Real-time simulation and online review system for building energy-saving design based on knowledge graph

Abstract

The application discloses a real-time simulation and online review system for building energy-saving design based on a knowledge graph, comprising a data matching module, a knowledge graph construction module, a design simulation module, a simulation calculation module and an online review module; the data matching module can access building information building static attribute data and external data sources, and align and construct a cumulative distance matrix for the building static attribute data and the external data sources; the knowledge graph construction module performs semantic modeling and fusion on the building static attribute data and the external data sources based on a predefined domain ontology to form a knowledge graph; and the design simulation module can extract HVAC system equipment entities and their connection relationships from the knowledge graph. The application fundamentally solves the problem that HVAC system equipment operation is disconnected from dynamic carbon signals of a power grid in traditional building energy consumption simulation by constructing a knowledge graph that fuses static attributes and dynamic data.

Description

Technical Field

[0001] This invention relates to the field of building energy-saving design technology, specifically to a real-time simulation and online review system for building energy-saving design based on knowledge graphs. Background Technology

[0002] Building energy efficiency design is a design concept that comprehensively considers the relationship between buildings and the environment. It aims to reduce energy consumption during building use by optimizing building design, material selection, and energy utilization methods. It covers all aspects from planning and layout to individual building design, such as rationally determining building orientation, form factor, and spatial layout to make full use of natural lighting and ventilation and reduce reliance on artificial lighting and air conditioning systems.

[0003] In building energy-efficient design, HVAC (Heating, Ventilation, and Air Conditioning) systems play a crucial role. HVAC system equipment not only automatically adjusts indoor temperature, humidity, and air quality based on indoor and outdoor environmental parameters, but also achieves on-demand heating and cooling through intelligent control strategies, avoiding energy waste. For example, utilizing renewable energy technologies such as ground source heat pumps and solar collectors, combined with advanced technologies such as variable frequency speed control and heat recovery, can significantly improve the energy efficiency ratio of HVAC system equipment, enabling buildings to provide a comfortable indoor environment while also achieving energy conservation and emission reduction goals.

[0004] However, most current real-time simulation systems for building energy conservation designs rely on static parameters or typical meteorological year data for energy consumption simulation. The operating strategies of HVAC system equipment (especially complex systems such as combined cold and heat sources and variable flow transmission and distribution) are disconnected from the real-time changing carbon emission intensity (carbon factor) of the power grid. This leads to the inability to accurately assess the real carbon reduction benefits of HVAC system equipment, resulting in a large deviation between the simulation results and the actual carbon footprint of the building. Furthermore, it is impossible to provide rapid feedback on energy-saving performance and automatic review of mandatory design standards (such as the "General Code for Building Energy Conservation and Renewable Energy Utilization" GB55015) during the design phase. Summary of the Invention

[0005] To address these issues, this invention provides a real-time simulation and online review system for building energy-saving design based on knowledge graphs.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] A real-time simulation and online review system for building energy-saving design based on knowledge graphs includes a data matching module, a knowledge graph construction module, a design simulation module, a simulation calculation module, and an online review module.

[0008] The data matching module can access the static attribute data of the building information and external data sources, and align the static attribute data of the building and external data sources and construct a cumulative distance matrix.

[0009] The knowledge graph construction module performs semantic modeling and fusion of building static attribute data and external data sources based on a predefined domain ontology to form a knowledge graph;

[0010] The design simulation module can extract HVAC system equipment entities and their connection relationships from the knowledge graph, construct a complete first directed graph, and generate a second directed graph by combining the key node set and the equivalent edge set.

[0011] The simulation calculation module obtains dynamic power grid carbon factors in real time through knowledge graph, determines the real-time operating power of the simulation design by combining the design parameter values ​​of HVAC system equipment, calculates the total carbon emissions and carbon emission share, and generates direct carbon emission responsibility curves at the equipment level.

