Building intelligent energy consumption management system based on BIM technology

Abstract

The invention discloses a building intelligent energy consumption management system based on a BIM technology, and relates to the technical field of building energy consumption, and the system comprises a BIM virtual identifier generation module, an equipment positioning calibration module, a component energy consumption database module, a transformation simulation engine module, and a transformation verification feedback module. A full-link association mechanism with a virtual device identifier (VID) as a core is constructed, a unique digital identity is given to each device through a BIM virtual identifier generation module, dynamic binding of physical devices and a BIM model is achieved in combination with a multi-source positioning technology of a device positioning calibration module, the mechanism breaks through a static matching mode, and the dynamic binding of the physical devices and the BIM model is achieved. According to the method, the continuity of data association can still be kept under the scene of newly adding, shifting and transforming the equipment, so that the energy consumption data and the equipment entity form an inseparable corresponding relation, technical support is provided for component-level tracing of building energy consumption, and the long-standing problems of data islands and association failure are fundamentally solved.

Description

Technical Field

[0001] This invention relates to the field of building energy management technology, specifically to a smart building energy management system based on BIM technology. Background Technology

[0002] As a key area of ​​energy consumption, the construction industry has made refined and intelligent energy management a core requirement for its development. With the surge in the number of devices in modern buildings and frequent equipment upgrades, traditional energy management models that rely on manual recording and spreadsheet statistics are no longer sufficient to meet the requirements of real-time performance and accuracy. BIM technology, as a core tool for building lifecycle management, provides a new technological carrier for energy management with its three-dimensional visualization characteristics. However, how to deeply integrate the energy consumption data of physical equipment with the BIM model remains a key challenge that the industry has yet to solve.

[0003] Existing technologies rely heavily on manual correlation and simple coordinate comparison in matching physical sensor data with BIM models. When equipment locations change or are added, data mismatches are prone to occur, and precise mapping at the component level cannot be achieved. This leads to a break in the correspondence between energy consumption data and equipment entities. Furthermore, the energy consumption simulation process for existing building renovations requires inputting a large number of parameters and is time-consuming, resulting in a prolonged evaluation cycle for renovation plans. This makes it difficult to meet the needs of rapid decision-making, and the large deviation between simulation results and actual energy consumption affects the reliability of the plan.

[0004] In summary, current building energy management faces the dual challenges of inaccurate data mapping and inefficient simulation evaluation. There is an urgent need to build an integrated solution based on BIM technology, which can achieve accurate correlation of equipment energy consumption data and efficient evaluation of renovation plans through innovative identification mechanisms and simulation technology. This will break through the technical bottlenecks of traditional management models and promote the intelligent development of building energy management. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of existing technologies and provide a building intelligent energy consumption management system based on BIM technology. This system constructs a full-link association mechanism with Virtual Device Identifier (VID) as its core. Through the BIM virtual identifier generation module, each device is given a unique digital identity. Combined with the multi-source positioning technology of the device positioning calibration module, the system achieves dynamic binding between physical devices and BIM models. This mechanism breaks through the static matching mode and can maintain the continuity of data association even in scenarios of device addition, relocation, and modification. It makes energy consumption data and device entities form an inseparable correspondence, providing technical support for component-level traceability of building energy consumption and fundamentally solving the long-standing problems of data silos and association failures.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution: a building intelligent energy consumption management system based on BIM technology, the system comprising: a BIM virtual identifier generation module, an equipment positioning and calibration module, a component energy consumption database module, a renovation simulation engine module, and a renovation verification feedback module;

[0007] The BIM virtual identifier generation module is used to generate a unique digital virtual device identifier (VID) for all energy-consuming devices in the building based on the geometric topology and equipment attribute information of the BIM model.

[0008] The device positioning and calibration module uses RFID scanners, UWB positioning base stations and QR code identification to collect the location information of physical devices, and binds the real-time energy consumption data stream of physical devices to the corresponding VID through multi-source positioning technology and collaborative collection rules.

