precisely associated with the corresponding spatial code of the building, floor, and even specific room in the CIM model. After a successful match, the data is strongly bound to the spatial unit, thus establishing a one-to-one mapping relationship between carbon emission data and geographic spatial location. This changes the spatial ambiguity of carbon emission data. It enables each piece of carbon emission data to be accurately located to a specific point in the city's 3D model, realizing the transformation from "numerical value" to "location," creating the prerequisite for refined spatial management. Step three, after binding data and space, begins to deeply utilize the rich information inherent in the CIM model itself. This involves activating the CIM model's semantic parsing and feature extraction capabilities. Based on preset rules, the system automatically accesses the attribute sets of associated spatial units. For a building, physical parameters such as its building age, structural type, window-to-wall ratio, and roof insulation materials may be extracted; for an industrial park, information such as its production process layout and main equipment types may be extracted; for an area, environmental parameters such as average floor area ratio, green space ratio, and prevailing wind direction may be extracted. These parameters, directly obtained from the CIM model and closely related to the inherent attributes of spatial entities, are collectively referred to by the system as "spatial feature parameters." This injects deep spatial contextual information into carbon emission analysis.These characteristic parameters are key factors in understanding "why carbon emissions are as they are here," enabling subsequent analysis to move beyond isolated data points and instead focus on understanding the characteristics of the spatial entity itself, thus greatly enriching the dimensions of the analysis.

[0040] As described in steps S4-S7 above, step four is a crucial step in introducing artificial intelligence for core calculations. The spatial characteristic parameters obtained in the previous step, along with real-time or near-real-time energy consumption data, are input into a machine learning model trained on a large amount of historical data. This model is the "intensity analysis model." The model can learn and understand complex nonlinear relationships. For example, it can learn patterns such as "in summer, buildings with a high proportion of glass curtain walls show a significantly stronger correlation between their air conditioning energy consumption and outdoor temperature." During calculations, the model does not simply apply fixed emission coefficients, but rather comprehensively considers current spatial characteristics, real-time meteorological conditions, and even dynamically changing grid carbon emission factors to dynamically calculate the carbon emission intensity of each spatial unit at any given moment. This improves the accuracy and timeliness of carbon emission calculations. The calculation results are dynamic and personalized, more realistically reflecting the actual emission performance of different spatial units under different conditions, overcoming the large errors of the traditional static coefficient method. Step five: When abnormally high carbon emission intensity is identified in certain spatial units, this step initiates deep diagnostics. The technical principle is to use the precise "mapping relationship" established in step two as a map for reverse tracing. First, high-emission units are identified. Then, based on the mapping relationship, all specific carbon emission data items bound to that unit are quickly retrieved, such as electricity consumption for lighting, air conditioning, and process steam consumption. By analyzing this detailed data and combining it with the contribution calculated by the model in step four, the system can quantitatively identify which link or activity is the main cause of excessive emissions. This is similar to not only knowing that someone has a high body temperature but also accurately diagnosing which organ's inflammation is causing it. The core technical effect is the realization of "precise diagnosis" of carbon emission problems. It shifts the management perspective from "which region has high emissions" to "what specific reasons are causing high emissions in this region," providing a solid basis for developing targeted measures. Step six, based on precise source tracing results and specific carbon emission intensity data, utilizes a built-in emission reduction measure knowledge base, which stores various types of emission reduction technologies, management measures, their applicable scope, costs, and expected emission reduction effects. Based on the type and characteristics of the traced "dominant carbon emission sources," a batch of candidate measures is initially screened from the knowledge base. Subsequently, combining the specific spatial characteristic parameters of the spatial unit (such as whether there are rooftop photovoltaic installations) and constraints (such as budget), optimization algorithms are used to combine, simulate, and evaluate candidate measures, ultimately generating one or more customized strategy solutions with optimal emission reduction effects under the premise of technical feasibility and economic rationality. The analysis conclusions are directly transformed into actionable guidelines. Step seven involves interface adaptation based on roles and needs. Instead of pushing the same information to all users, processed information is precisely delivered through different data interfaces and service protocols.For government regulatory platforms, the main data disseminated includes regional carbon emission heat maps, warnings of exceeding emission limits, and summary analysis reports. For enterprise energy management platforms, detailed emission inventories of their assets and cost-benefit analyses of emission reduction plans are provided. For public information services, the overall carbon emission level of the region and suggestions for low-carbon living are shared. This approach effectively releases analytical value and facilitates multi-stakeholder collaboration, ensuring that accurate information is delivered to the right decision-makers or stakeholders in the right format.

[0041] As described above, by implementing precise coding, matching, and binding between CIM spatial units and carbon emission data, this method overcomes the drawbacks of ambiguous spatial locations in traditional methods, ensuring that each data point obtains precise digital coordinates down to the specific building or facility. Building upon this foundation, the method goes beyond simple data visualization, deeply exploring the value of the CIM model and automatically extracting spatial feature parameters related to carbon emission mechanisms, injecting rich spatial contextual information into the analysis. Subsequently, by inputting these spatial features along with real-time dynamic data into a machine learning-based intensity analysis model, personalized intelligent assessment is achieved. Once high-emission units are identified, this method can utilize the established precise spatial mapping for reverse intelligent source tracing, quickly locating and quantifying the specific activities and sources leading to excessive emissions, achieving accurate diagnosis. Based on clear diagnostic conclusions, further combining a knowledge base and optimization algorithms, customized and quantifiable emission reduction strategies are automatically generated. Finally, differentiated results are pushed to different roles.

[0042] Reference Figure 2 In one embodiment, the step of associating each data point in the carbon emission dataset with a spatial unit in the CIM 3D model based on the processing results to determine the mapping relationship between carbon emission data and geospatial location includes:

[0043] S21. Assign a unique spatial code to each spatial unit with independent geographical significance in the CIM 3D model;

[0044] S22. For each piece of data in the structured carbon emission dataset, add a spatial code to identify the spatial location of the source.

[0045] S23. Match the spatial code of the carbon emission data with the unique spatial code of the spatial unit in the CIM three-dimensional model, and determine whether the match is successful.

[0046] S24. When a match is successful, the carbon emission data is bound to the corresponding spatial unit, and the binding relationship is recorded.