[0012] carbon emission share The calculation formula is as follows:

[0013]

[0014] in, To assign weights, Total carbon emissions;

[0015] The online review module can calculate the volatility M of the carbon emission sequence based on the total carbon emissions, combine it with the real-time energy-carbon synergy efficiency B, calculate the dynamic energy saving value F that characterizes the overall performance, and automatically determine the design compliance and output the review conclusion.

[0016] Furthermore, the specific content of the knowledge graph construction module is as follows:

[0017] 1) Process building static attribute data to obtain logical network information;

[0018] 2) Process the marginal carbon emission factor sequence of the power grid to obtain dynamic time data;

[0019] 3) Based on the logical network information framework, dynamic time data is integrated into the framework, so that the dynamic time data is bound to the building's overall energy consumption entrance entity and related entities in the logical network to obtain a knowledge graph.

[0020] Furthermore, the building static attribute data includes HVAC system equipment parameters, pipeline parameters, and equipment nameplate parameters, and the external data source includes the power grid marginal carbon emission factor sequence.

[0021] Furthermore, the design simulation module includes an identification submodule and an aggregation submodule;

[0022] The identification submodule can mark all nodes in the directed graph according to preset rules and determine them as the set of key nodes. ;

[0023] The aggregation submodule is capable of processing key node sets. The paths in the graph are aggregated. If there is a connecting path between two key nodes consisting of equivalent nodes, then all equivalent edges on the path are determined as an equivalent edge set, and a second directed graph is constructed.

[0024] Furthermore, the specific contents of the simulation calculation module are as follows:

[0025] 1) Locate the HVAC system equipment parameters corresponding to the simulation time in the knowledge graph, and obtain the real-time grid carbon emission factor value acting on the building's overall energy inlet at the current time. Meanwhile, by using the dynamic attribute nodes linked by the knowledge graph, the current heating and cooling load values ​​of each area of ​​the building can be obtained;

[0026] 2) Using the load demand of HVAC system equipment parameters as known boundary conditions, and simultaneously calling the HVAC system equipment design parameter values ​​stored in the knowledge graph, the actual operating power of the HVAC system equipment is obtained through the built-in equipment performance library;

[0027] 3) Based on the carbon flow tracing algorithm, the real-time operating power of all devices connected to the HVAC system during the simulation time interval is summed to obtain the total power consumption. And calculate the building's total carbon emissions. ;

[0028] The calculation formula is as follows:

[0029]

[0030] in, To be in the time interval Real-time carbon emission factor of internal power grid This is a simulated time interval;

[0031] 4) After obtaining the total carbon emissions of the building, according to the second directed graph The corresponding equivalent edges are used to determine the carbon emission share that each end node should share. ;

[0032] 5) Starting from each end node, backtrack along the equivalent edge path to the HVAC system equipment point, connect the carbon emission shares related to the HVAC system equipment point at that simulation moment, and obtain the direct carbon emission responsibility curve.

[0033] Furthermore, the online review module includes a fluctuation amplitude assessment submodule, a coupling efficiency assessment submodule, and a judgment submodule;

[0034] The fluctuation amplitude assessment submodule can receive the total building carbon emission sequence output by the simulation calculation module, which is synchronized with the main time axis. Total power consumption And calculate the magnitude of carbon emission fluctuations over time for simulated HVAC system equipment. The calculation formula is as follows:

[0035]

[0036] in, The number of time intervals, Total carbon emissions The average value at each time point.

[0037] Furthermore, the coupling efficiency evaluation submodule is capable of calculating efficiency indices. This is used to evaluate the overall energy efficiency and carbon efficiency synergy of HVAC system equipment under real-time fluctuations in the grid carbon emission factor. The calculation formula is as follows:

[0038]

[0039] in, The real-time heating and cooling loads at time t are obtained and summarized through the knowledge graph. It represents the sum of the real-time operating electrical power of all HVAC system devices in the second directed graph at the same time.

[0040] Furthermore, the judgment submodule can calculate the magnitude of the time-varying carbon emissions simulated by HVAC system equipment based on the objective function. and efficiency indicators Dynamic energy saving value during design Then the dynamic energy saving value The results are compared with the preset threshold range.