[0009] The component energy consumption database module stores real-time / historical energy consumption data of all bound VID devices in layers, and establishes a device energy consumption time series curve library according to VID, providing a data foundation for the pre-evaluation of the renovation plan;

[0010] The transformation simulation engine module calls the historical operating data of the specified VID in the component energy consumption database module, and combines it with the transformation component parameters added by the user interaction to calculate the energy consumption change after the transformation and generate a pre-assessment report including energy saving and investment payback period.

[0011] The modification verification feedback module automatically receives real-time energy consumption data of the modified equipment after the modification is implemented. It generates an error analysis by comparing the predicted value with the actual value in the pre-assessment report, and inputs the correction parameters back into the component energy consumption database module to calibrate historical data.

[0012] Furthermore, in the BIM virtual identifier generation module, VID is used to associate the geometric parameters, attribute information, and topological relationships of energy-consuming equipment, wherein:

[0013] The geometric parameters include dimensions and spatial coordinates;

[0014] The attribute information includes model and power;

[0015] The topology includes the associated loops and connected devices.

[0016] Furthermore, the generation rule for the VID is as follows:

[0017] Extract the geometric parameters and attribute information of energy-consuming equipment components in the BIM model as input;

[0018] Generate topology codes based on the topological relationships of the equipment within the building;

[0019] The geometric parameters, attribute information, and topology code are concatenated into a string, and a unique 128-bit virtual device identifier (VID) is generated using the SHA-256 hash algorithm.

[0020] When a new device is added, a VID is automatically generated for it and the topology relationship is inherited.

[0021] When a device is deleted, its VID is marked as invalid, while retaining the historical data association;

[0022] All VID-related spatial coordinates are based on the building origin coordinate system and are consistent with the coordinate matching reference of the equipment positioning calibration module.

[0023] After generating VIDs, the equipment components are highlighted in the BIM model, and a mapping table between VIDs and equipment parameters is output for user verification.

[0024] Furthermore, the RFID scanner, UWB positioning base station, and QR code recognition in the device positioning calibration module specifically include:

[0025] The RFID scanner is used to read the initial installation coordinates and device ID stored in the RFID tag of the physical device;

[0026] The UWB positioning base station is used to receive location signals sent by UWB tags on physical devices in real time and parse them into real-time coordinates.

[0027] The QR code recognition device is used to scan the QR code on the physical device to obtain the device's unique code and associated installation coordinates.

[0028] Furthermore, the multi-source positioning technology collaborative acquisition rules in the device positioning calibration module are specifically as follows:

[0029] The coordinates of physical devices obtained by RFID, UWB, and QR codes are converted into coordinate values ​​in the building origin coordinate system in real time.

[0030] Calculate the Euclidean distance between the coordinates of the physical equipment and the spatial coordinates associated with VID in the BIM model under the transformed building origin coordinate system;

[0031] When the distance is ≤0.1m, the mapping relationship between physical devices and corresponding VIDs is automatically established. The real-time energy consumption data stream collected by the physical device through the sensor is bound to the VID. The binding information includes the device's unique code, VID, binding time and coordinate deviation value. At the same time, the binding result is synchronized to the component energy consumption database module. The real-time energy consumption data stream includes voltage, current, power and cumulative energy consumption value.

[0032] When the distance is greater than 0.1m, the binding is paused and the location information is re-collected;

[0033] When adding new equipment during the renovation, VID binding is performed through QR code identification and RFID tag installation location. When the equipment is moved, its coordinates are dynamically updated and VID is rematched based on UWB positioning data.

[0034] Furthermore, the component energy consumption database module is used for hierarchical storage of various types of data associated with the Virtual Device Identifier (VID), specifically divided into a basic information layer, a real-time data stream layer, and a historical data layer;

[0035] The basic information layer stores the device geometric parameters, attribute information, and topology relationships corresponding to the VID;

[0036] The real-time data stream layer receives the real-time energy consumption data stream output by the device positioning and calibration module through the data interface;

[0037] The historical data layer aggregates and stores real-time energy consumption data according to daily and monthly time dimensions, and establishes a multi-dimensional index system with VID as the index key, including time index, device type index and topology relationship index. The time index is associated with the data collection timestamp, the device type index is associated with the type code in the device attribute information, and the topology relationship index is associated with the loop ID and the connected device VID. It supports quick querying of all associated data by VID and filtering energy consumption data corresponding to VID by device type and time range conditions.