[0047] As described above, Step One first performs semantic parsing and unit division on the imported CIM 3D model. The technical principle is to automatically or semi-automatically identify each entity with independent management or accounting significance based on physical separations such as building components, floor boundaries, and independent equipment bases. For example, a complete building is divided into one unit; different functional areas within a building, such as office floors, data center server rooms, and underground parking garages, can be divided into sub-units; independent workshops, chimneys, and storage tanks within a factory area are also considered independent units. Subsequently, a globally unique code is generated for each identified spatial unit. This code typically possesses structured features, which may integrate administrative division codes, land parcel numbers, building serial numbers, and sub-unit type identifiers. This creates a precise and unique digital identity for physical entities in the digital space. Step Two operates on the cleaned and standardized carbon emission dataset. Spatial semantic enhancement is performed on the data records. The system parses the information contained in each data record that indicates its source location. For example, energy consumption data uploaded from a smart meter might contain the physical address or building number of the meter in its metadata; a fuel consumption record submitted by a company might include the name of the corresponding workshop or production line; and exhaust gas data reported by a mobile monitoring device might include GPS coordinates. Using Geographic Information System (GIS) technology or a pre-defined rule mapping table, this descriptive location information (address, name, coordinates) is converted and assigned a standard spatial code consistent with the CIM spatial unit coding system established in the first step. Step three involves performing a keyword-based precise or fuzzy query and comparison. The spatial code added to the data record in the second step is used as the query condition, and the data is searched and compared in the CIM spatial unit coding library established in the first step. Ideally, the data code should perfectly match a spatial unit code, indicating a successful match. This is equivalent to the address on a delivery slip perfectly matching the standard address in the database. The logical link between the data and the spatial entity is then verified. In step four, when a match is successful, a persistent "binding" operation is performed. A relational record is created and stored in the relational or graph database in the system backend. This record contains at least the unique ID of the data entry, the unique code of the spatial unit, and the binding timestamp, forming a robust and traceable "data-space" mapping relationship. This not only supports real-time visualization but, more importantly, provides a relational database for all subsequent spatial analysis, unit-based statistics, and reverse tracing. When analyzing the carbon emissions of a particular space, all relevant data can be quickly aggregated directly through this relationship; when an anomaly is detected in the data, the specific responsible space can be immediately located.

[0048] In one embodiment, after the step of matching the spatial code of the carbon emission data with the unique spatial code of the spatial unit in the CIM 3D model and determining whether the match is successful, the method further includes:

[0049] If a match fails, analyze the spatial location information carried by the carbon emission data entries that failed to match.

[0050] Based on the analysis results, the coordinates of the spatial location information are extracted, and the nearest spatial unit is searched within a preset buffer radius according to the coordinates for matching, and the matching success is analyzed.

[0051] If a match fails, the information of the failed carbon emission data entry and the information of several candidate spatial units in the CIM 3D model will be displayed side by side on the interactive interface.

[0052] Real-time reception of user selection instructions for a target spatial unit specified from the plurality of candidate spatial units;

[0053] If no valid selection instruction is received, the carbon emission data entry will be marked as spatially correlated anomaly. The marked carbon emission data will not be included in the calculation of carbon emission intensity and source tracing of the mapping relationship.

[0054] As described above, the first step involves initiating an analysis subroutine when the spatial code of a data entry cannot find a completely matching entry in the CIM coding library. The principle is to trace back and deeply analyze the original location description information of the data entry. For example, an industrial energy consumption data entry might carry a text description like "Boiler Room No. 2" instead of a standard code. The system will use natural language processing techniques or query a pre-defined synonym mapping table to understand the actual meaning of "Boiler Room No. 2". For data carrying geographic coordinates, the system directly reads its latitude and longitude values. Essentially, this step attempts to retrieve spatial clues from the data's "raw memory" after the standard coding path fails. In the second step, if the previous step resolved specific geographic coordinates, proximity matching based on spatial location will be initiated. The technical principle is to utilize buffer analysis and spatial proximity algorithms in geographic information systems. A circular buffer area with a preset radius, such as 50 meters, is generated centered on the resolved coordinate point. Subsequently, the CIM model searches for all spatial geometric center points or boundaries that fall within this buffer area. Next, the precise spatial distance between the coordinate point and these candidate units is calculated, and the closest one is selected as the most likely matching target for a secondary association. This solves the problem of "being close but unable to match" caused by slight coordinate drift, surveying errors, or simplification of the CIM model. The third step involves initiating a human-machine collaboration mechanism when automatic matching (whether coded matching or coordinate proximity matching) fails. This mechanism works by constructing an interactive interface to assist decision-making. In a specific area of ​​the visualization interface, key information about the failed match is clearly displayed, such as the numerical value, timestamp, and the parsed location description or coordinates. Simultaneously, in another area of ​​the interface, based on intelligent inference (e.g., other units belonging to the same company or other units with similar names), several most likely candidate spatial units in the CIM model are listed, along with preview images or basic information of these units. The user then reviews this information and makes a final judgment through clicking or selection. The fourth step sets a waiting time limit for the user's decision. If no valid selection instruction is received within the specified time, the system will execute the final contingency plan. The underlying technology is data quality grading and process isolation. This data entry is tagged with a specific "spatial association anomaly" label and moved to a dedicated pending database or log, separating it from successfully associated data. Most importantly, in all subsequent core calculation processes based on precise spatial mapping, such as carbon emission intensity calculation and source tracing, all data carrying this anomaly label will be actively filtered and excluded. On one hand, this prevents "dirty data" with unclear or erroneous location information from contaminating subsequent analysis results, ensuring the purity and reliability of the input data to the core analysis engine; on the other hand, it creates a clear pending list.

[0055] In one embodiment, the step of tracing the carbon emission sources of spatial units corresponding to the carbon emission intensity data based on the mapping relationship includes:

[0056] Based on the carbon emission intensity data, spatial units whose carbon emission intensity exceeds a preset intensity threshold are identified as target traceability units;

[0057] Based on the mapping relationship, all carbon emission data entries bound to the target traceability unit are analyzed to form a direct emission source list;

[0058] In the spatial topology and business logic relationships stored in the CIM 3D model, analyze other spatial units that are associated with the target traceability unit;

[0059] Obtain carbon emission intensity data of the other spatial units, and identify units whose intensity exceeds the correlation threshold as associated emission sources to form an associated emission source list;

[0060] For each source in the direct emission source inventory and the associated emission source inventory, the type of each source is analyzed, and quantitative data of emission activities corresponding to that source are extracted from the structured carbon emission dataset;

[0061] Match the corresponding dynamic emission factor based on the type of each source and the spatial characteristic parameters of the target traceability unit;

[0062] Based on the quantitative data of emission activities from the source and the matching dynamic emission factors, the contribution and contribution ratio to the carbon emission intensity of the target traceability unit are calculated.

