Intelligent control building energy consumption monitoring data management method and system fusing digital twinning

Abstract

The invention relates to the technical field of digital twinning, and solves the technical problems of insufficient building energy consumption analysis accuracy and difficulty in timely and effective generation of a control strategy caused by difficulty in realizing dynamic fusion of multi-source real-time monitoring data and a building digital twinning model in the prior art. The invention provides an intelligent control building energy consumption monitoring data management method and system fusing digital twinning. The method comprises the following steps: constructing an initial building digital twin model; performing model updating on the initial building digital twinning model to obtain a target building digital twinning model; based on the target building digital twin model, obtaining a building energy consumption analysis result; determining a building operation control target according to a building energy consumption analysis result, determining constraint conditions and adjustable parameters based on a target building digital twin model, and generating a control strategy according to the constraint conditions and the adjustable parameters; and according to the control strategy, intelligent control of building energy consumption is realized. According to the invention, refined, dynamic and intelligent control of building energy consumption can be realized.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of digital twinning, in particular to a digital twinning-fused intelligent control building energy consumption monitoring data management method and system. BACKGROUND

[0002] With the development of new generation information technology, digital twinning technology has gradually become an important technical means for monitoring and managing complex systems. Digital twinning technology realizes the mapping and synchronous updating of the structure, state and behavior of a physical system by constructing a digital model corresponding to the physical entity in a virtual space, thereby supporting the visual display of the running state of the physical system, the analysis of the running process and the prediction of the future state. This technology usually integrates various technical means such as sensing monitoring, data modeling and simulation calculation, and has been widely concerned and applied in the fields of industrial manufacturing, energy management and smart city.

[0003] In the field of building energy consumption monitoring and management, the existing digital twinning technology usually constructs a three-dimensional structure model or a device running model of the building, and maps part of the energy consumption data to the virtual model for displaying the overall energy consumption level of the building or the running state of a single device. However, the construction and updating of the digital twinning model in the existing technology usually focuses on static structure or single-source data mapping, and lacks unified fusion and dynamic updating mechanism for real-time environmental data, device running data and personnel access and other multi-source heterogeneous data, resulting in deviation between the digital twinning model and the actual running state of the building; at the same time, the existing energy consumption analysis is mostly based on historical statistics or single parameter analysis, which is difficult to comprehensively reflect the real energy consumption characteristics of the building under the current running state in the digital twinning model, so as to provide accurate and reliable decision basis for the formulation of building operation control target and intelligent adjustment of energy consumption equipment.

[0004] Therefore, how to realize the unified fusion and dynamic mapping of multi-source real-time monitoring data in the process of building operation, construct a digital twinning model that can truly reflect the current running state of the building, and accurately analyze the building energy consumption based on the model, so as to provide reliable decision basis for the intelligent control of building energy consumption equipment, is a technical problem to be solved. SUMMARY

[0005] Therefore, the present application provides a digital twinning-fused intelligent control building energy consumption monitoring data management method and system to solve the technical problem that the existing technology cannot realize the dynamic fusion of multi-source real-time monitoring data and building digital twinning model, resulting in insufficient accuracy of building energy consumption analysis and difficulty in timely and effective generation of control strategy.

[0006] The technical scheme adopted by the present application is as follows: In a first aspect, the present application provides a digital twinning-fused intelligent control building energy consumption monitoring data management method, which comprises: Based on building structure information and building energy consumption equipment information, the spatial structure status of the building and the operating status of energy consumption equipment are modeled to obtain an initial digital twin model of the building. Acquire real-time monitoring data, map the real-time monitoring data to the initial building digital twin model, update the initial building digital twin model, and obtain the updated target building digital twin model. The real-time monitoring data includes real-time environmental data, real-time equipment operation data, and real-time building personnel entry and exit data. Based on the digital twin model of the target building, the energy consumption of the building under the current operating state is simulated and analyzed to obtain the building energy consumption analysis results; Based on the building energy consumption analysis results, a preset building operation control target is determined, and based on the target building digital twin model, constraints and adjustable parameters corresponding to the building operation control target are determined, and a control strategy is generated based on the constraints and adjustable parameters. According to the control strategy, the operating parameters of building energy consumption equipment are adjusted to achieve intelligent control of building energy consumption.

[0007] Preferably, the step of modeling the spatial structural state of the building and the operating state of the energy-consuming equipment based on the building structure information and building energy-consuming equipment information to obtain an initial building digital twin model includes: Based on the building structure information, the building's floor structure, functional areas, and spatial topology are modeled to obtain a building spatial structure model; Based on the building energy consumption equipment information, the equipment type, operating parameters and energy consumption characteristics of each energy consumption equipment in the building are modeled to obtain the energy consumption equipment operation model; Based on the building space structure model and the energy consumption equipment operation model, establish the relationship between the energy consumption equipment and the building space structure; Based on the building space structure model, the energy consumption equipment operation model, and the associated relationships, an initial building digital twin model is constructed.

[0008] Preferably, the steps of acquiring real-time monitoring data, mapping the real-time monitoring data to the initial building digital twin model, and updating the initial building digital twin model to obtain the updated target building digital twin model include: The real-time environmental data, real-time equipment operation data, and real-time building personnel access data are collected respectively to obtain the real-time monitoring data; The real-time monitoring data is preprocessed to obtain preprocessed target environment data, target equipment operation data, and target building personnel entry and exit data. The preprocessing includes timestamp calibration and spatial location identification of the real-time environment data, real-time equipment operation data, and real-time building personnel entry and exit data to achieve consistency of the real-time monitoring data in both time and spatial dimensions. The target environment data, target equipment operation data, and target building personnel entry and exit data are mapped to the initial building digital twin model, and the initial building digital twin model is updated to obtain the target building digital twin model.

[0009] Preferably, the preprocessing of the real-time monitoring data to obtain preprocessed target environment data, target equipment operation data, and target building personnel entry and exit data includes: The real-time monitoring data is timestamped and aligned to unify the time reference of real-time monitoring data from different sources, resulting in time-aligned monitoring data. Configure corresponding spatial location identifiers for the time-aligned monitoring data, and map the time-aligned monitoring data to corresponding spatial units under a unified building space reference system based on the spatial location identifiers, so that real-time monitoring data from different sources have a consistent spatial reference relationship in the spatial dimension, thereby obtaining spatiotemporally aligned monitoring data. Based on the spatiotemporally aligned monitoring data, a set of associated monitoring data within the same time point, adjacent time points, and adjacent spatial units is determined; Based on the associated monitoring data set, outlier detection is performed on the spatiotemporally aligned monitoring data to identify abnormal monitoring data whose numerical deviation from the associated monitoring data exceeds a preset anomaly judgment threshold, and the abnormal monitoring data is removed to obtain monitoring data with missing data. Based on the spatial location identifier, the spatial unit adjacent to the spatial unit to which the monitoring data with missing data belongs is determined, and the monitoring data collected by the adjacent spatial units at the same time point or adjacent time points is combined to complete the monitoring data with missing data to obtain the completed data. Based on the supplemented data, environmental status parameters, equipment operation status parameters, and space usage status parameters are extracted to determine the target environmental data, target equipment operation data, and target building personnel entry and exit data.

[0010] Preferably, the step of simulating and analyzing the energy consumption of the building under its current operating state based on the digital twin model of the target building, and obtaining the building energy consumption analysis results, includes: Based on the target building digital twin model, obtain the building's spatial structure status, equipment operation status parameters, environmental status parameters, and space usage status parameters at the current moment; Based on the spatial structure state and the equipment operating state parameters, determine the target energy-consuming equipment to participate in the energy consumption analysis under the current operating state; Based on the device operating status parameters of the target energy-consuming device, and in combination with the environmental status parameters and the space usage status parameters, the energy consumption of the target energy-consuming device within a preset time period is simulated and calculated to obtain the device energy consumption data of each target energy-consuming device. Based on the energy consumption data of the equipment and in conjunction with the spatial structure status, the energy consumption data of each target energy-consuming equipment are summarized and analyzed in a spatial dimension to obtain the energy consumption analysis results of the building under the current operating state.