[0041] Furthermore, the dynamic energy saving value The calculation formula is as follows:

[0042]

[0043] in, and These are weighting coefficients pre-set based on policy guidance and design objectives.

[0044] This invention offers the following advantages: By constructing a knowledge graph that integrates static attributes and dynamic data, it fundamentally solves the problem of disconnect between HVAC system equipment operation and dynamic carbon signals from the power grid in traditional building energy consumption simulation. This system achieves "real-time carbon-energy coupled simulation," accurately simulating minute-level carbon emission trajectories under real power grid carbon intensity fluctuations during the design phase, and generating direct carbon emission responsibility curves at the equipment level. This transforms carbon footprint assessment from static averages to dynamic and precise tracking, significantly reducing the deviation between simulation results and actual operational carbon footprint.

[0045] Simultaneously, by combining the dynamic performance indicator F in the online review module, it can automatically determine whether the design scheme meets dynamic carbon constraints under simulated operating conditions. This overcomes the limitation of traditional specification reviews, which can only verify static equipment parameters, and enables the real-time identification of carbon emission exceedance risks during the design phase, ensuring the effectiveness of online reviews.

[0046] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. Attached Figure Description

[0047] To more intuitively illustrate the prior art and this application, exemplary drawings are provided below. It should be understood that the specific shapes and structures shown in the drawings should not generally be regarded as limiting conditions for implementing this application; for example, based on the technical concept disclosed in this application and the exemplary drawings, those skilled in the art are able to easily make conventional adjustments or further optimizations to the addition / reduction / classification, specific shapes, positional relationships, connection methods, size ratios, etc. of certain units (components).

[0048] Figure 1 This is a module diagram of the real-time simulation and online review system for building energy-saving design based on knowledge graphs, as described in this invention.

[0049] Figure 2 This is an implementation architecture diagram of the online review module in the real-time simulation and online review system for building energy-saving design based on knowledge graphs of the present invention. Detailed Implementation

[0050] The following specific embodiments illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. It should be understood that these embodiments are merely for further explanation of the present invention and should not be construed as limiting the scope of protection of the present invention. Technical engineers in the field can make some non-essential improvements and adjustments to the present invention based on the above-described content. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0051] Please see Figure 1 The real-time simulation and online review system for building energy-saving design based on knowledge graphs includes: a data matching module, a knowledge graph construction module, a design simulation module, a simulation calculation module, and an online review module.

[0052] The data matching module can access the static attribute data of building information, including HVAC system equipment parameters (heating, ventilation and air conditioning system equipment), pipeline parameters, and equipment nameplate parameters. It can also continuously access external data sources through a preset application programming interface. External data sources include minute-level (e.g., 15-minute interval) marginal carbon emission factor sequences of the power grid published by power grid dispatching agencies or environmental exchanges.

[0053] The data matching module also incorporates an adaptive dynamic time warping algorithm to align building static attribute data with fine-grained time intervals of carbon factors, ensuring that the two are comparable in the time dimension.

[0054] Simultaneously, it can calculate the minimum cumulative distance path between two time series, dynamically establish the optimal nonlinear mapping relationship, and facilitate the reflection of the correspondence between the two time series at different time points through the nonlinear mapping relationship. This effectively compensates for time phase deviations caused by various factors, achieving precise synchronization of building information model file data and carbon factor data in the time dimension. Specific details are as follows:

[0055] Construct the cumulative distance matrix , where matrix elements Represents the building simulation energy consumption sequence The former Individual points and power grid carbon factor sequence The former The minimum cumulative alignment distance between points, starting from the endpoint By backtracking to find the minimum cumulative path, we can obtain the optimal nonlinear mapping relationship that minimizes the overall distance between the two sequences. The calculation formula is as follows:

[0056]

[0057] in, It is a local distance metric such as Euclidean distance.