[0063] Based on the contribution amount and contribution ratio, the dominant carbon emission source of the carbon emission intensity of the target traceability unit is determined.

[0064] As mentioned above, the first step, identifying target traceability units, does not involve indiscriminate in-depth analysis of all units, but rather target locking. The technical principle is to set a configurable carbon emission intensity threshold, which can be an industry standard, regional average, or historical baseline. The calculated real-time carbon emission intensity of each spatial unit is automatically compared with this threshold. If the intensity of a unit consistently or significantly exceeds the threshold, it is marked as a "target traceability unit." The second step is to construct a direct emission source list. After locking the target, the system begins the first layer of evidence collection. Utilizing the established and maintained precise "data-space" mapping database, all original carbon emission data entries successfully bound to the target traceability unit are directly retrieved using the unique spatial code of the target traceability unit as the query key. These entries are the most direct evidence, such as all electricity bills, gas consumption records, and internal vehicle refueling data for the building. These data entries are compiled into a clear "direct emission source list." The third step is to construct a related emission source list, not only examining the target unit itself but also activating the pre-built spatial topology network and business logic relationship graph in the CIM model. Spatial topology relationships refer to physical adjacency, connection, and inclusion relationships. For example, other buildings adjacent to the target building, workshops sharing the same ventilation ducts, and facilities located downwind of it. Business logic relationships refer to management or process connections, such as belonging to the same company, being in the same upstream or downstream of the same industrial chain, or sharing energy supply. Through these relationship networks, a batch of "other spatial units" with potential influence relationships with the target unit are automatically discovered. Subsequently, carbon emission intensity data of these related units are obtained, and a correlation threshold is used again for screening, identifying units with higher emissions as "related emission sources," forming a second list. This breaks through the limitations of traditional analysis that only focuses on the boundary of responsibility, and can discover external related sources that indirectly cause or aggravate the emission problems of the target unit, such as heat emissions from adjacent data centers increasing the air conditioning load of this building. The fourth step quantifies the contribution of each source. After obtaining two source lists, the precise quantitative analysis stage begins. The principle is a standardized calculation process: First, for each source in the list, its type is identified based on its data characteristics, such as electricity consumption, natural gas combustion, or gasoline consumption, and corresponding quantitative activity data, such as electricity consumption and gas consumption, are extracted from the structured dataset. Next, instead of using a uniform emission factor, the most appropriate emission factor is matched from a dynamic factor library based on the type of the source and the specific spatial characteristics of the target traceability unit. For example, when calculating the carbon emissions from building electricity consumption, the factor considers the real-time emission intensity of the power grid in the building's area. Finally, by multiplying the activity quantification data with the matched dynamic emission factor, the absolute contribution of the single source to the total carbon emission intensity of the target unit is accurately calculated, and its contribution ratio is then determined. Fifth step: Identify the dominant carbon emission source. After quantifying the contribution of all sources, the final diagnostic judgment is performed.The technical principle is based on ranking and threshold analysis of contribution ratio data. The one or several sources with the highest contribution ratio are identified as the "dominant carbon emission source" causing the carbon emission intensity of the target traceability unit to be too high. For example, the analysis results may show that for a high-emission commercial building, the electricity consumption of IT equipment in its data center contributes 65% of the total emissions, while air conditioning and lighting contribute only 20% and 15%, respectively. In this case, the electricity consumption of IT equipment would be identified as the dominant carbon emission source.

[0065] In this embodiment, traditional methods, when tracing emission sources, often rely on empirical judgment or simple listing of direct data, making it difficult to handle complex indirect impacts and quantify the degree of responsibility from different sources. This embodiment, through a dual-track evidence collection system of "direct list plus related list," fully utilizes the systematic correlation information contained in CIM to ensure the breadth of tracing. Furthermore, through quantitative contribution analysis based on dynamic factors, the depth and accuracy of tracing are ensured.

[0066] It is worth noting that in the current operation of cities and industrial parks, there exist many complex symbiotic entities with closely adjacent physical spaces and deeply coupled energy systems. For example, a high-performance computing data center generates a massive amount of waste heat during operation. If this waste heat cannot be effectively dissipated, it will significantly increase the air conditioning load of the building and even adjacent buildings, leading to additional electricity consumption and carbon emissions. On the other hand, in some areas where industry and agriculture are combined, waste heat generated during factory production is piped to nearby greenhouses for winter heating, thus directly replacing the natural gas or coal that would otherwise be burned in the greenhouses.

[0067] In one embodiment, to address the above-mentioned problems, this embodiment further proposes that the method also includes: apportioning indirect responsibility for associated emission sources, the steps of which include:

[0068] Obtain the energy system topology information integrated in the CIM 3D model;

[0069] Based on the energy system topology information integrated in the CIM 3D model, it is determined whether there is a direct energy exchange relationship between the target traceability unit and any associated emission source;

[0070] If the energy exchange relationship exists, then obtain the energy consumption data transferred via the energy exchange relationship from the structured carbon emission dataset;

[0071] Based on the energy consumption data and the corresponding emission factors, the resulting indirect transfer emissions are calculated;

[0072] The indirect transfer emissions are deducted from the energy outflow party's contribution and included in the energy receiving party's contribution.