[0011] Preferably, the step of simulating and calculating the energy consumption of the target energy-consuming device within a preset time period based on the device operating status parameters of the target energy-consuming device, combined with the environmental status parameters and the space usage status parameters, to obtain the device energy consumption data of each target energy-consuming device includes: Based on the digital twin model of the target building, the spatial unit to which each target energy-consuming device belongs is determined, and the device operation status parameters corresponding to each target energy-consuming device in the current operating state are obtained; Obtain the environmental state parameters and space usage state parameters corresponding to the space unit in its current operating state, and establish the correspondence between the target energy-consuming device and the environmental state parameters and space usage state parameters; Based on the environmental state parameters, the operating conditions of the target energy-consuming equipment are corrected to determine the equipment operating load correction parameters under the influence of the environmental state. Based on the space usage status parameters, the operating intensity or operating time of the target energy-consuming equipment is corrected, and the equipment operation demand correction parameters under the influence of space usage status are determined. Based on the equipment operating status parameters, the equipment operating load correction parameters, and the equipment operating demand correction parameters, the target operating status parameters of each target energy-consuming device are determined within the preset time period. According to the preset time step, the target operating state parameters are expanded in a time-division manner, and the equipment energy consumption within each time step is calculated based on the preset energy consumption calculation model. The energy consumption of the equipment calculated within each time step is accumulated to obtain the energy consumption data of each target energy-consuming equipment within the preset time period.

[0012] Preferably, the step of determining a preset building operation control target based on the building energy consumption analysis results, and determining the constraints and adjustable parameters corresponding to the building operation control target based on the target building digital twin model, and generating a control strategy based on the constraints and adjustable parameters includes: Based on the building energy consumption analysis results, the overall energy consumption level of the building under the current operating state, the energy consumption distribution of each spatial unit, and the equipment energy consumption data of each target energy-consuming device are obtained. Based on the overall energy consumption level, the energy consumption distribution, and the equipment energy consumption data, at least one building operation control objective is determined. The building operation control objective includes at least one of the following: energy consumption reduction objective, energy consumption balance objective, and operation comfort maintenance objective. Based on the target building digital twin model, obtain the equipment operation status parameters, environmental status parameters, and space usage status parameters related to the building operation control target; Based on the pre-set operational constraints, spatial structure constraints, and environmental adaptation constraints of each device in the target building digital twin model, determine the constraint conditions corresponding to the building operation control target; Under the premise of satisfying the constraints, adjustable parameters are determined from the equipment operating status parameters, and the adjustable parameters include at least one of equipment operating power, operating period or operating priority; Based on the building operation control objectives, the constraints, and the adjustable parameters, a control strategy for adjusting the operating status of building energy-consuming equipment is generated.

[0013] Preferably, the step of generating a control strategy for adjusting the operating state of building energy-consuming equipment based on the building operation control objective, the constraints, and the adjustable parameters includes: Based on the building operation control objectives, control evaluation indicators corresponding to the building operation control objectives are determined. The control evaluation indicators are used to characterize the energy consumption control effect and the rationality of the operation status. Under the premise of satisfying the constraints, construct the parameter combination space of the adjustable parameters to obtain multiple candidate operating parameter combinations; Based on the digital twin model of the target building, simulation calculations are performed on each of the candidate operating parameter combinations to obtain the corresponding energy consumption prediction results and operating status prediction results; Based on the energy consumption prediction results, the operating status prediction results, and the control evaluation indicators, each candidate operating parameter combination is evaluated to obtain each evaluation result; The target operating parameter combination is determined from the candidate operating parameter combinations whose evaluation results satisfy the building operation control objectives; Based on the target combination of operating parameters, a control strategy is generated to adjust the operating status of the building's energy-consuming equipment.

[0014] In a second aspect, the present invention provides an intelligent building energy consumption monitoring and management system integrating digital twins, comprising: at least one processor, at least one memory, and computer program instructions stored in the memory, wherein when the computer program instructions are executed by the processor, the method of the first aspect described above is implemented.

[0015] In summary, the beneficial effects of the present invention are as follows: The present invention provides a method and system for intelligent building energy consumption monitoring data management that integrates digital twins. The method includes: modeling the spatial structure state of the building and the operating state of the energy consumption equipment based on building structure information and building energy consumption equipment information to obtain an initial building digital twin model; acquiring real-time monitoring data, mapping the real-time monitoring data to the initial building digital twin model, and updating the initial building digital twin model to obtain an updated target building digital twin model. The real-time monitoring data includes real-time environmental data and real-time equipment operating data. Based on real-time building occupancy data, and using the target building's digital twin model, the energy consumption of the building under its current operating state is simulated and analyzed to obtain building energy consumption analysis results. Based on these results, a preset building operation control target is determined, and based on the target building's digital twin model, constraints and adjustable parameters corresponding to the control target are determined. A control strategy is generated based on these constraints and adjustable parameters. According to the control strategy, the operating parameters of the building's energy-consuming equipment are adjusted to achieve intelligent control of building energy consumption. This invention effectively solves the technical problems of insufficient integration of multi-source real-time monitoring data and building operation status, and lagging energy consumption analysis and control decisions in existing technologies by constructing and dynamically updating a building digital twin model. First, based on building structure information and energy-consuming equipment information, a unified model is created for the building's spatial structure and the operation status of energy-consuming equipment, forming an initial building digital twin model with spatial topological relationships and equipment energy consumption characteristics. This provides a complete and computable foundational model for subsequent analysis. Then, multi-source real-time monitoring data, including real-time environmental data, real-time equipment operation data, and real-time building personnel access data, are introduced and mapped onto the initial building digital twin model. The model's state is continuously updated, enabling the digital twin to continuously improve its functionality and control decisions. The digital twin model can realistically and dynamically reflect the actual operating status of a building under different time and spatial dimensions. Based on this, relying on the updated target building digital twin model, the energy consumption of the building under the current operating state is simulated and analyzed, thereby obtaining energy consumption analysis results that are closer to actual working conditions. Furthermore, based on the energy consumption analysis results, the building operation control target that meets the current operating requirements is determined, and combined with the equipment operation constraints, environmental constraints, and space usage constraints reflected in the digital twin model, adjustable operating parameters are identified and targeted control strategies are generated. Finally, by adjusting the operating parameters of the building's energy-consuming equipment according to the control strategies, refined, dynamic, and intelligent control of building energy consumption is achieved. Attached Figure Description

[0016] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the embodiments of the present invention will be briefly introduced below. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort, and these are all within the protection scope of the present invention.

[0017] Figure 1 This is a schematic diagram illustrating the overall workflow of the intelligent building energy consumption monitoring data management method integrating digital twins in Embodiment 1 of the present invention; Figure 2 This is a flowchart illustrating the process of simulating and analyzing the energy consumption of a building under its current operating state in Embodiment 1 of the present invention to obtain the building energy consumption analysis results. Figure 3 This is a flowchart illustrating the process of generating a control strategy for adjusting the operating status of building energy-consuming equipment in Embodiment 1 of the present invention. Figure 4 This is a schematic diagram of the intelligent building energy consumption monitoring data management system integrating digital twins in Embodiment 2 of the present invention. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. In the description of the present invention, it should be understood that the terms "center," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer," etc., indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the referred device or element must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the present invention. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus 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, method, article, or apparatus. Unless otherwise specified, the element defined by the phrase "comprising..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element. Where there is no conflict, embodiments of the present invention and the various features thereof can be combined with each other, all of which are within the scope of protection of the present invention.