[0058] The aforementioned Adaptive Dynamic Time Warping (ADTW) algorithm refers to a class of algorithms that can flexibly handle the alignment of time series of different lengths and time rhythms. It is used to align static building attribute data (such as building structural parameters, fixed power of equipment, etc., which do not change frequently over time but need to be correlated with time in carbon emission analysis) with fine-grained time interval data of carbon factors (such as real-time grid carbon emission factors recorded by hour, minute, or even second, etc., which change dynamically over time), ensuring that the two are comparable in the time dimension, so as to accurately analyze the carbon emissions generated by buildings at different times based on their static attributes; in addition, it also includes dynamic time alignment algorithms based on interpolation methods, etc.

[0059] Once the mapping relationship is established, the data matching module can apply a unified timestamp system to all input data, generating a master timeline. This timeline becomes the absolute time series reference for all subsequent simulation calculations and analyses.

[0060] The knowledge graph construction module performs semantic modeling and fusion of static building attribute data and external data sources based on a predefined domain ontology to form a knowledge graph. The knowledge graph defines the core concepts, attributes, and relationships in the field of building energy conservation, including categories such as building components, equipment systems, energy flow, time dimension, and carbon emissions, and specifies strict hierarchical and constraint relationships.

[0061] The specific contents of the knowledge graph construction module are as follows:

[0062] 1) Process building static attribute data to obtain logical network information

[0063] For the connection relationships between HVAC system devices, corresponding entity nodes are created and their IDs and static attributes are labeled. Then, by parsing the logical associations in the design file, relational edges such as upstream or downstream connections are generated, thereby constructing logical network information that accurately describes the system topology, which facilitates the conversion of ontology specifications.

[0064] 2) Process the marginal carbon emission factor sequence of the power grid to obtain dynamic time data.

[0065] Based on a nonlinear time mapping relationship, each grid carbon emission factor sequence is paired with time on the main time axis to determine effective time points. These effective time points are recorded with their timestamps through data attributes and associated with the corresponding grid carbon emission factor values ​​to obtain dynamic time data. This facilitates the binding of time-fluctuating carbon intensity with the overall energy consumption of a building at the knowledge level.

[0066] 3) Based on the logical network information framework, dynamic time data is integrated into the basic framework, which facilitates the determination of effective time points and their associated grid carbon emission factor values ​​through nonlinear time mapping relationships. Then, the dynamic time data is bound to the building's overall energy consumption entrance entity and related entities in the logical network to obtain a knowledge graph.

[0067] The design simulation module can extract HVAC system equipment entities and their connections from the knowledge graph, construct a complete first directed graph G, and mark key nodes such as energy source points, sink points, and mixing points according to rules such as in-degree and out-degree, forming a set of key nodes. The connection paths between key nodes are topologically simplified, and the paths formed by non-key nodes are aggregated into an equivalent set of edges, generating a simplified second directed graph that retains only key nodes and equivalent edges. This process significantly reduces the complexity of simulation calculations by compressing the system topology, providing the necessary performance guarantee for subsequent real-time carbon-energy coupling simulations.

[0068] The design simulation module includes an identification submodule and an aggregation submodule;

[0069] The identification submodule can extract all entity nodes classified as HVAC system devices from the knowledge graph, as well as the directed edges between these entity nodes, constructing a first directed graph G, G=(V,E), describing the complete connectivity of the system. Here, V is the set of HVAC system device nodes, and E is the set of directed edges representing connections. Predefined rules are used to label all nodes in the directed graph and identify them as the key node set. .

[0070] For example, source device nodes with an in-degree of 0 (such as "chiller" or "boiler") are marked as energy source nodes; device nodes with an out-degree of 0 that serve the end space (such as "air conditioning unit" or "fan coil") are marked as energy sink nodes; and device nodes with multiple inputs and / or multiple outputs (such as "manifold" or "three-way mixing valve") are marked as mixing nodes.

[0071] The aggregation submodule can aggregate key node sets The paths in the code are aggregated. If there is a connecting path between two key nodes consisting of equivalent nodes, then all equivalent edges on the path are determined as the equivalent edge set. And combine them to form the second directed graph. , Furthermore, while preserving the energy transfer and logic between key nodes, intermediate details were removed, reducing computational degrees of freedom and thus improving simulation speed, creating the necessary conditions for subsequent real-time carbon-energy coupling simulations.