[0038] The equipment energy consumption time-series curve library is based on the energy consumption data stored in the historical data layer. It generates equipment energy consumption time-series curves according to VID to provide historical operating data benchmarks for the transformation simulation engine module. The time-series curves include real-time fluctuation curves, daily load curves, and monthly trend curves.

[0039] Furthermore, the transformation simulation engine module, based on the Virtual Device Identifier (VID), retrieves historical operating data corresponding to a specified VID from the historical data layer of the component energy consumption database module. This includes real-time fluctuation curves, daily load curves, and monthly trend curves generated by the device energy consumption time series curve library. At the same time, it extracts the device geometric parameters, attribute information, and topological relationships associated with the VID from the basic information layer as the basic simulation data.

[0040] The system receives modification component parameters added by users through interaction with the BIM model processing module. These parameters include the new equipment model, rated power, and energy efficiency ratio of the equipment being replaced. The system then compares the modification component parameters with the equipment attribute information associated with the corresponding VID to generate a parameter difference table.

[0041] Based on retrieved historical operating data, basic information layer data, and parameter difference tables, and using historical energy consumption data as a baseline, the theoretical energy consumption value after the modification is calculated according to the changes in the modification parameters in the parameter difference table and the topological relationship of the equipment in the building. E all P is the historical average annual energy consumption of the equipment before the renovation, retrieved from the component energy consumption database module. x P is the rated power of the modified equipment. j η is the rated power of the equipment before the modification. j The energy efficiency ratio before the retrofit is the ratio of the effective output energy to the input energy of the equipment before the retrofit, η. x K represents the energy efficiency ratio after the upgrade, which is the ratio of the effective output energy to the input energy of the upgraded equipment. xs K is the topology influence coefficient, ranging from 0.9 to 1.1. It is calculated based on the load balance of the circuit to which the equipment belongs (1.0 when load fluctuation is ≤5%, and ±0.01 for every 1% exceeding 5%). sjxs It is a time correction factor with a value range of 0.98-1.02, determined based on the annual fluctuation trend of historical energy consumption data (1.02 when the average annual growth rate of energy consumption is greater than 2%, 0.98 when it is less than -2%, and 1.0 otherwise).

[0042] The theoretical energy consumption value E output nh Based on the user-input cost data of the modified components, the investment payback period formula is used. Calculate the payback period, where C zfy The total cost of the renovation is given by ΔE, where ΔE represents the energy saved and ΔE = E. all -E nh C dnjz S represents the value of a unit of electricity. wh The annual maintenance cost savings, which is the difference between the average annual maintenance cost before and after the renovation, will generate a pre-assessment report that includes the location of VID-associated devices, renovation parameters, energy consumption comparison, and investment payback period. The report will also be associated with the corresponding VID and synchronized to the interactive display module.

[0043] Furthermore, after the modification is implemented, the modification verification feedback module receives, in real time, the actual energy consumption data stream collected by sensors from the physical device associated with the new device VID bound to the device positioning and calibration module. The actual energy consumption data stream includes actual voltage, actual current, actual power, and actual cumulative energy consumption value E. sjnh The data is aggregated and stored in a temporary database according to daily and monthly time dimensions. At the same time, it is associated with the parameters of the new equipment, such as model, rated power, and energy efficiency ratio, to calculate the actual energy saving ΔE. sj =E all -E sjnh The system retrieves the energy saving ΔE from the corresponding VID pre-assessment report in the component energy consumption database module and compares it with the actual energy consumption data ΔE. sj Comparative analysis was conducted, and the energy saving error Δδ=|ΔE was calculated. sj-ΔE|×100%, when Δδ>5%, the new equipment VID, the comparison curve of actual energy consumption and theoretical energy consumption, and the energy saving error are input in reverse into the component energy consumption database module to calibrate the historical energy consumption data associated with the VID and the benchmark parameters for subsequent simulation calculations.