[0019] Example 1 Please see Figure 1 Embodiment 1 of the present invention discloses a method for managing intelligent building energy consumption monitoring data by integrating digital twins, the method comprising: Based on building structure information and building energy consumption equipment information, the spatial structure status of the building and the operating status of energy consumption equipment are modeled to obtain an initial digital twin model of the building. Specifically, in building energy management, spatial structure and equipment distribution are crucial fundamental factors influencing energy consumption. By incorporating building structure information, the hierarchy and connections between floors, rooms, functional areas, and spaces can be expressed in model form. Building energy-consuming equipment information is used to characterize the differences in equipment types, operational capabilities, and their deployment locations within the building. Based on this, the spatial structure status and the operational status of energy-consuming equipment are integrated into the modeling process, enabling the virtual model to simultaneously reflect the building's physical composition and the basic characteristics of equipment operation. This process typically involves constructing spatial unit models, equipment object models, and their relationships, thereby forming an initial digital twin model that is consistent with the real building structure and contains complete equipment information. This provides a unified platform for subsequent data access and dynamic analysis. Acquire real-time monitoring data, map the real-time monitoring data to the initial building digital twin model, update the initial building digital twin model, and obtain the updated target building digital twin model. The real-time monitoring data includes real-time environmental data, real-time equipment operation data, and real-time building personnel entry and exit data. Specifically, the environmental, equipment, and personnel access data continuously generated during building operation directly reflect the actual operating status at any given moment. Introducing this real-time monitoring data into a digital twin model allows the model to move beyond a static description and evolve synchronously with the real building's condition. By associating environmental data with corresponding spatial units, equipment operation data with specific equipment objects, and personnel access data with space usage status, the model's internal parameters are dynamically updated based on the monitoring data, thus forming a digital twin model of the target building that reflects its current operating conditions. Through this update process, the state deviation between the model and the real building is continuously corrected, providing a realistic state basis for subsequent energy consumption calculations.

[0020] Based on the digital twin model of the target building, the energy consumption of the building under the current operating state is simulated and analyzed to obtain the building energy consumption analysis results; Specifically, after the model update, the digital twin model is capable of describing the current operating status of the building. Using this model, energy consumption behavior can be calculated and extrapolated in a virtual environment, such as analyzing the energy consumption contribution of different devices under current operating conditions and the energy consumption distribution of each spatial unit. Through comprehensive calculations of device operating status, environmental conditions, and space usage, analytical results reflecting the overall energy consumption level and structural characteristics can be obtained. This type of analysis does not rely on simple historical statistics but is based on the current operating status, making energy consumption results more timely and targeted, providing an intuitive basis for subsequent control and decision-making.

[0021] Based on the building energy consumption analysis results, a preset building operation control target is determined, and based on the target building digital twin model, constraints and adjustable parameters corresponding to the building operation control target are determined, and a control strategy is generated based on the constraints and adjustable parameters. Specifically, after obtaining the energy consumption analysis results, energy consumption problems or optimization opportunities in the current operating state can be further identified, such as equipment with excessive energy consumption or areas with uneven load. Based on this, and combined with the building's operational needs, corresponding operational control objectives are formed, and equipment parameters and spatial states related to these objectives are selected in the digital twin model. Simultaneously, pre-defined operational boundaries, equipment safety conditions, and environmental adaptation requirements in the model constrain control behavior. Within these constraints, adjustable operational parameters are combined and matched to form a control strategy that guides the building's operational state towards the target direction, maintaining a balance between feasibility and effectiveness in the control scheme.

[0022] According to the control strategy, the operating parameters of building energy consumption equipment are adjusted to achieve intelligent control of building energy consumption.

[0023] Specifically, once the control strategy is formulated, the building's operating status changes accordingly by adjusting the operating parameters of actual energy-consuming equipment. Related equipment can adjust its operating power, operating time, or operating priority according to the control strategy, gradually bringing the overall energy consumption performance closer to the expected control target. As the equipment status changes, new operating data is collected again and fed back to the digital twin model, forming a continuously iterative operating loop. Through this process, building energy management no longer relies on manual experience or single rules, but instead relies on a model-driven intelligent control method to achieve dynamic optimization and stable control of energy consumption levels.

[0024] Preferably, the step of modeling the spatial structural state of the building and the operating state of the energy-consuming equipment based on the building structure information and building energy-consuming equipment information to obtain an initial building digital twin model includes: Based on the building structure information, the building's floor structure, functional areas, and spatial topology are modeled to obtain a building spatial structure model; Specifically, building structure information reflects the spatial organization of a building at the physical level, including the distribution of different floors, the division of functional areas within each floor, and the connections between spaces. When modeling this type of information, floors, rooms, and areas are typically abstracted as independent spatial units, and the connectivity, inclusion, or hierarchical relationships between adjacent spaces are further described to form a complete spatial topology. By constructing a building spatial structure model, the actual spatial layout of the building can be digitally reconstructed, providing a clear spatial reference basis for subsequent energy consumption analysis and equipment association, and facilitating a refined depiction of the operational differences in different areas.

[0025] Based on the building energy consumption equipment information, the equipment type, operating parameters and energy consumption characteristics of each energy consumption equipment in the building are modeled to obtain the energy consumption equipment operation model; Specifically, different types of energy-consuming equipment in buildings exhibit significant differences in their operating mechanisms and energy consumption behaviors. For example, air conditioning equipment, elevator equipment, and lighting equipment differ in their power variation patterns and usage patterns. By uniformly modeling equipment types, adjustable operating parameters, and energy consumption characteristics, various types of equipment can be abstracted into equipment models with standard attributes and behavioral rules. This modeling approach enables the accurate description of equipment operating states and energy consumption performance within a digital twin environment, providing clear equipment-level input conditions for subsequent energy consumption simulation calculations, thereby improving the overall model's ability to depict the actual operating conditions of equipment.

[0026] Based on the building space structure model and the energy consumption equipment operation model, establish the relationship between the energy consumption equipment and the building space structure; Specifically, in actual buildings, energy-consuming equipment does not operate in isolation but is deployed within specific spatial units. Its operational status often directly impacts the environmental conditions and energy consumption levels of the area. By linking the operational model of energy-consuming equipment with the building's spatial structure model, the spatial location and service range of each piece of equipment can be clearly defined, thus establishing a one-to-one or one-to-many relationship between equipment and space. This association allows for accurate tracing of relevant equipment when analyzing the energy consumption or operational status of a specific spatial unit, and also enables reasonable attribution and statistical analysis of equipment energy consumption results across spatial dimensions.

[0027] Based on the building space structure model, the energy consumption equipment operation model, and the associated relationships, an initial building digital twin model is constructed.

[0028] Specifically, after completing spatial modeling, equipment modeling, and establishing the relationship between the two, the aforementioned model elements can be integrated to form an initial building digital twin model. This model not only includes the building's spatial structure information and basic equipment operation information, but also reflects the actual distribution of equipment in space, providing a complete structural framework for subsequent real-time data mapping and model updates. Through this integration process, the digital twin model possesses the fundamental capabilities to carry multi-source data, support operational status evolution, and conduct energy consumption analysis, providing reliable model support for subsequent intelligent analysis and control strategy generation.

[0029] Preferably, the steps of acquiring real-time monitoring data, mapping the real-time monitoring data to the initial building digital twin model, and updating the initial building digital twin model to obtain the updated target building digital twin model include: The real-time environmental data, real-time equipment operation data, and real-time building personnel access data are collected respectively to obtain the real-time monitoring data; Specifically, by synchronously collecting different types of data, the basic inputs required for the dynamic updating of the digital twin model are provided. Real-time environmental data reflects changes in the objective environment, such as temperature, humidity, and air quality, within the building or in specific areas. Real-time equipment operation data describes the current operating conditions and energy consumption status of each energy-consuming device. Real-time building occupancy data characterizes the intensity of space usage and the characteristics of occupant activity. By collecting these multi-source data, the actual operating status of the building can be comprehensively reflected from three dimensions: environment, equipment, and occupants, providing sufficient and fine-grained data support for subsequent model updates and energy consumption analysis.