[0072] The simulation module acquires dynamic grid carbon factors and building loads in real time through a knowledge graph. Combined with HVAC system equipment design parameters, it determines the real-time operating power of the simulation design and calculates carbon emission shares, generating direct carbon emission responsibility curves at the equipment level. This facilitates the dynamic coupling simulation of building energy consumption, equipment operation, and grid carbon intensity fluctuations. It fundamentally solves the problem of disconnect between HVAC system equipment operation and dynamic carbon signals in traditional simulations, allowing for energy-saving effect assessment during the design phase. Details are as follows:

[0073] 1) Locate the HVAC system equipment parameters corresponding to the simulation time in the knowledge graph, and obtain the real-time grid carbon emission factor value acting on the building's overall energy inlet at the current time. At the same time, by linking various dynamic attribute nodes through the knowledge graph, the current heating and cooling load values ​​of each area of ​​the building can be obtained.

[0074] 2) Using the load requirements of HVAC system equipment parameters as known boundary conditions, and calling the HVAC system equipment design parameter values ​​stored in the knowledge graph, the actual operating power of the HVAC system equipment is obtained through the built-in equipment performance library, so that the simulation calculation can accurately reflect the actual energy consumption and carbon emission performance of the design selection in dynamic scenarios.

[0075] 3) Based on the carbon flow tracing algorithm, the real-time operating power of all devices connected to the HVAC system during the simulation time interval is summed to obtain the total power consumption. And calculate the building's total carbon emissions. The calculation formula is as follows:

[0076]

[0077] in, To be in the time interval Real-time carbon emission factor of internal power grid This represents the simulated time interval.

[0078] 4) After obtaining the total carbon emissions of the building, according to the second directed graph The corresponding equivalent edges are used to determine the carbon emission share that each end node should share. The calculation formula is as follows:

[0079]

[0080] in, To assign weights.

[0081] Assign weights The calculation formula is as follows:

[0082] in, To indicate the first Each end node (such as an air conditioning unit) at time Instantaneous load, This represents the set of terminal nodes corresponding to the equivalent edge. This represents the sum of instantaneous loads at the end nodes at time t.

[0083] 5) Starting from each end node, trace back along the equivalent edge path to the HVAC system equipment point, and connect the carbon emission shares related to the HVAC system equipment point at that simulation moment. This forms the direct carbon emission responsibility curve for that equipment. This provides a convenient and intuitive reflection of the carbon emission responsibility of each device at different times, offering detailed and accurate data support for the dynamic performance evaluation and precise carbon management of HVAC system equipment in building design.

[0084] The online review module can calculate the volatility M of the carbon emission sequence, quantify the system's response sensitivity to grid carbon signals, and, combined with the real-time energy-carbon synergy efficiency B, calculate the dynamic energy-saving value F, which characterizes the overall performance. Then, based on preset compliance thresholds, the module automatically determines the design compliance of the F value, outputs the review conclusion, and generates a structured review report with a complete traceability chain.

[0085] The online review module includes a fluctuation amplitude assessment submodule, a coupling efficiency assessment submodule, and a judgment submodule.

[0086] The fluctuation amplitude assessment submodule can receive the total building carbon emission sequence synchronized with the main time axis from the simulation calculation module. Total power consumption The data includes the grid marginal carbon emission factor sequence CEF(t) from the data matching module, and the magnitude of time-varying carbon emissions simulated by HVAC system equipment. This is used to quantify its response sensitivity to fluctuations in grid carbon intensity. The calculation formula is as follows:

[0087]

[0088] in, The number of time intervals, Total carbon emissions The average value at each time point.

[0089] The coupling efficiency assessment submodule is used to evaluate the overall energy efficiency and carbon efficiency synergy of HVAC system equipment under real-time fluctuations in the grid carbon emission factor. The efficiency index is obtained by calculating the ratio of the total building heating and cooling load demand to the sum of the real-time operating electrical power of the HVAC system equipment. The calculation formula is as follows:

[0090]

[0091] in, The real-time heating and cooling loads at time t are obtained and summarized through the knowledge graph. It represents the sum of the real-time operating electrical power of all HVAC system devices in the second directed graph at the same time.