[0044] Compared with existing technologies, this BIM-based intelligent building energy management system has the following advantages:

[0045] I. This invention constructs a full-link association mechanism centered on Virtual Device Identifier (VID). By assigning each device a unique digital identity through the BIM virtual identifier generation module and combining it with the multi-source positioning technology of the device positioning calibration module, it achieves dynamic binding between physical devices and BIM models. This mechanism breaks through the static matching mode and can maintain the continuity of data association even in scenarios of device addition, relocation, and modification. It enables energy consumption data to form an inseparable correspondence with device entities, providing technical support for component-level traceability of building energy consumption and fundamentally solving the long-standing problems of data silos and association failures.

[0046] Second, this invention combines the lightweight calculation of the retrofit simulation engine module with the actual data calibration of the retrofit verification feedback module. By driving the dynamic correction of simulation parameters through historical energy consumption data and real-time data streams, the energy consumption prediction of the retrofit scheme no longer relies on empirical values, but generates accurate results based on the actual operating status of the building. At the same time, the feedback mechanism transforms the actual energy consumption deviation into the basis for model optimization, forming a self-iteratory technical system. This upgrades building energy consumption management from passive statistics to proactive prediction and dynamic optimization, providing an intelligent technical path for energy-saving retrofit of existing buildings.

[0047] Other advantages, objectives and features of the invention will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from the following examination or study, or may be learned from the practice of the invention. Attached Figure Description

[0048] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0049] Figure 1 This is a flowchart illustrating the operation of a BIM-based intelligent building energy management system.

[0050] Figure 2This is a diagram showing the modular components of a BIM-based intelligent building energy management system. Detailed Implementation

[0051] The technical solutions in the embodiments of the present invention will be clearly and completely described below. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0052] Example 1

[0053] This embodiment uses a commercial complex as an application scenario to explain in detail the working principle and module composition of a BIM-based intelligent building energy management system, such as... Figure 2 As shown, the system generates unique virtual device identifiers (VIDs) for all energy-consuming devices within a building through a BIM virtual identifier generation module. Combined with a device positioning and calibration module, it achieves dynamic binding between physical devices and VIDs. Relying on a component energy consumption database module, it stores energy consumption data in layers. Through a renovation simulation engine module, it generates a renovation pre-assessment report. And using a renovation verification feedback module, it completes data calibration and optimization. The entire process incorporates the association role of VIDs, integrates multi-source positioning technology and feedback mechanisms, and achieves precise management of building energy consumption and scientific evaluation of renovation plans.

[0054] The BIM virtual identifier generation module generates a unique virtual device identifier (VID) for each energy-consuming device within the building, realizing the digital association between physical devices and the BIM model. This is achieved by extracting the geometric parameters, attribute information, and topological relationships of all energy-consuming devices in the model. The geometric parameters include the device's dimensions and spatial coordinates (three-dimensional coordinates based on the building's origin coordinate system set for the commercial complex). The attribute information includes the device's model, rated power, manufacturer, installation date, and other attribute identifiers. The topological relationships refer to the device's connection relationships within the building's energy system, such as its power supply circuit and upstream / downstream connections, reflecting the device's functional positioning in the energy network. After extraction, the data is validated, duplicate or erroneous information is removed, and a topological code is generated based on the device's hierarchical relationship within the energy system. This process is repeated after obtaining the geometric parameters, attribute information, and... After topology coding, the three types of information are converted into string format and encrypted to obtain a unique VID. For newly added equipment, the system automatically detects new equipment components in the BIM model, extracts their parameters, inherits the topological relationship of their location, and generates a new VID. For removed equipment, its VID is marked as "invalid," but the association between the VID and historical energy consumption data is retained to ensure the integrity of data traceability. The spatial coordinates associated with all VIDs follow the building origin coordinate system and are consistent with the coordinate reference of the subsequent equipment positioning and calibration module to avoid positioning deviations caused by differences in coordinate systems. After generating the VID, the module will automatically highlight the corresponding equipment in the BIM model and output a "VID and Equipment Parameter Mapping Table," which contains VID, equipment name, geometric parameters, attribute information, topology coding, etc., for management personnel to verify.