[0073] As mentioned above, the first step involves accessing the engineering design data contained in the CIM model, which goes beyond its geometric appearance. A complete CIM model not only includes the building's external shape but may also integrate its internal HVAC system piping diagrams, regional energy network connections, or the routing information of the factory's internal steam and cooling water networks. This information is stored in the CIM in the form of a topological network, clearly recording which equipment, buildings, and pipes are physically connected, and along which energy can flow. This provides a clear energy flow route map. This map allows the system to understand that the emissions of a unit may not only originate from its own consumption but may also be physically coupled with the energy consumption of another unit through a real pipe or cable. The second step is to determine whether there is a direct energy exchange relationship between the target unit and the associated source. Based on the energy topology map obtained in the previous step, a specific correlation screening is performed. Using the target traceable unit and a certain associated emission source as two nodes, the path connecting these two nodes is searched in the energy system topology network. For example, determining whether the target building's air conditioning system is directly connected to the cooling water return pipes of an adjacent data center; or determining whether steam generated by a factory's waste heat boiler has a physical pipe leading to a neighboring greenhouse. If such a direct physical connection exists, it is determined that a direct energy exchange relationship exists. This achieves accurate identification and confirmation of indirect impact relationships. It transforms the vague "potential impact" in the association list into "direct energy exchange exists" based on engineering facts, locking in a clear object for subsequent quantitative calculations. The third step, after confirming the physical exchange relationship, is to obtain quantitative evidence of the exchange. The principle is to correlate metering data with the exchange path. In actual energy systems, key energy exchange nodes are usually equipped with metering instruments. Based on the type and path of the energy exchange relationship, the corresponding metering data is retrieved and extracted from structured carbon emission datasets or dedicated energy management systems. For example, reading the heat value transported to the greenhouse through pipes from the heat meter, or reading the electricity value of the data center supplying power to the adjacent building through a dedicated line from the electricity meter. These data objectively record the magnitude and direction of energy flow, avoiding subjective estimation. Step 4: Calculate indirect transfer emissions. After obtaining the energy exchange amount, carbon responsibility accounting begins. The physical energy flow is converted into a carbon emission flow. An appropriate carbon emission factor is matched based on the type of energy exchanged, such as heat, electricity, or steam. This factor may consider the emission intensity of the energy source, such as the fuel emission factor of a boiler providing waste heat, or the real-time emission factor of the power grid transmitting electricity. Subsequently, a multiplicative calculation is performed, multiplying the exchanged energy consumption data by its corresponding carbon emission factor. The result is the amount of carbon emissions carried by this energy exchange that can be transferred from one entity to another, i.e., indirect transfer emissions. This achieves the calculability of carbon responsibility along the energy flow, providing a precise numerical basis for redistributing carbon responsibility between two entities.The fifth step involves revising the original contribution statistics based on the calculated indirect transfer emissions. The principle is to perform a carbon responsibility redistribution based on physical flow. The direction of energy flow is clearly defined. For energy outflowers, since the energy they output has been used by other units, the carbon emission responsibility corresponding to this energy should be deducted from their total contribution. For energy receivers, since they use carbon-containing energy from other units, this carbon emission responsibility should be included in their total contribution. For example, if a data center discharges waste heat to an adjacent building through its cooling system, causing a reduction in the adjacent building's air conditioning load, the calculated emissions corresponding to this waste heat will be deducted from the data center's contribution and added to the adjacent building's contribution, because the adjacent building saves the cooling power it would otherwise consume. This achieves a true match between carbon responsibility and physical energy consumption. It makes the tracing results more equitable and scientific, reflecting who actually consumes energy and bears the emission responsibility in complex coupled systems, and who provides energy-saving benefits, thereby guiding truly effective collaborative emission reduction strategies.

[0074] This embodiment achieves dynamic tracing and equitable redistribution of carbon responsibility along the physical energy flow. It enables the party providing low-carbon energy output in a symbiotic system to obtain the correct carbon reduction credits, while the party receiving and using this energy bears the corresponding carbon consumption responsibility. This not only solves the attribution distortion problem, making carbon emission accounting more scientific and equitable, but more importantly, by optimizing the system's energy flow, such as maximizing waste heat utilization and optimizing heat dissipation paths, it achieves cross-unit, coordinated carbon reduction, providing a precise data foundation and value measurement tools.

[0075] In one embodiment, the step of generating an emission reduction strategy for each spatial unit based on the carbon emission source tracing and carbon emission intensity data includes:

[0076] Analyze the types of dominant carbon emission sources, and based on the types of dominant carbon emission sources, query and match multiple candidate emission reduction measures for the types of dominant emission sources from a predefined emission reduction measure rule base;

[0077] Obtain the spatial characteristic parameters of the target traceability unit, as well as the contribution amount and contribution ratio of the dominant carbon emission source;

[0078] Based on the spatial characteristic parameters of the target traceability unit and the contribution amount and proportion of the dominant carbon emission sources, the baseline implementation parameters of the candidate emission reduction measures are modified to generate a parameterized preliminary strategy.

[0079] The parameterized preliminary strategies are simulated, and based on the simulation results, the expected carbon emission intensity changes and cost impact data for each preliminary strategy are determined.

[0080] According to the preset screening rules, one of the simulated strategies is selected as the emission reduction strategy for the target tracing unit.