[0092] The judgment submodule can calculate the magnitude of time fluctuations in simulated carbon emissions from HVAC system equipment based on the objective function. and efficiency indicators Dynamic energy saving value during design Then the dynamic energy saving value The results are compared with the preset threshold range. If the dynamic energy saving value... If the result exceeds the threshold, the energy-saving design scheme is deemed to have a risk of exceeding carbon emission standards in this scenario and requires further optimization. Dynamic energy saving value. The calculation formula is as follows:

[0093]

[0094] in, and These are weighting coefficients pre-set based on policy guidance and design objectives.

[0095] like ≤ ≤ If it is deemed "compliant", then it is considered "compliant". < ≤ This indicates the dynamic energy saving value. If the value slightly exceeds the threshold but remains within the acceptable buffer range, it is considered a "recommendation". The preset buffer threshold upper limit indicates potential for optimization; if If so, it will be judged as "violation".

[0096] The assessment submodule can also automatically generate a structured review report. The report integrates all "compliance," "recommendation," and "violation" entries, each containing the assessment result and design values ​​for the HVAC system equipment. The review report is encrypted and archived, forming a complete and tamper-proof review traceability chain to ensure the transparency of the review process.

[0097] This invention fundamentally solves the problem of the disconnect between HVAC system equipment operation and dynamic carbon signals from the power grid in traditional building energy consumption simulation by constructing a knowledge graph that integrates static attributes and dynamic data. The system achieves "real-time carbon-energy coupled simulation," accurately simulating minute-level carbon emission trajectories under real power grid carbon intensity fluctuations during the design phase, and generating direct carbon emission responsibility curves at the equipment level. This transforms carbon footprint assessment from static averages to dynamic and precise tracking, significantly reducing the deviation between simulation results and actual operating carbon footprint.

[0098] Simultaneously, by combining the dynamic performance indicator F in the online review module, it can automatically determine whether the design scheme meets dynamic carbon constraints under simulated operating conditions. This overcomes the limitation of traditional specification reviews, which can only verify static equipment parameters, and enables the real-time identification of carbon emission exceedance risks during the design phase, ensuring the effectiveness of online reviews.

[0099] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A real-time simulation and online review system for building energy-saving design based on a knowledge graph, characterized in that, It includes a data matching module, a knowledge graph construction module, a design simulation module, a simulation calculation module, and an online review module; The data matching module can access the static attribute data of the building information and external data sources, and align the static attribute data of the building and external data sources and construct a cumulative distance matrix. The knowledge graph construction module performs semantic modeling and fusion of building static attribute data and external data sources based on a predefined domain ontology to form a knowledge graph; The design simulation module can extract HVAC system equipment entities and their connection relationships from the knowledge graph, construct a complete first directed graph, and generate a second directed graph by combining the key node set and the equivalent edge set. The simulation calculation module obtains dynamic power grid carbon factors in real time through knowledge graph, determines the real-time operating power of the simulation design by combining the design parameter values ​​of HVAC system equipment, calculates the total carbon emissions and carbon emission share, and generates direct carbon emission responsibility curves at the equipment level. Carbon emission share The calculation formula is as follows: wherein, to assign weights, total carbon emissions; The online review module can calculate the volatility M of the carbon emission sequence based on the total carbon emissions, combine it with the real-time energy-carbon synergy efficiency B, calculate the dynamic energy saving value F that characterizes the overall performance, and automatically determine the design compliance and output the review conclusion. 2.The knowledge graph-based real-time simulation and online review system for building energy-saving design according to claim 1, wherein, The specific contents of the knowledge graph construction module are as follows: 1) Process building static attribute data to obtain logical network information; 2) Process the marginal carbon emission factor sequence of the power grid to obtain dynamic time data; 3) Based on the logical network information framework, dynamic time data is integrated into the framework, so that the dynamic time data is bound to the building's overall energy consumption entrance entity and related entities in the logical network to obtain a knowledge graph. 3.The real-time simulation and online review system for building energy-saving design based on knowledge graph according to claim 1, wherein, The building static attribute data includes HVAC system equipment parameters, pipeline parameters, and equipment nameplate parameters, and the external data source includes the power grid marginal carbon emission factor sequence. 4.The knowledge graph-based real-time simulation and online review system for building energy-saving design according to claim 1, wherein, The design simulation module includes an identification submodule and an aggregation submodule; The identification submodule can mark all nodes in the directed graph through a preset rule and determine a key node set ; The aggregation submodule is capable of processing key node sets. The paths in the graph are aggregated. If there is a connecting path between two key nodes consisting of equivalent nodes, then all equivalent edges on the path are determined as an equivalent edge set, and a second directed graph is constructed.