[0055] The device positioning and calibration module collects real-time location information of physical devices using multi-source positioning technology, matches it with the spatial coordinates associated with VIDs in the BIM model, and completes the binding of physical devices with VIDs. This ensures that real-time energy consumption data streams are accurately associated with the corresponding devices. The application of this module in commercial complexes requires the deployment of RFID scanners, UWB positioning base stations, and QR code recognition devices to form a comprehensive positioning network. Specifically, the RFID scanners are deployed at key locations such as entrances and exits on each floor of the commercial complex and equipment rooms to read the initial installation coordinates and device ID stored in the RFID tags on the surface of physical devices. When the equipment is installed for the first time, construction personnel will... Coordinates are written to RFID tags, and RFID scanners can read this information non-contactly via radio frequency signals. The UWB positioning base station uses ultra-wideband wireless communication technology to receive position signals sent by UWB tags on the physical device and analyzes the device's dynamic coordinates in real time. The QR code recognition device affixes a QR code containing the device's unique code and installation coordinates to the device surface. Managers can scan the QR code using a handheld terminal or a fixed scanning device to obtain the device's static basic information. These three types of devices work together to collect the device's initial coordinates, real-time dynamic coordinates, and static code information, providing multi-dimensional data support for positioning calibration. The module processes the data according to preset collaborative acquisition rules. The location data specifically involves converting the coordinates of physical devices collected by RFID, UWB, and QR codes into coordinates within the building origin coordinate system of the commercial complex. This eliminates the coordinate system differences between different positioning devices. Under this unified coordinate system, the Euclidean distance between the real-time coordinates of the physical device and the spatial coordinates associated with the VID in the BIM model is calculated. When the calculated distance d ≤ 0.1 meters, the module determines that the physical device matches the VID, automatically establishes a mapping relationship, and binds the real-time energy consumption data stream (including voltage, current, power, and cumulative energy consumption value) collected by the physical device through sensors to the VID. The binding information records the device's unique code, VID, binding time, and coordinate deviation value. (i.e., the difference between physical coordinates and VID coordinates), and synchronized to the component energy consumption database module. When the distance d>0.1 meters, the module determines that the positioning data is abnormal, suspends the binding and triggers the re-collection process. For newly added equipment during the renovation, the installation coordinates are obtained by scanning its QR code, and the equipment information in the RFID tag is read. A new VID is automatically generated for it and the topology relationship of the loop is inherited to complete the binding. When the equipment is moved, the UWB positioning base station will capture its coordinate changes in real time. The module recalculates the Euclidean distance with the VID based on the new coordinates. If they match, the binding relationship is updated to ensure that the energy consumption data is always associated with the correct equipment and to ensure the continuity and accuracy of the energy consumption data.