[0081] As described above, the first step, after receiving the identified dominant carbon emission sources, is to initiate a strategy matching engine. This involves a knowledge base retrieval based on type tags. A pre-built structured rule base for emission reduction measures is provided, where each measure is explicitly associated with one or more emission source type tags it can address, such as "coal-fired boilers," "building lighting," and "data center cooling." The type tags of the dominant emission sources are read and used as query keys to quickly retrieve all candidate emission reduction measures matching the applicable tags from the rule base. For example, if the dominant source is identified as "electricity consumption for office building lighting," multiple candidate measures such as "replacing LED lamps," "installing intelligent lighting control systems," and "optimizing natural lighting design" might be matched. The effect of this step is to transform the open-ended strategy formulation problem into a targeted, structured selection problem. The second step, after obtaining a batch of general candidate measures, requires injecting specific scenario-specific elements into them. This involves gathering and utilizing all detailed data related to the target spatial unit. This process not only acquires the spatial characteristic parameters of the unit defined in the CIM model, such as building area, window-to-wall ratio, and roof structure, but also obtains calculated detailed contribution and contribution ratio data for the dominant emission source. For example, for the same measure of "replacing LED lights," the urgency and potential value are drastically different when applied to a large exhibition hall lighting system with a contribution ratio as high as 40%, compared to when applied to a warehouse lighting system with a contribution ratio of only 5%. This allows strategy generation to take into account both "common technologies" and "individual conditions." The third step: revising baseline parameters to generate a personalized preliminary strategy. This step is crucial for the strategy to move from general to customized. It involves executing a parameterized strategy optimization algorithm. The pre-stored candidate measures include a set of baseline implementation parameters, such as unit energy-saving renovation cost and expected energy savings under standard operating conditions. However, these baseline values ​​are not applicable to all scenarios. The baseline parameters are dynamically revised based on the spatial characteristic parameters of the target unit. For example, for the measure of "adding external wall insulation," the required material quantity, engineering cost, and expected energy-saving effect are accurately recalculated based on the specific area of ​​the building's external wall, the type of existing insulation material, and local climate parameters provided by the CIM model. Simultaneously, the system intelligently adjusts the recommended priority or implementation scale of the measure based on the contribution ratio of the dominant source. Through this calculation, each candidate measure transforms from an abstract suggestion into a parameterized preliminary strategy with specific engineering quantities, expected carbon emission reductions, investment costs, and payback periods. After generating multiple parameterized strategies in the fourth step, a selection is not made directly; instead, the system enters a simulation and prediction environment. The specific parameters of each preliminary strategy are input into a simulation engine. This engine combines historical operating data, equipment performance curves, and external conditions such as electricity prices and weather forecasts to simulate the system's operational status within the space unit over a future period after the strategy's implementation.Through simulation, two key expected outcome data for each strategy are output: first, the specific change or percentage reduction in carbon emission intensity of the unit after implementation; and second, the cost impact of the entire implementation process, including initial investment, changes in operating expenses, and potential maintenance costs. This transforms the uncertain future into comparable numerical indicators. The fifth step involves automated decision-making based on the simulation results. Pre-defined multi-objective trade-offs and optimization rules are applied. These selection rules may be set to pursue the lowest cost while meeting the minimum carbon emission reduction target, or to pursue the maximum emission reduction under a given budget constraint, or to pursue the shortest investment payback period. The carbon emission intensity change data and cost impact data output from each strategy simulation are substituted into these pre-defined rules for comprehensive evaluation and ranking. For example, the rules may set an internal rate of return threshold to automatically eliminate strategies with excessively low returns; or among strategies with similar emission reductions, automatically select the one with the lower implementation difficulty. Finally, one or more strategies with the best comprehensive score are output as the final emission reduction strategies recommended to the target tracking unit.

[0082] In one embodiment, after the step of selecting an emission reduction strategy from the simulated strategies according to a preset screening rule as the target tracing unit, the method further includes:

[0083] Obtain the emission reduction strategies selected for multiple target traceability units within the same management area;

[0084] Obtain the shared resource data and constraint rules integrated in the CIM 3D model;

[0085] Based on the shared resource data and constraint rules, it is detected whether there are conflicts between multiple emission reduction strategies. The conflicts include competitive use of the same physical shared resource, or the offsetting of expected changes in system-level carbon emissions after the execution of multiple strategies.

[0086] If a conflict is detected between multiple emission reduction strategies, the conflicting emission reduction strategies are adjusted according to the preset conflict resolution rules to generate a set of emission reduction strategies that have been collaboratively adjusted; the conflict resolution rules include re-optimization rules based on maximizing the overall emission reduction benefits.

[0087] The coordinated set of emission reduction strategies is then re-input into the strategy evaluation model for system-level simulation verification.

[0088] If the verification passes, the coordinated set of emission reduction strategies will be output as the final strategy.

[0089] As mentioned above, the first step involves aggregating the independent emission reduction strategies of each unit within the region. For example, optimal strategies have already been generated for Unit A (data center), Unit B (office building), and Unit C (warehouse) within the park. At this point, a collaborative analysis process is initiated, which involves regional-level aggregation of strategy data. Using the management area, such as the entire park, as the boundary, the independent emission reduction strategy sets generated for all target traceability units within that area are retrieved and obtained. The originally scattered, unit-specific decision-making schemes are aggregated into a strategy set that can be reviewed at the system level. The second step, to assess the compatibility between strategies, requires understanding the physical world they commonly rely on. This is achieved by accessing system-level infrastructure and rule data from the CIM model that transcends the scope of individual buildings. This includes shared resource data for the park, such as the total roof area of ​​the entire park, the total capacity limit of distributed photovoltaic grid connection, the peak load constraints of the regional power grid, and the total supply capacity of centralized cooling and heating sources. It also includes system-level constraint rules, such as the overall carbon emission control target for the park, time-of-use electricity pricing policies, and the real-time carbon intensity change curve of the power grid. The effect of this step is to provide the system with an objective benchmark and a competitive arena for measuring strategy conflicts. The third step involves comparing and analyzing the multiple independent strategies obtained in the first step with the system-level resources and rules obtained in the second step. A multi-dimensional conflict detection algorithm is executed to identify two main types of conflicts: First, resource competition conflicts: For example, Unit A's strategy includes "installing rooftop photovoltaics," and Units B and C also include the same measure. The planned photovoltaic capacity of these three measures is added together and compared with the total available rooftop area of ​​the park recorded in the CIM model and the total grid-connected capacity allowed by the power grid. If the total demand exceeds any limit, a competitive conflict for scarce rooftop resources and grid-connected capacity is determined. Second, system effect offsetting conflicts: For example, one of the strategies of Unit A's data center is "increasing nighttime natural ventilation cooling and shifting some computing load to nighttime when electricity prices are lower." However, the grid carbon emission factor model in the system-level constraint rules shows that the marginal carbon emission factor of the grid in this area may be significantly higher at night than during the day due to reliance on high-carbon baseload power sources. System simulations reveal that while this strategy reduces Unit A's electricity costs, it may lead to an increase in total carbon emissions from the perspective of the entire park due to the consumption of higher-carbon nighttime electricity. This creates a conflict between local cost optimization and the overall carbon reduction target. The hidden strategic contradictions are made explicit. In the fourth step, upon detecting a conflict, a strategy is not simply rejected; instead, a higher-level optimization procedure is initiated. Pre-defined conflict resolution rules, such as the "maximizing overall emission reduction benefits" rule, are applied. With maximizing the overall net reduction in carbon emissions for the entire park as the core objective, and considering total cost constraints and all shared resource limitations, the conflicting strategies are treated as a new combinatorial optimization problem and solved anew.For example, in the case of photovoltaic installation conflicts, installation capacity can be reallocated, prioritizing units with the highest emission reduction efficiency per unit area, or suggesting that units B and C adopt other measures. For carbon conflicts caused by data center peak shifting, the load transfer period can be adjusted, or its charging and discharging strategies can be optimized in conjunction with the park's energy storage system. Ultimately, a new set of globally coordinated emission reduction strategies is generated. The core effect of this step is to achieve strategy re-optimization and coordination, ensuring that individual actions conform to and serve the overall optimal. The fifth step involves a more careful evaluation of the adjusted new strategy set. Integrated simulation verification covering all related units in the entire region is conducted. This coordinated strategy set is input into a more macro-level strategy evaluation model to simulate its comprehensive impact on the operation of the entire park's energy system, total carbon emissions, and economic costs over a certain period in the future. Only when the simulation verification shows that the new strategy set, while satisfying all system constraints, has an overall emission reduction efficiency that is indeed better than or at least not worse than the simple sum of the original independent strategies will it be finally verified and output as the final implementation plan for the region.