5. The real-time simulation and online review system for building energy-saving design based on knowledge graphs according to claim 1, characterized in that, The specific contents of the simulation calculation module are as follows: 1) Locate the HVAC system equipment parameters corresponding to the simulation time in the knowledge graph, and obtain the real-time grid carbon emission factor value acting on the building's overall energy inlet at the current time. Meanwhile, by using the dynamic attribute nodes linked by the knowledge graph, the current heating and cooling load values ​​of each area of ​​the building can be obtained; 2) Using the load demand of HVAC system equipment parameters as known boundary conditions, and simultaneously calling the HVAC system equipment design parameter values ​​stored in the knowledge graph, the actual operating power of the HVAC system equipment is obtained through the built-in equipment performance library; 3) Based on the carbon flow tracing algorithm, the real-time operating power of all devices connected to the HVAC system during the simulation time interval is summed to obtain the total power consumption. And calculate the building's total carbon emissions. ; The calculation formula is as follows: in, To be in the time interval Real-time carbon emission factor of internal power grid This is a simulated time interval; 4) After obtaining the total carbon emissions of the building, according to the second directed graph The corresponding equivalent edges are used to determine the carbon emission share that each end node should share. ; 5) Starting from each end node, backtrack along the equivalent edge path to the HVAC system equipment point, connect the carbon emission shares related to the HVAC system equipment point at that simulation moment, and obtain the direct carbon emission responsibility curve.

6. The real-time simulation and online review system for building energy-saving design based on knowledge graphs according to claim 1, characterized in that, The online review module includes a fluctuation amplitude assessment submodule, a coupling efficiency assessment submodule, and a judgment submodule; The fluctuation amplitude assessment submodule can receive the total building carbon emission sequence output by the simulation calculation module, which is synchronized with the main time axis. Total power consumption And calculate the magnitude of carbon emission fluctuations over time for simulated HVAC system equipment. The calculation formula is as follows: in, The number of time intervals, Total carbon emissions The average value at each time point.

7. The real-time simulation and online review system for building energy-saving design based on knowledge graphs according to claim 6, characterized in that, The coupling efficiency evaluation submodule can calculate efficiency indicators. This is used to evaluate the overall energy efficiency and carbon efficiency synergy of HVAC system equipment under real-time fluctuations in the grid carbon emission factor. The calculation formula is as follows: in, The real-time heating and cooling loads at time t are obtained and summarized through the knowledge graph. It represents the sum of the real-time operating electrical power of all HVAC system devices in the second directed graph at the same time.

8. The real-time simulation and online review system for building energy-saving design based on knowledge graphs according to claim 6, characterized in that, The judgment submodule can calculate the amplitude of carbon emission fluctuations over time in the HVAC system equipment simulation based on the objective function. and efficiency indicators Dynamic energy saving value during design Then the dynamic energy saving value The results are compared with the preset threshold range.

9. The real-time simulation and online review system for building energy-saving design based on knowledge graphs according to claim 8, characterized in that, The dynamic energy saving value The calculation formula is as follows: in, and These are weighting coefficients pre-set based on policy guidance and design objectives.

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