[0030] The real-time monitoring data is preprocessed to obtain preprocessed target environment data, target equipment operation data, and target building personnel entry and exit data. The preprocessing includes timestamp calibration and spatial location identification of the real-time environment data, real-time equipment operation data, and real-time building personnel entry and exit data to achieve consistency of the real-time monitoring data in both time and spatial dimensions. Specifically, since different monitoring data typically originate from various sensors or information systems, their acquisition frequencies, time bases, and spatial affiliations often differ, making direct use prone to ineffective data correspondence. By timestamping and calibrating various real-time monitoring data, data from different sources can be unified under a common time reference, ensuring that environmental conditions, equipment status, and personnel activity information at the same moment can be matched. Simultaneously, by configuring clear spatial location identifiers for space-related data, each monitoring data point can be mapped to a specific floor, area, or spatial unit, thereby establishing a unified reference system at the spatial level. The target environmental data, target equipment operation data, and target building personnel access data generated after this preprocessing exhibit good spatiotemporal consistency, facilitating accurate mapping with subsequent digital twin models.

[0031] The target environment data, target equipment operation data, and target building personnel entry and exit data are mapped to the initial building digital twin model, and the initial building digital twin model is updated to obtain the target building digital twin model.

[0032] Specifically, after data acquisition and preprocessing, the processed multi-source data needs to be introduced into the digital twin model to drive the dynamic evolution of the model's state. By mapping target environmental data to corresponding spatial units, the environmental state of each area in the model can be updated; by mapping target equipment operation data to corresponding energy consumption equipment models, the operating conditions of the equipment can be reflected in real time; and by introducing target building personnel entry and exit data, it helps to characterize changes in space usage. With the continuous input of this data, various state parameters in the initial building digital twin model are synchronously corrected and updated, thus forming a target building digital twin model that can truly reflect the current building operating state, laying a reliable foundation for subsequent energy consumption simulation analysis and control strategy generation.

[0033] Preferably, the preprocessing of the real-time monitoring data to obtain preprocessed target environment data, target equipment operation data, and target building personnel entry and exit data includes: The real-time monitoring data is timestamped and aligned to unify the time reference of real-time monitoring data from different sources, resulting in time-aligned monitoring data. Specifically, during building operation, real-time environmental data, equipment operation data, and personnel access data are typically collected by different sensing terminals or management systems. These data often differ in sampling cycles, reporting frequencies, and local time bases. By timestamping the real-time monitoring data, various types of data can be converted to a unified time reference system, enabling them to accurately correspond to the building's operational status at the same moment or within the same time window. After time alignment, monitoring data from different sources form a consistent descriptive basis in the time dimension, providing a reliable prerequisite for subsequent multi-source data correlation analysis and status fusion.

[0034] Configure corresponding spatial location identifiers for the time-aligned monitoring data, and map the time-aligned monitoring data to corresponding spatial units under a unified building space reference system based on the spatial location identifiers, so that real-time monitoring data from different sources have a consistent spatial reference relationship in the spatial dimension, thereby obtaining spatiotemporally aligned monitoring data. Specifically, after completing time alignment, to further clarify the actual spatial attribution of various monitoring data within the building, it is necessary to configure clear spatial location identifiers for the time-aligned monitoring data. These spatial location identifiers indicate the floor, functional area, or specific spatial unit to which the monitoring data belongs, and based on this, the monitoring data is mapped to a unified building spatial reference system. Through this mapping method, monitoring data from different sources and of different types can be located and compared within the same spatial framework, thereby eliminating differences in spatial representation and establishing a consistent reference relationship between the monitoring data in both time and space dimensions, resulting in spatiotemporally aligned monitoring data that can be used for subsequent analysis.

[0035] Based on the spatiotemporally aligned monitoring data, a set of associated monitoring data within the same time point, adjacent time points, and adjacent spatial units is determined; Specifically, after obtaining monitoring data with a unified spatiotemporal reference, the inherent correlations between the data can be further explored. By analyzing the distribution of monitoring data at the same time point, adjacent time points, and adjacent spatial units, data that are adjacent in time or space are combined to form corresponding correlated monitoring data sets. This correlated monitoring data set can reflect the continuity characteristics of the building's operational status during the temporal evolution and spatial diffusion process, providing the necessary contextual information for identifying abnormal changes, judging the rationality of data, and subsequent data supplementation processing.

[0036] Based on the associated monitoring data set, outlier detection is performed on the spatiotemporally aligned monitoring data to identify abnormal monitoring data whose numerical deviation from the associated monitoring data exceeds a preset anomaly judgment threshold, and the abnormal monitoring data is removed to obtain monitoring data with missing data. Specifically, based on the associated monitoring data set, the reasonableness of individual monitoring data can be judged within its temporal and spatial context. By comparing and analyzing a monitoring data point with associated monitoring data at the same time point, adjacent time points, or adjacent spatial units, data that significantly deviates from the overall trend or spatial distribution pattern can be identified. When the numerical deviation of the monitoring data exceeds a preset anomaly judgment threshold, it can be identified as abnormal monitoring data and removed from subsequent analysis. After this processing, although local monitoring data is removed, the overall dataset is significantly improved in terms of reliability and consistency, but it also creates data gaps that need to be filled. Based on the spatial location identifier, the spatial unit adjacent to the spatial unit to which the monitoring data with missing data belongs is determined, and the monitoring data collected by the adjacent spatial units at the same time point or adjacent time points is combined to complete the monitoring data with missing data to obtain the completed data. Specifically, for data gaps resulting from outlier removal, the spatial location identifiers already present in the monitoring data can be used to accurately determine the spatial affiliation of the missing data within the building, and further locate adjacent spatial units. By introducing monitoring data collected from adjacent spatial units at the same or adjacent time points, and combining this with temporal continuity characteristics, the missing data can be reasonably inferred and supplemented, thereby restoring the continuous representation of the monitoring data in both time and space. This supplementation method fully utilizes the correlation between the building's spatial structure and the operational status of adjacent areas, making the supplementation results more consistent with the actual operational scenario and avoiding the distortion problems caused by simple interpolation.

[0037] Based on the supplemented data, environmental status parameters, equipment operation status parameters, and space usage status parameters are extracted to determine the target environmental data, target equipment operation data, and target building personnel entry and exit data.

[0038] Specifically, after anomaly handling and data completion, the monitoring data possesses high integrity and consistency, serving as reliable input for subsequent updates to the digital twin model. By classifying and extracting the completed monitoring data, monitoring information reflecting environmental changes is organized into environmental state parameters, data describing equipment operation is organized into equipment operation state parameters, and personnel access information is converted into space usage state parameters. This results in clearly structured and semantically defined target environmental data, target equipment operation data, and target building personnel access data. This data can be directly mapped into the building's digital twin model, providing a stable data foundation for subsequent energy consumption simulation and control decisions.

[0039] Preferably, please refer to Figure 2The energy consumption of the target building under its current operating state is simulated and analyzed based on the digital twin model of the target building, and the resulting building energy consumption analysis results include: Based on the target building digital twin model, obtain the building's spatial structure status, equipment operation status parameters, environmental status parameters, and space usage status parameters at the current moment; Specifically, in the digital twin model of the target building, the spatial structure status describes the spatial division relationships of the building at the logical and physical levels, such as floors, functional areas, and their connection methods; equipment operation status parameters reflect the current operating conditions of various energy-consuming devices, such as power levels, start / stop status, or load ratios; environmental status parameters are usually derived from environmental monitoring data and are used to characterize environmental conditions such as temperature, humidity, or air quality; and space usage status parameters reflect the intensity of personnel activities or usage in each space unit. By uniformly obtaining the above multi-dimensional status information from the digital twin model of the target building, the comprehensive operating status of the building at the current moment can be fully depicted, providing a consistent and synchronized data foundation for subsequent energy consumption analysis.