[0090] In one feasible embodiment, in current carbon management practices, in addition to traditional energy consumption and carbon emission data generated by production processes, a new type of data has emerged that characterizes carbon reduction or offsetting effects. Examples include carbon dioxide sequestration recorded by factory carbon capture equipment, carbon absorption monitored in forests owned by enterprises, and negative grid energy consumption data generated by building-integrated photovoltaic (BIPV) systems. A common characteristic of this type of data is that its numerical or semantic representation indicates a net contribution to the reduction of atmospheric carbon dioxide concentration, which is physically the opposite of conventional data characterizing increased emissions. However, existing mainstream carbon emission analysis systems were primarily designed to account for positive emission activities. When the aforementioned new data is incorporated into the system as "carbon emission-related data," the entire analysis chain faces systematic distortion due to a lack of ability to identify and classify its specific semantics. In the calculation stage, general models struggle to correctly handle such negative or offsetting inputs, potentially leading to logically inconsistent calculation results. In the tracing stage, the system may incorrectly count carbon removal activities as increased emissions, resulting in a complete reversal of responsibility. In the decision-making process, the system may disregard existing carbon offset assets and still generate unnecessary emission reduction recommendations for units that have already achieved low-carbon or even negative carbon emissions. The essence of this problem is that traditional analytical frameworks assume all input data as positive emission activities, and their built-in data processing, model calculations, and decision-making logic are all built on this single assumption, which cannot adapt to the real-world management needs of diversified carbon flow directions.

[0091] In this embodiment, the proposed solution includes: in the step of acquiring multi-source heterogeneous carbon emission data in real time, preprocessing the multi-source heterogeneous carbon emission data, and obtaining a structured carbon emission dataset, the method further includes:

[0092] For each piece of carbon emission data acquired, the semantic identifiers in the carbon emission data content are parsed;

[0093] Based on the semantic identifier, the direction of carbon flow represented by the carbon emission data is determined, and the direction of carbon flow includes forward emission, reverse removal, or alternative offsetting.

[0094] Based on the determined carbon flow direction, the carbon emission data is classified, labeled, and stored;

[0095] For the carbon emission data that has been removed in reverse, when storing the carbon emission data into the structured carbon emission dataset, a removal activity identifier is attached.

[0096] For the carbon emission data that is offset by alternatives, when storing the carbon emission data into the structured carbon emission dataset, a correlation is established between it and the baseline emission activity data that is being replaced.

[0097] In the process of inputting the spatial characteristic parameters into the preset intensity analysis model and calculating the carbon emission intensity data of each space through the intensity analysis model, if the input data is associated with carbon emission data with a removal activity flag, a special calculation rule is adopted. The special calculation rule includes multiplying the quantified value of the carbon emission data by a negative carbon intensity factor, and the result is used as a negative contribution to participate in the final calculation of the carbon emission intensity data.

[0098] As mentioned above, the first step, in the data preprocessing stage, involves deep content parsing of each piece of imported raw data. The technical principle is to scan and analyze specific semantic identifier fields in the data records. These identifiers may exist directly in the data labels; for example, data streams from carbon capture devices may have explicit fields such as "carbon removal" or "carbon sequestration." They may also be implied in the data context; for example, forestry carbon sequestration monitoring data is usually associated with descriptive terms such as "carbon sequestration" and "absorption." Based on a pre-defined semantic rule base, the parsed identifiers are matched and judged to determine the fundamental direction represented by the data. It mainly distinguishes three categories: positive emissions representing an increase in atmospheric carbon concentration, reverse removal representing the removal of carbon dioxide from the atmosphere, and alternative offsetting representing the replacement of a high-carbon activity. At the initial stage of data entering the system, key carbon flow semantic attributes are assigned, achieving a deep understanding of the data's essence, rather than simply receiving numerical values. The second step, based on the determined carbon flow direction, involves classifying the data. The technical principle is to perform structured data labeling and relationship construction. For data identified as reverse removal, a specific removal activity identifier is forcibly attached when it is stored in the main dataset. This identifier acts like a special stamp, ensuring that the data can be immediately identified as having the essential attribute of "carbon removal" in any subsequent calls and calculations. For data identified as substitution offsets, the processing is more refined. A logical relationship needs to be found and established between it and the baseline emission activity data it replaces. For example, offset data from a green electricity direct supply project must be explicitly associated with the emission data generated by the average power supply from the grid it replaces. This step creates a clear association at the database level. By semantically tagging and linking the data, the legal status and calculation basis of special carbon flow data are established at the data structure level. The third step, in the core carbon emission intensity calculation stage, checks whether the data involved in the calculation is associated with a removal activity identifier. The principle is to design a branch calculation logic. When an input data point with a removal identifier is found, a dedicated calculation rule is automatically triggered. The core of this rule is to multiply the quantified value of this data, such as the number of tons of carbon dioxide captured and stored, by a specific negative carbon intensity factor. This negative factor physically represents the "emission equivalent offset by a unit of removal." The result is a negative value, representing the activity's contribution to the reduction of net carbon emissions in a region or facility. When finally summarizing the carbon intensity data for a given space, this negative value is algebraically added to the positive emission contributions from other sources. For example, if a factory emits 10,000 tons, but its carbon capture equipment removes 1,500 tons, then its net emissions are calculated as 8,500 tons. This accounting logic truly achieves a fair reflection of carbon removal activities, ensuring that the final carbon intensity data scientifically characterizes the "net emissions" status and avoids incorrectly calculating removals as zero or positive contributions.