[0056] The aforementioned component energy consumption database module is responsible for hierarchically storing real-time and historical energy consumption data of all bound VID devices and establishing a multi-dimensional index system to provide a data foundation for transformation simulation. In commercial complexes, this module ensures rapid data retrieval and access through structured storage and efficient index design. The data is divided into three levels: the basic information layer stores static data of VID-associated devices, including geometric parameters (size, coordinates), attribute information (model, power), and topological relationships (corresponding circuit, connected devices); the real-time data stream layer receives real-time energy consumption data streams transmitted by the device positioning and calibration module through a data interface, including electrical... The module provides dynamic data such as voltage, current, power, and cumulative energy consumption. The historical data layer aggregates real-time data by daily and monthly time dimensions. Historical data uses VID as the index key, establishing three types of indexes: a time index (linking to the data collection timestamp, supporting queries by time range), a device type index (linking to the type code in device attribute information, supporting data filtering by device type), and a topology index (linking to the circuit ID of the device and the VID of the connected device, supporting queries by circuit). This layered storage achieves structured data management, ensuring both efficient processing of real-time data and long-term archiving and reuse of historical data. Based on the aggregated data from the historical data layer, the module generates three types of energy consumption time-series curves by VID: a real-time fluctuation curve (recording instantaneous changes in device power in minutes, reflecting the real-time operating status of the device), a daily load curve (statistically displaying the daily energy consumption distribution, showing the load change patterns of the device within a day), and a monthly trend curve (summarizing the total monthly energy consumption to form an annual energy consumption trend chart for analyzing seasonal energy consumption fluctuations). These curves are stored in visual chart form and can be quickly retrieved via VID, providing an intuitive historical operating data benchmark for the simulation engine module.

[0057] The aforementioned retrofit simulation engine module, based on historical data from the component energy consumption database and combined with user-input retrofit parameters, calculates post-retrofit energy consumption changes and investment payback period, generating a pre-assessment report. In commercial complex applications, this module can perform simulation analysis on equipment to be retrofitted, providing managers with a scientific basis for retrofit decisions. First, based on the user-specified VID of the retrofitted equipment, it retrieves its historical operating data from the historical data layer of the component energy consumption database, including real-time fluctuation curves, daily load curves, and monthly trend curves generated through a time-series curve library. Simultaneously, it extracts the geometric parameters, attribute information, and topological relationships associated with the VID from the basic information layer, serving as the foundational data for simulation calculations. Managers input retrofit component parameters through the system interface, such as the model, rated power, and energy efficiency ratio of the new equipment. After receiving these parameters, the module compares them with the attribute information of the original equipment under the VID, generating a "parameter difference table." This table clearly lists key differences such as power difference and energy efficiency ratio changes before and after the retrofit, providing a variable basis for energy consumption calculations. Based on historical energy consumption data and retrofit parameters, through… Calculate the theoretical energy consumption value E after the modification. nh , of which E all To retrieve the average annual energy consumption of the equipment before the renovation from the historical data layer, P x P is the rated power of the new equipment after the modification. j η is the rated power of the old equipment before the upgrade. j η is the energy efficiency ratio (the ratio of effective output energy to input energy) of the old equipment before the upgrade. x To determine the energy efficiency ratio of the new equipment after the upgrade, K xs The topology influence coefficient (value 0.9-1.1) is calculated based on the load balance of the circuit to which the equipment belongs. For example, if the circuit load fluctuation is ≤5%, it indicates a stable load, so a value of 1.0 is used. If the fluctuation exceeds 5%, an adjustment coefficient based on the direction of fluctuation is applied (1.1 for excessively high load and 0.9 for excessively low load). K sjxs The time correction factor (ranging from 0.98 to 1.02) is determined based on the annual fluctuation trend of historical energy consumption. For example, if the average annual energy consumption growth rate of a commercial complex over the past 5 years is >2%, it indicates an upward trend in energy consumption, and a value of 1.02 is used; if the average annual growth rate is <-2%, a value of 0.98 is used; otherwise, a value of 1.0 is used. Using this formula, the module can calculate the theoretical annual energy consumption E after the renovation. nh Therefore, the energy saving ΔE = E all -E nh Combined with the total renovation cost C input by the user zfy (Including equipment procurement costs, installation costs, etc.), unit electricity value C dnjz and annual maintenance cost savings S wh The payback period is calculated using the investment payback period formula, which is the difference between the average annual maintenance cost before and after the renovation. Finally, a pre-assessment report is generated, which includes the location of VID-related equipment in the BIM model, a table of differences in renovation parameters, energy consumption comparison, investment payback period, and economic analysis. After the report is generated, it is automatically linked to the corresponding VID and synchronized to the interactive display module for managers to view and make decisions.