[0040] Based on the spatial structure state and the equipment operating state parameters, determine the target energy-consuming equipment to participate in the energy consumption analysis under the current operating state; Specifically, not all equipment within a building contributes to energy consumption at any given moment. Therefore, a screening analysis combining spatial structure status and equipment operating status parameters is necessary. Spatial structure status clarifies the spatial unit to which each piece of equipment belongs and its functional attributes; equipment operating status parameters identify whether the equipment is in operation, standby, or disabled. Based on this, equipment currently in effective operation and relevant to the space's usage and energy consumption needs is selected as the target energy-consuming equipment for the energy consumption analysis. This process helps avoid interference from ineffective or redundant equipment in the energy consumption analysis results, improving the relevance and accuracy of the analysis.

[0041] Based on the device operating status parameters of the target energy-consuming device, and in combination with the environmental status parameters and the space usage status parameters, the energy consumption of the target energy-consuming device within a preset time period is simulated and calculated to obtain the device energy consumption data of each target energy-consuming device. Specifically, after identifying the target energy-consuming equipment, energy consumption simulation no longer relies solely on the equipment's own operating parameters. Instead, it incorporates environmental and spatial usage parameters for comprehensive modeling. Environmental parameters reflect the impact of external or indoor environments on equipment operating loads, such as the effect of temperature changes on air conditioning energy consumption. Spatial usage parameters reflect the impact of human activity levels on equipment operating intensity and duration. By incorporating these factors into the energy consumption calculation process and simulating within a preset time period, the energy consumption changes of each target energy-consuming device under actual operating conditions can be more realistically reflected, resulting in highly reliable equipment energy consumption data.

[0042] Based on the energy consumption data of the equipment and in conjunction with the spatial structure status, the energy consumption data of each target energy-consuming equipment are summarized and analyzed in a spatial dimension to obtain the energy consumption analysis results of the building under the current operating state.

[0043] Specifically, after obtaining the energy consumption data of each target energy-consuming device, this data is organized and summarized spatially in conjunction with the spatial structure status. This allows for analysis of building energy consumption from both overall and local perspectives. By mapping device energy consumption back to corresponding spatial units, not only can the energy consumption distribution of each spatial unit be obtained, but further analysis of energy consumption differences and concentrated energy consumption areas between different floors and functional areas can also be conducted. Based on this spatialized energy consumption analysis, a holistic understanding of the building's current energy consumption level can be formed, providing an intuitive and quantifiable basis for determining subsequent operational control targets and formulating control strategies.

[0044] Preferably, the step of simulating and calculating the energy consumption of the target energy-consuming device within a preset time period based on the device operating status parameters of the target energy-consuming device, combined with the environmental status parameters and the space usage status parameters, to obtain the device energy consumption data of each target energy-consuming device includes: Based on the digital twin model of the target building, the spatial unit to which each target energy-consuming device belongs is determined, and the device operation status parameters corresponding to each target energy-consuming device in the current operating state are obtained; Specifically, in the digital twin model of the target building, it is first necessary to clarify the specific affiliation of each target energy-consuming device within the building's spatial structure, that is, to determine its corresponding spatial unit. For example, a particular air conditioning unit is associated with the specific office or area it serves. After establishing the correspondence between devices and spatial units, the system extracts the device's operating status parameters from the model, reflecting the device's real-time operating status. These parameters directly reflect the device's instantaneous working condition, such as the air conditioning's set temperature, operating mode, and compressor frequency, or the on / off status and brightness level of lighting equipment, providing basic input data for subsequent energy consumption simulation calculations.

[0045] Obtain the environmental state parameters and space usage state parameters corresponding to the space unit in its current operating state, and establish the correspondence between the target energy-consuming device and the environmental state parameters and space usage state parameters; Specifically, to more accurately simulate energy consumption, dynamic data related to the spatial unit to which the equipment belongs is further acquired, including environmental state parameters and spatial usage state parameters. Environmental state parameters characterize the physical environmental conditions within the space, such as temperature, humidity, and light intensity, while spatial usage state parameters reflect the characteristics of human activity in the area, such as the number of people and the frequency of entry and exit. By establishing the correspondence between the target energy-consuming equipment and these two types of parameters, the equipment operating status, environmental conditions, and human activities are linked to form a complete analysis unit, providing a basis for subsequent correction of equipment energy consumption based on environmental and usage status.

[0046] Based on the environmental state parameters, the operating conditions of the target energy-consuming equipment are corrected to determine the equipment operating load correction parameters under the influence of the environmental state. Specifically, environmental condition parameters directly impact the operating load of equipment. For example, environmental factors such as indoor-outdoor temperature differences and solar radiation intensity significantly alter the cooling or heating load of air conditioning equipment. Based on the acquired environmental condition parameters, the system corrects the operating conditions of target energy-consuming equipment using a preset algorithm or model, quantifying the degree of influence of environmental factors on equipment energy consumption, thereby determining the equipment operating load correction parameter under a given environmental condition. This parameter is used to adjust the theoretical operating load of the equipment, ensuring that energy consumption calculations reflect the impact of actual environmental changes and improving the accuracy of energy consumption prediction.

[0047] Based on the space usage status parameters, the operating intensity or operating time of the target energy-consuming equipment is corrected, and the equipment operation demand correction parameters under the influence of space usage status are determined. Specifically, space usage parameters reflect the demands of human activity on equipment operation. For example, unoccupied areas can reduce equipment operating intensity, while high-density areas require increased equipment operation to meet demand. Based on these space usage parameters, the system adjusts the operating intensity or duration of target energy-consuming equipment. For instance, it adjusts the airflow of the fresh air system according to the number of people, or the operating duration of lighting equipment according to the duration of human activity. Through this adjustment, the system determines a parameter for correcting equipment operating demands under a given space usage state. This parameter quantifies the driving effect of human activity on equipment energy consumption, making the energy consumption analysis results closer to the actual usage of the building and providing support for developing refined control strategies.

[0048] Based on the equipment operating status parameters, the equipment operating load correction parameters, and the equipment operating demand correction parameters, the target operating status parameters of each target energy-consuming device are determined within the preset time period. Specifically, after acquiring the equipment's basic operating status, environmental load corrections, and personnel demand corrections, the system needs to integrate these factors to determine the equipment's final operating status within a preset time period. This process typically involves parameter fusion or superposition calculations. For example, the equipment operating status parameters might provide the initial set power of the air conditioner, the equipment operating load correction parameters might calculate a power increment based on the indoor-outdoor temperature difference, and the equipment operating demand correction parameters might calculate another power increment based on the number of personnel. By weighting and superimposing these parameters or using a comprehensive calculation model, the system ultimately determines a target operating status parameter that better reflects actual operating conditions. This target parameter is no longer a theoretical value of the equipment under ideal conditions, but rather a predicted value that more closely approximates the real operating scenario after comprehensively considering environmental load and personnel demand, providing accurate input for subsequent energy consumption calculations.

[0049] According to the preset time step, the target operating state parameters are expanded in a time-division manner, and the equipment energy consumption within each time step is calculated based on the preset energy consumption calculation model. Specifically, to simulate the energy consumption changes of equipment within a preset time period, the system needs to incorporate the time dimension into its calculations. First, the system divides the entire time period into several time segments according to preset time steps (e.g., every 15 minutes or hour). Then, the system performs time-sharing decomposition on the determined target operating state parameters. This means the system predicts or simulates how the equipment's operating state parameters change within each time step. For example, between 9:00 AM and 10:00 AM, when population density is high, the target operating state parameters of the air conditioner may be higher; while between 12:00 PM and 1:00 PM, when people are out for lunch, the parameters may decrease. Next, based on a preset energy consumption calculation model, the system calculates the equipment energy consumption for each time step, considering the target operating state parameters. This energy consumption calculation model can be based on a simple product of equipment power and operating time, or it can be a more complex nonlinear model that considers the relationship between equipment efficiency curves, load rate, and energy consumption. Through this time-sharing calculation, the system can capture the dynamic characteristics of equipment energy consumption changes over time, rather than simply obtaining a static total.