[0099] In this embodiment, by endowing the data with the semantic ability to identify "carbon flow direction" and establishing corresponding differentiated calculation rules, carbon removal and carbon offsetting activities are incorporated into automated carbon emission accounting. This addresses the shortcomings of traditional frameworks that assume all data is emissions. Specifically, this solution performs semantic parsing and classification at the data entry point, establishing a correct foundation for subsequent processing; in the core calculation stage, dedicated negative calculation logic is designed for removal data to ensure its true contribution is fairly measured. This allows the final analysis results to accurately reflect net emissions.

[0100] Reference Figure 3 This application also provides a carbon emission information data analysis system based on CIM, including:

[0101] The acquisition module 1 is used to acquire multi-source heterogeneous carbon emission data in real time, and preprocess the multi-source heterogeneous carbon emission data to obtain a structured carbon emission dataset.

[0102] The association module 2 is used to associate each data point in the carbon emission dataset with a spatial unit in the CIM 3D model based on the processing results, and to determine the mapping relationship between carbon emission data and geographic spatial location.

[0103] Extraction module 3 is used to extract the attribute parameters of the spatial unit from the CIM 3D model based on the association binding result, and use the attribute parameters as spatial feature parameters corresponding to carbon emissions;

[0104] Calculation module 4 is used to input the spatial characteristic parameters into a preset intensity analysis model and calculate the carbon emission intensity data of each space through the intensity analysis model;

[0105] Source tracing module 5 is used to trace the source of carbon emissions for the spatial units corresponding to the carbon emission intensity data based on the mapping relationship;

[0106] The strategy generation module 6 is used to generate an emission reduction strategy for each spatial unit based on the carbon emission source tracing and carbon emission intensity data.

[0107] The sending module 7 is used to send the emission reduction strategy and carbon emission intensity data to the corresponding target information platform through the difference interface.

[0108] As described above, it is understood that each component of the CIM-based carbon emission information data analysis system proposed in this application can realize the function of any of the CIM-based carbon emission information data analysis methods described above, and the specific structure will not be repeated.

[0109] Reference Figure 4This application also provides a computer device, which may be a server, and its internal structure may be as follows: Figure 4 As shown, the computer device includes a processor, memory, network interface, and database connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and database. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The database stores monitoring data and other data. The network interface is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements a CIM-based carbon emission information data analysis method.

[0110] The processor described above executes the CIM-based carbon emission information data analysis method, including: acquiring multi-source heterogeneous carbon emission data in real time; preprocessing the multi-source heterogeneous carbon emission data to obtain a structured carbon emission dataset; based on the processing results, associating each data point in the carbon emission dataset with a spatial unit in the CIM 3D model to determine the mapping relationship between carbon emission data and geographic spatial location; based on the association and binding results, extracting attribute parameters of the spatial unit from the CIM 3D model and using the attribute parameters as spatial feature parameters corresponding to carbon emissions; inputting the spatial feature parameters into a preset intensity analysis model and calculating the carbon emission intensity data of each space through the intensity analysis model; based on the mapping relationship, tracing the carbon emission sources of the spatial units corresponding to the carbon emission intensity data; generating an emission reduction strategy for each spatial unit based on the carbon emission source tracing and carbon emission intensity data; and sending the emission reduction strategy and carbon emission intensity data to the corresponding target information platform through a difference interface.

[0111] One embodiment of this application also provides a computer-readable storage medium storing a computer program thereon. When executed by a processor, the computer program implements a carbon emission information data analysis method based on CIM, including the following steps: acquiring multi-source heterogeneous carbon emission data in real time; preprocessing the multi-source heterogeneous carbon emission data to obtain a structured carbon emission dataset; based on the processing results, associating each data point in the carbon emission dataset with a spatial unit in a CIM 3D model to determine the mapping relationship between carbon emission data and geographic spatial location; based on the association binding results, extracting attribute parameters of the spatial unit from the CIM 3D model and using the attribute parameters as spatial feature parameters corresponding to carbon emissions; inputting the spatial feature parameters into a preset intensity analysis model and calculating the carbon emission intensity data of each space through the intensity analysis model; based on the mapping relationship, tracing the carbon emission sources of the spatial units corresponding to the carbon emission intensity data; generating an emission reduction strategy for each spatial unit based on the carbon emission source tracing and carbon emission intensity data; and sending the emission reduction strategy and carbon emission intensity data to the corresponding target information platform through a difference interface.

[0112] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in this application and in the embodiments can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual-speed SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0113] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, apparatus, article, or method that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, apparatus, article, or method. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, apparatus, article, or method that includes that element.

[0114] The above description is only a preferred embodiment of this application and does not limit the patent scope of this application. Any equivalent structural or procedural changes made based on the content of this application's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of this application.