[0050] The energy consumption of the equipment calculated within each time step is accumulated to obtain the energy consumption data of each target energy-consuming equipment within the preset time period.

[0051] Specifically, after completing the energy consumption calculations for all time steps, the system needs to summarize the results. This process involves adding up the device energy consumption data calculated within all time steps. For example, if the preset time period is one day and the time step is one hour, the system will add up the energy consumption values ​​calculated within 24 time steps. Through this accumulation operation, the system finally obtains the device energy consumption data corresponding to each target energy-consuming device within a preset time period (such as one day, one week, or one month). This data is obtained through refined simulation and calculation. It not only reflects the energy consumption of the device at a specific moment but also accurately predicts the total energy consumption level of the device throughout the entire period, considering the impact of environmental changes and human activities. This provides a reliable data foundation for subsequent energy consumption analysis, cost accounting, and the formulation of control strategies.

[0052] Preferably, please refer to Figure 3 The step of determining a preset building operation control target based on the building energy consumption analysis results, and determining the constraints and adjustable parameters corresponding to the building operation control target based on the target building digital twin model, and generating a control strategy based on the constraints and adjustable parameters includes: Based on the building energy consumption analysis results, the overall energy consumption level of the building under the current operating state, the energy consumption distribution of each spatial unit, and the equipment energy consumption data of each target energy-consuming device are obtained. Specifically, the system first extracts key data from the building energy consumption analysis results. This data forms the basis for decision-making in formulating control strategies. The overall energy consumption level reflects the building's current total energy consumption, used to determine whether it exceeds the budget or if there is room for energy saving. The energy consumption distribution of each spatial unit reveals the allocation of energy consumption in different areas of the building (such as different floors or functional areas), helping to identify energy hotspots or uneven load distribution. The equipment energy consumption data for each target energy-consuming device provides more granular information, clarifying which specific devices are the main energy contributors. By acquiring these three types of data, the system can comprehensively and accurately diagnose the building's current energy consumption status, providing data support for setting subsequent control targets.

[0053] Based on the overall energy consumption level, the energy consumption distribution, and the equipment energy consumption data, at least one building operation control objective is determined. The building operation control objective includes at least one of the following: energy consumption reduction objective, energy consumption balance objective, and operation comfort maintenance objective. Specifically, after diagnosing energy consumption, the system needs to set clear operational control objectives. If the overall energy consumption level is too high, the system will set an energy consumption reduction target to control the total energy consumption within a reasonable range. If the energy consumption distribution among different spatial units is uneven, for example, some areas have excessively high energy consumption while others have excessively low energy consumption, the system will set an energy consumption balancing target to optimize energy allocation. Simultaneously, the system also needs to consider operational comfort maintenance objectives to ensure that while saving energy, it maintains the comfort of the indoor environment, such as keeping temperature and humidity within suitable ranges. These objectives can be single objectives or combinations of multiple objectives, collectively guiding the generation of subsequent control strategies.

[0054] Based on the target building digital twin model, obtain the equipment operation status parameters, environmental status parameters, and space usage status parameters related to the building operation control target; Specifically, to formulate effective control strategies, the system needs to obtain state parameters related to the established control objectives from the target building's digital twin model. For example, if the objective is to reduce energy consumption, the system will obtain the operating state parameters of high-energy-consuming equipment; if the objective is to balance energy consumption, the system will obtain the environmental state parameters and equipment operating state parameters of different spatial units; if the objective is to maintain comfort, the system will obtain the environmental state parameters and space usage state parameters of key areas. These parameters provide specific operational objects and a data foundation for subsequent constraint determination and parameter adjustment.

[0055] Based on the pre-set operational constraints, spatial structure constraints, and environmental adaptation constraints of each device in the target building digital twin model, determine the constraint conditions corresponding to the building operation control target; Specifically, a series of safety, functional, and comfort constraints must be adhered to when adjusting the operating status of equipment. The system determines these constraints based on pre-set constraints in the target building's digital twin model. Operating constraints for each device include maximum and minimum power, start / stop frequency limits, and safe operating ranges, ensuring that the equipment is not damaged or poses a safety hazard during adjustment. Spatial structural constraints consider the building's physical layout, such as the connection relationships of ventilation ducts and the service range of equipment, ensuring that the adjustment strategy is spatially feasible. Environmental adaptation constraints specify the comfort range that the indoor environment must maintain, such as upper and lower limits for temperature and humidity, ensuring that the control strategy does not sacrifice user comfort. These constraints collectively constitute the boundary conditions that must be followed when generating the control strategy.

[0056] Under the premise of satisfying the constraints, adjustable parameters are determined from the equipment operating status parameters, and the adjustable parameters include at least one of equipment operating power, operating period or operating priority; Specifically, after defining the constraints, the system needs to identify which equipment parameters are adjustable to achieve the control objectives. These adjustable parameters are the direct targets of the control strategy. Equipment operating power is the most direct adjustment method, such as reducing the cooling power of an air conditioner or dimming lighting. Operating time adjustments involve changing the start-up and shutdown times of equipment, such as operating high-energy-consuming equipment during off-peak hours or controlling equipment in shifts based on personnel activity. Operating priority adjustments are used to determine the order of start-up, shutdown, or power adjustments when multiple devices are competing for resources. When determining these adjustable parameters, the system must ensure that any adjustment scheme is within the aforementioned constraints to guarantee the safety and feasibility of the adjustment.

[0057] Based on the building operation control objectives, the constraints, and the adjustable parameters, a control strategy for adjusting the operating status of building energy-consuming equipment is generated.

[0058] Specifically, finally, by integrating the building operation control objectives, constraints, and adjustable parameters, a specific control strategy is generated. This process typically involves optimization algorithms or rule engines. The system explores different combinations of adjustable parameters, evaluates the contribution of each combination to the control objectives, and checks whether it satisfies all constraints. For example, to reduce energy consumption, the system might generate a strategy suggesting reducing air conditioning power in sparsely populated areas and adjusting the operating times of high-energy-consuming equipment to avoid peak electricity usage. The final generated control strategy is a series of specific instructions or suggestions used to guide the adjustment of the operating status of building energy-consuming equipment, thereby achieving the predetermined goals of energy reduction, balance, or comfort maintenance.

[0059] Preferably, the step of generating a control strategy for adjusting the operating state of building energy-consuming equipment based on the building operation control objective, the constraints, and the adjustable parameters includes: Based on the building operation control objectives, control evaluation indicators corresponding to the building operation control objectives are determined. The control evaluation indicators are used to characterize the energy consumption control effect and the rationality of the operation status. Specifically, before generating control strategies, it's necessary to clarify how to measure the effectiveness of a strategy. To this end, specific control evaluation indicators are first set based on building operation control objectives (such as reducing energy consumption, balancing load, and maintaining comfort). These indicators are quantitative standards used to evaluate the effectiveness and rationality of the control strategies. For example, the effectiveness of energy consumption control can be characterized by indicators such as the percentage reduction in total energy consumption and energy consumption per unit area; the rationality of operational status may include the degree to which indoor temperature deviates from the set value, whether the equipment operating load rate is within a safe range, and the differences in energy consumption between different areas. By setting these evaluation indicators, the system provides clear criteria for subsequent strategy selection and optimization.

[0060] Under the premise of satisfying the constraints, construct the parameter combination space of the adjustable parameters to obtain multiple candidate operating parameter combinations; Specifically, when formulating a strategy, the system does not consider only a single parameter, but explores all possible combinations of adjustable parameters. Under the premise of satisfying equipment operation constraints, spatial structure constraints, and environmental adaptation constraints, the system constructs a parameter combination space. For example, adjustable parameters might include the power of air conditioner A, the power of air conditioner B, and the brightness of lighting C. The system generates all possible combinations of these parameters under different values, forming multiple candidate operating parameter combinations. Each candidate combination represents a possible equipment operating state adjustment scheme, and these schemes collectively constitute the scope of the strategy search.

[0061] Based on the digital twin model of the target building, simulation calculations are performed on each of the candidate operating parameter combinations to obtain the corresponding energy consumption prediction results and operating status prediction results; Specifically, to evaluate the merits of each candidate combination of operating parameters, simulation calculations are performed using a digital twin model of the target building. Each candidate combination (i.e., a set of equipment operating parameters) is input into the digital twin model, simulating the building's operation under these parameters. Through simulation, the system can predict the energy consumption results (such as total energy consumption and energy consumption of each device) and operating status results (such as temperature in each area and equipment load rate) of each candidate combination over a future period. This simulation calculation based on the digital twin model allows the system to pre-evaluate the effectiveness of control strategies before implementation, avoiding the risks and inconveniences of trial and error directly in the actual system.

[0062] Based on the energy consumption prediction results, the operating status prediction results, and the control evaluation indicators, each candidate operating parameter combination is evaluated to obtain each evaluation result; Specifically, after obtaining the simulation prediction results for all candidate combinations of operating parameters, the system needs to evaluate these combinations. It uses the control evaluation indicators determined in the first step to quantitatively score the energy consumption prediction results and operating state prediction results for each candidate combination. For example, the system calculates the total energy consumption reduction and comfort deviation values ​​for each combination. Through this evaluation, the system can transform the simulation results of each candidate combination into one or more comparable evaluation results, thereby providing data support for selecting the optimal strategy.

[0063] The target operating parameter combination is determined from the candidate operating parameter combinations whose evaluation results satisfy the building operation control objectives; Specifically, based on the evaluation results, the system selects the combination that meets the building's operational control objectives from all candidate combinations of operating parameters. For example, if the objective is to reduce energy consumption, the system will choose the combination with the lowest predicted energy consumption; if the objective is multi-objective optimization, the system may choose the combination that performs best in terms of multiple indicators such as energy consumption, comfort, and balance. This selected combination is the target operating parameter combination, which represents the equipment operating parameter setting scheme that can most effectively achieve the control objectives under the current conditions.

[0064] Based on the target combination of operating parameters, a control strategy is generated to adjust the operating status of the building's energy-consuming equipment.

[0065] Specifically, the determined combination of target operating parameters is transformed into specific, executable control instructions, i.e., a control strategy is generated. This strategy includes the specific equipment to be adjusted, the parameters to be adjusted (such as power, time period, and priority), and the target value for adjustment. For example, a control strategy might include specific instructions such as "reduce the air conditioning power in area A to 70%" or "turn off the lighting in area B during non-working hours." By executing these instructions, the operating status of building energy-consuming equipment can be intelligently adjusted, thereby achieving the preset building operation control goals.

[0066] Example 2 In addition, combined Figure 1 The intelligent building energy consumption monitoring data management method integrating digital twins described in Embodiment 1 of the present invention can be implemented by an intelligent building energy consumption monitoring data management system integrating digital twins. Figure 4 The diagram shows a hardware structure of the intelligent building energy consumption monitoring data management system integrating digital twins provided in Embodiment 3 of the present invention.

[0067] A smart building energy consumption monitoring and data management system that integrates digital twins can include a processor and a memory storing computer program instructions.

[0068] Specifically, the processor may include a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits that can be configured to implement embodiments of the present invention.

[0069] The memory may include a large-capacity storage device for data or instructions. For example, and not limitingly, the memory may include a hard disk drive (HDD), a floppy disk drive, flash memory, an optical disk drive, a magneto-optical disk drive, magnetic tape, or a Universal Serial Bus (USB) drive, or a combination of two or more of these. Where appropriate, the memory may include removable or non-removable (or fixed) media. Where appropriate, the memory may be internal or external to a data processing device. In a particular embodiment, the memory is a non-volatile solid-state memory. In a particular embodiment, the memory includes a read-only memory (ROM). Where appropriate, the ROM may be a mask-programmed ROM, a programmable ROM (PROM), an erasable PROM (EPROM), an electrically erasable PROM (EEPROM), an electrically rewritable ROM (EAROM), or flash memory, or a combination of two or more of these.

[0070] The processor reads and executes computer program instructions stored in the memory to implement any of the above embodiments of the intelligent building energy consumption monitoring data management method that integrates digital twins.

[0071] In one example, the intelligent building energy consumption monitoring and data management system integrating digital twins may also include communication interfaces and buses. For example, Figure 4 As shown, the processor, memory, and communication interface are connected via a bus and communicate with each other.

[0072] The communication interface is mainly used to enable communication between various modules, devices, units and / or equipment in the embodiments of the present invention.

[0073] A bus, including hardware, software, or both, couples components of the device together. For example, and not limitingly, a bus may include an Accelerated Graphics Port (AGP) or other graphics bus, an Enhanced Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), HyperTransport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an Infinite Bandwidth Interconnect, a Low Pin Count (LPC) bus, a memory bus, a Microchannel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local (VLB) bus, or other suitable buses, or combinations of two or more of these. Where appropriate, a bus may include one or more buses. While specific buses are described and illustrated in embodiments of the invention, the invention contemplates any suitable bus or interconnect.

[0074] In summary, the embodiments of the present invention provide a method and system for intelligent building energy consumption monitoring data management that integrates digital twins.

[0075] It should be clarified that the present invention is not limited to the specific configurations and processes described above and shown in the figures. For the sake of brevity, detailed descriptions of known methods are omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of the present invention is not limited to the specific steps described and shown. Those skilled in the art can make various changes, modifications, and additions, or change the order of steps, after understanding the spirit of the present invention.

[0076] The functional blocks shown in the above-described structural diagram can be implemented as hardware, software, firmware, or a combination thereof. When implemented in hardware, they can be, for example, electronic circuits, application-specific integrated circuits (ASICs), appropriate firmware, plug-ins, function cards, etc. When implemented in software, the elements of this invention are programs or code segments used to perform the required tasks. The programs or code segments can be stored on a machine-readable medium or transmitted over a transmission medium or communication link via data signals carried in a carrier wave. "Machine-readable medium" can include any medium capable of storing or transmitting information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, fiber optic media, radio frequency (RF) links, etc. Code segments can be downloaded via computer networks such as the Internet, intranets, etc.

[0077] The user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties. Furthermore, the collection, use and processing of the relevant data shall comply with the relevant laws, regulations and standards of the relevant locality, and corresponding operation entry points shall be provided for the user to choose to authorize or refuse.

[0078] It should also be noted that the exemplary embodiments mentioned in this invention describe methods or systems based on a series of steps or apparatus. However, this invention is not limited to the order of the steps described above; that is, the steps can be performed in the order mentioned in the embodiments, or in a different order, or several steps can be performed simultaneously.

[0079] The above description is merely a specific embodiment of the present invention. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, modules, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here. It should be understood that the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present invention, and these modifications or substitutions should all be covered within the protection scope of the present invention.