Claims

1. A carbon emission information data analysis method based on CIM, characterized in that, The method includes: Real-time acquisition of multi-source heterogeneous carbon emission data, preprocessing of the multi-source heterogeneous carbon emission data to obtain a structured carbon emission dataset; Based on the processing results, each data point in the carbon emission dataset is associated with a spatial unit in the CIM 3D model to determine the mapping relationship between carbon emission data and geographic spatial location. Based on the association and binding results, the attribute parameters of the spatial unit are extracted from the CIM 3D model, and the attribute parameters are used as spatial characteristic parameters corresponding to carbon emissions. The spatial characteristic parameters are input into a preset intensity analysis model, and the carbon emission intensity data of each space are calculated through the intensity analysis model. Based on the mapping relationship, carbon emission sources are traced for the spatial units corresponding to the carbon emission intensity data; Based on the carbon emission source tracing and carbon emission intensity data, an emission reduction strategy is generated for each spatial unit; The emission reduction strategy and carbon emission intensity data are sent to the corresponding target information platform through a difference interface; The step of tracing the carbon emission sources of spatial units corresponding to the carbon emission intensity data based on the mapping relationship specifically includes: Based on the carbon emission intensity data, spatial units whose carbon emission intensity exceeds a preset intensity threshold are identified as target traceability units; Based on the mapping relationship, all carbon emission data entries bound to the target traceability unit are analyzed to form a direct emission source list; In the spatial topology and business logic relationships stored in the CIM 3D model, analyze other spatial units that are associated with the target traceability unit; Obtain carbon emission intensity data of the other spatial units, and identify units whose intensity exceeds the correlation threshold as associated emission sources to form an associated emission source list; For each source in the direct emission source inventory and the associated emission source inventory, the type of each source is analyzed, and quantitative data of emission activities corresponding to that source are extracted from the structured carbon emission dataset; Match the corresponding dynamic emission factor based on the type of each source and the spatial characteristic parameters of the target traceability unit; Based on the quantitative data of emission activities from the source and the matching dynamic emission factors, the contribution and contribution ratio to the carbon emission intensity of the target traceability unit are calculated. Based on the contribution amount and contribution ratio, the dominant carbon emission source of the carbon emission intensity of the target traceability unit is determined; The method also includes: apportioning indirect responsibility for related emission sources, specifically including: Obtain the energy system topology information integrated in the CIM 3D model; Based on the energy system topology information integrated in the CIM 3D model, it is determined whether there is a direct energy exchange relationship between the target traceability unit and any associated emission source; If the energy exchange relationship exists, then obtain the energy consumption data transferred via the energy exchange relationship from the structured carbon emission dataset; Based on the energy consumption data and the corresponding emission factors, the resulting indirect transfer emissions are calculated; The indirect transfer emissions are deducted from the energy outflow party's contribution and included in the energy receiving party's contribution.

2. The carbon emission information data analysis method based on CIM according to claim 1, characterized in that, Based on the processing results, each data point in the carbon emission dataset is associated with a spatial unit in the CIM 3D model to determine the mapping relationship between carbon emission data and geographic spatial location. Specifically, this includes: Assign a unique spatial code to each spatial unit with independent geographical significance in the CIM 3D model; For each data point in the structured carbon emissions dataset, add a spatial code to identify the spatial location of its source; The spatial code of the carbon emission data is matched with the unique spatial code of the spatial unit in the CIM 3D model, and it is determined whether the match is successful. When a match is successful, the carbon emission data is bound to the corresponding spatial unit, and the binding relationship is recorded.

3. The carbon emission information data analysis method based on CIM according to claim 2, characterized in that, After the step of matching the spatial code of the carbon emission data with the unique spatial code of the spatial unit in the CIM 3D model and determining whether the match is successful, the method further includes: If a match fails, analyze the spatial location information carried by the carbon emission data entries that failed to match. Based on the analysis results, the coordinates of the spatial location information are extracted, and the nearest spatial unit is searched within a preset buffer radius according to the coordinates for matching, and the matching success is analyzed. If a match fails, the information of the failed carbon emission data entry and the information of several candidate spatial units in the CIM 3D model will be displayed side by side on the interactive interface. Real-time reception of user selection instructions for a target spatial unit specified from the plurality of candidate spatial units; If no valid selection instruction is received, the carbon emission data entry will be marked as spatially correlated anomaly. The marked carbon emission data will not be included in the calculation of carbon emission intensity and source tracing of the mapping relationship.

4. The carbon emission information data analysis method based on CIM according to claim 1, characterized in that, The process of generating emission reduction strategies for each spatial unit based on the carbon emission source tracing and carbon emission intensity data specifically includes: Analyze the types of dominant carbon emission sources, and based on the types of dominant carbon emission sources, query and match multiple candidate emission reduction measures for the types of dominant carbon emission sources from a predefined emission reduction measure rule base; Obtain the spatial characteristic parameters of the target traceability unit, as well as the contribution amount and contribution ratio of the dominant carbon emission source; Based on the spatial characteristic parameters of the target traceability unit and the contribution amount and proportion of the dominant carbon emission sources, the baseline implementation parameters of the candidate emission reduction measures are modified to generate a parameterized preliminary strategy. The parameterized preliminary strategies are simulated, and based on the simulation results, the expected carbon emission intensity changes and cost impact data for each preliminary strategy are determined. According to the preset screening rules, one of the simulated strategies is selected as the emission reduction strategy for the target tracing unit.

5. The carbon emission information data analysis method based on CIM according to claim 4, characterized in that, After the step of selecting an emission reduction strategy from the simulated strategies according to preset screening rules as the target tracing unit, the method further includes: Obtain the emission reduction strategies selected for multiple target traceability units within the same management area; Obtain the shared resource data and constraint rules integrated in the CIM 3D model; Based on the shared resource data and constraint rules, it is detected whether there are conflicts between multiple emission reduction strategies. The conflicts include competitive use of the same physical shared resource, or the offsetting of expected changes in system-level carbon emissions after the execution of multiple strategies. If a conflict is detected between multiple emission reduction strategies, the conflicting emission reduction strategies are adjusted according to the preset conflict resolution rules to generate a set of emission reduction strategies that have been coordinated and adjusted. The conflict resolution rules include re-optimization rules based on maximizing the overall emission reduction benefits. The coordinated set of emission reduction strategies is input into a preset strategy evaluation model for simulation verification. If the verification passes, the coordinated set of emission reduction strategies will be output as the final strategy.

6. A carbon emission information data analysis system based on CIM, used in the method described in any one of claims 1-5, characterized in that, include: The acquisition module is used to acquire multi-source heterogeneous carbon emission data in real time, and preprocess the multi-source heterogeneous carbon emission data to obtain a structured carbon emission dataset. The association module is used to associate each data point in the carbon emission dataset with a spatial unit in the CIM 3D model based on the processing results, and to determine the mapping relationship between carbon emission data and geographic spatial location. The extraction module is used to extract the attribute parameters of the spatial unit from the CIM 3D model based on the association binding result, and use the attribute parameters as spatial feature parameters corresponding to carbon emissions; The calculation module is used to input the spatial characteristic parameters into a preset intensity analysis model and calculate the carbon emission intensity data of each space through the intensity analysis model. The source tracing module is used to trace the source of carbon emissions for the spatial units corresponding to the carbon emission intensity data based on the mapping relationship. The strategy generation module is used to generate an emission reduction strategy for each spatial unit based on the carbon emission source tracing and carbon emission intensity data. The sending module is used to send the emission reduction strategy and carbon emission intensity data to the corresponding target information platform through the difference interface.

7. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 5.

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