Claims

1. A method for managing energy consumption monitoring data of a smart building with digital twinning, characterized in that, The method comprises: According to the building structure information and the building energy consumption equipment information, the space structure state and the energy consumption equipment operation state of the building are modeled to obtain an initial building digital twin model; Real-time monitoring data is obtained, and the real-time monitoring data is mapped into the initial building digital twin model to update the initial building digital twin model to obtain an updated target building digital twin model, wherein the real-time monitoring data comprises real-time environmental data, real-time equipment operation data and real-time building personnel access data; Based on the target building digital twin model, the energy consumption of the building under the current operation state is simulated and analyzed to obtain a building energy consumption analysis result; According to the building energy consumption analysis result, a preset building operation control target is determined, and based on the target building digital twin model, a constraint condition and an adjustable parameter corresponding to the building operation control target are determined, and a control strategy is generated according to the constraint condition and the adjustable parameter; According to the control strategy, the operation parameters of the building energy consumption equipment are adjusted to realize intelligent control of the building energy consumption. 2.The method of claim 1, wherein, According to the building structure information and the building energy consumption equipment information, the space structure state and the energy consumption equipment operation state of the building are modeled to obtain an initial building digital twin model, comprising: According to the building structure information, the floor structure, the functional area and the space topological relationship of the building are modeled to obtain a building space structure model; According to the building energy consumption equipment information, the equipment type, the operation parameter and the energy consumption characteristic of each energy consumption equipment in the building are modeled to obtain an energy consumption equipment operation model; According to the building space structure model and the energy consumption equipment operation model, an association relationship between the energy consumption equipment and the building space structure is established; Based on the building space structure model, the energy consumption equipment operation model and the association relationship, an initial building digital twin model is constructed. 3.The method of claim 1, wherein, The real-time monitoring data is obtained, and the real-time monitoring data is mapped into the initial building digital twin model to update the initial building digital twin model to obtain an updated target building digital twin model, comprising: The real-time environmental data, the real-time equipment operation data and the real-time building personnel access data are collected respectively to obtain the real-time monitoring data; The real-time monitoring data is preprocessed to obtain target environmental data, target equipment operation data and target building personnel access data, wherein the preprocessing comprises timestamp calibration and spatial position identification of the real-time environmental data, the real-time equipment operation data and the real-time building personnel access data respectively, so as to realize consistency of the real-time monitoring data in time dimension and space dimension; The target environmental data, the target equipment operation data and the target building personnel access data are respectively mapped into the initial building digital twin model to update the initial building digital twin model to obtain the target building digital twin model. 4.The method of claim 3, wherein, The real-time monitoring data is preprocessed to obtain target environmental data, target equipment operation data and target building personnel access data, comprising: Timestamp calibration and alignment processing is performed on the real-time monitoring data to unify the time reference of real-time monitoring data from different sources, and time-aligned monitoring data is obtained. A corresponding spatial position identifier is configured for the time-aligned monitoring data, and the time-aligned monitoring data is mapped to a corresponding spatial unit under a unified building space reference system according to the spatial position identifier, so that real-time monitoring data from different sources has a consistent spatial reference relationship in the spatial dimension, and spatio-temporally aligned monitoring data is obtained. Based on the spatio-temporally aligned monitoring data, a set of associated monitoring data of the monitoring data at the same time point, adjacent time points and adjacent spatial units is determined. According to the set of associated monitoring data, the spatio-temporally aligned monitoring data is subjected to outlier detection, abnormal monitoring data whose numerical deviation between the spatio-temporally aligned monitoring data and the associated monitoring data exceeds a preset abnormality determination threshold is identified, and the abnormal monitoring data is subjected to elimination processing, and monitoring data with data loss is obtained. Based on the spatial position identifier, spatial units adjacent to the spatial unit to which the monitoring data with data loss belongs are determined, and the monitoring data with data loss is subjected to completion processing in combination with the monitoring data collected by the adjacent spatial units at the same time point or adjacent time points, and completion data is obtained. According to the completion data, environmental state parameters, equipment operation state parameters and space use state parameters are extracted respectively, and the target environmental data, target equipment operation data and target building personnel access data are determined. 5.The method of claim 1, wherein, Based on the target building digital twin model, the energy consumption of the building under the current running state is simulated and analyzed, and the building energy consumption analysis result is obtained, including: Based on the target building digital twin model, the spatial structure state, equipment operation state parameter, environmental state parameter and space use state parameter corresponding to the building at the current time are obtained. According to the spatial structure state and the equipment operation state parameter, the target energy consumption equipment participating in the energy consumption analysis under the current running state is determined. Based on the equipment operation state parameter of the target energy consumption equipment, and in combination with the environmental state parameter and the space use state parameter, the energy consumption of the target energy consumption equipment within a preset time period is simulated and calculated, and the equipment energy consumption data of each target energy consumption equipment is obtained. According to the equipment energy consumption data, and in combination with the spatial structure state, the equipment energy consumption data of each target energy consumption equipment is subjected to spatial dimension summarization and analysis, and the energy consumption analysis result of the building under the current running state is obtained. 6.The method of claim 5, wherein, Based on the equipment operation state parameter of the target energy consumption equipment, and in combination with the environmental state parameter and the space use state parameter, the energy consumption of the target energy consumption equipment within a preset time period is simulated and calculated, and the equipment energy consumption data of each target energy consumption equipment is obtained, including: Based on the target building digital twin model, the spatial unit to which each target energy consumption equipment belongs is determined, and the corresponding equipment operation state parameter of each target energy consumption equipment under the current running state is obtained. Obtaining the environmental state parameters and the space use state parameters corresponding to the space unit in the current running state, and establishing the corresponding relationship between the target energy consumption equipment and the environmental state parameters and the space use state parameters; According to the environmental state parameters, the running condition of the target energy consumption equipment is corrected, and the equipment running load correction parameter under the action of the environmental state is determined; According to the space use state parameters, the running intensity or running time of the target energy consumption equipment is corrected, and the equipment running demand correction parameter under the action of the space use state is determined; Based on the equipment running state parameters, the equipment running load correction parameters and the equipment running demand correction parameters, the target running state parameters of each target energy consumption equipment in the preset time period are determined; According to the preset time step, the target running state parameters are expanded in time, and based on the preset energy consumption calculation model, the equipment energy consumption in each time step is calculated; The equipment energy consumption calculated in each time step is accumulated to obtain the equipment energy consumption data of each target energy consumption equipment in the preset time period.

7. The fusion digital twin intelligent building energy consumption monitoring data management method according to any one of claims 1-6, characterized in that, The building energy consumption analysis result is used to determine a preset building operation control target, and based on the target building digital twin model, a constraint condition and an adjustable parameter corresponding to the building operation control target are determined, and a control strategy is generated according to the constraint condition and the adjustable parameter, which includes: Based on the building energy consumption analysis result, the overall energy consumption level of the building in the current running state, the energy consumption distribution of each space unit and the equipment energy consumption data of each target energy consumption equipment are obtained; According to the overall energy consumption level, the energy consumption distribution and the equipment energy consumption data, at least one building operation control target is determined, which includes at least one of the energy consumption reduction target, the energy consumption balance target and the running comfort maintenance target; Based on the target building digital twin model, the equipment running state parameters, the environmental state parameters and the space use state parameters related to the building operation control target are obtained; According to the running constraints, space structure constraints and environmental adaptation constraints of each device in the target building digital twin model, the constraint condition corresponding to the building operation control target is determined; Under the premise of meeting the constraint condition, the adjustable parameter is determined from the equipment running state parameter, which includes at least one of the equipment running power, the running period or the running priority; Based on the building operation control target, the constraint condition and the adjustable parameter, a control strategy for adjusting the running state of the building energy consumption equipment is generated. 8.The method of claim 7, wherein, Based on the building operation control target, the constraint condition and the adjustable parameter, a control strategy for adjusting the running state of the building energy consumption equipment is generated. Based on the building operation control target, the constraint condition and the adjustable parameter, a control strategy for adjusting the running state of the building energy consumption equipment is generated. Based on the building operation control target, the constraint condition and the adjustable parameter, a control strategy for adjusting the running state of the building energy consumption equipment is generated. Based on the building operation control target, the constraint condition and the adjustable parameter, a control strategy for adjusting the running state of the building energy consumption equipment is generated. perform simulation calculation on each of the candidate operation parameter combinations based on the target building digital twin model to obtain corresponding energy consumption prediction results and operation state prediction results; evaluate each of the candidate operation parameter combinations according to the energy consumption prediction results, the operation state prediction results and the control evaluation indexes to obtain evaluation results; determine a target operation parameter combination from the candidate operation parameter combinations whose evaluation results meet the building operation control target; generate a control strategy for adjusting the operation state of the building energy consumption equipment based on the target operation parameter combination.

9. A fusion digital twin intelligent building energy consumption monitoring data management system, characterized in that, comprise: at least one processor, at least one memory, and computer program instructions stored in the memory that, when executed by the processor, implement the method of any one of claims 1-8.

Images