[0058] After the renovation plan is implemented, the aforementioned renovation verification feedback module compares the actual energy consumption with the predicted values ​​in the pre-assessment report, generates error analysis, and corrects system data to improve the accuracy of subsequent simulations. In renovation projects of commercial complexes, this module can verify the renovation effect, promptly identify simulation deviations, and optimize model parameters. After the renovation is implemented, the new equipment will be assigned a new VID, and the equipment positioning and calibration module will bind the real-time energy consumption data stream of the new equipment to this VID. The renovation verification feedback module receives this actual energy consumption data through a data interface, including actual voltage, actual current, actual power, and actual cumulative energy consumption value E. sjnhThe module aggregates actual data by day and month, generating actual statistical indicators corresponding to the "theoretical energy consumption value" in the pre-assessment report. The module then calculates the actual energy savings ΔE. sj =E all -E sjnh Compare the energy savings ΔE in the pre-assessment report to calculate the error rate Δδ = |ΔE|. sj -ΔE|×100%, if Δδ≤5%, it means that the simulation results are in good agreement with the actual situation and no parameter correction is needed. If Δδ>5%, the module will generate a detailed error analysis report to analyze the cause of the deviation. The corrected parameters will be input back into the component energy consumption database module to calibrate the historical data associated with the VID, such as correcting the benchmark value of the historical energy consumption curve, so that the subsequent simulation calculations are more in line with the actual operating conditions. The corrected parameters will be synchronized to the renovation simulation engine module and become the calculation benchmark for the next renovation simulation.

[0059] This embodiment fully demonstrates the working principle of a building intelligent energy management system based on BIM technology through the application scenario of a commercial complex. It accurately and automatically maps the real-time collected energy consumption data stream to the virtual identifiers of the corresponding BIM components, realizing equipment-level energy consumption monitoring. The system quickly calculates key indicators such as energy consumption comparison before and after the renovation and investment payback period, and displays them in a visual way, greatly improving the efficiency of renovation plan decision-making.

[0060] Example 2

[0061] This embodiment provides the operational process for building monitoring and management in a BIM-based intelligent building energy consumption management system, such as... Figure 1 As shown, the specific steps of this process are as follows:

[0062] (1) Generation of digital identification for equipment

[0063] Import the building BIM model and extract the geometric parameters (dimensions / coordinates), attribute information (model / power), and topological relationships (circuit to which it belongs / connected device) of the energy-consuming equipment;

[0064] The device data is concatenated into a string and a unique 128-bit Virtual Device Identifier (VID) is generated using the SHA-256 algorithm;

[0065] Highlight VID devices in the BIM model and output a VID-device parameter mapping table for verification;

[0066] (2) Physical device positioning and binding

[0067] The initial installation coordinates of the device tag are read using an RFID scanner;

[0068] UWB positioning base stations are used to capture the device's displacement coordinates in real time;

[0069] Obtain the device's unique code through a QR code recognition device;

[0070] Convert the physical coordinates to the building origin coordinate system;

[0071] Calculate the Euclidean distance between the physical coordinates and the VID coordinates in the BIM model. When the distance is ≤0.1 meters: establish the binding relationship between the physical device and the VID.

[0072] Associate the sensor's real-time energy consumption data stream (voltage / current / power) with V ID and synchronously bind the information to the database;

[0073] (3) Layered storage of energy consumption data

[0074] Basic information layer: Stores the geometric parameters, device attributes, and topology relationships associated with the VID;

[0075] Real-time data stream layer: Receives and stores real-time energy consumption data uploaded by device sensors;

[0076] Historical data layer: Aggregates and stores energy consumption data by day / month, and establishes a three-dimensional index (time stamp / device type / topology relationship);

[0077] Generate equipment energy consumption time-series curves (real-time fluctuation curve / daily load curve / monthly trend curve);

[0078] (4) Simulation of energy-saving renovation scheme

[0079] Users select the device VID through the BIM interface and enter the new device parameters (model / power / energy efficiency ratio);

[0080] Retrieve the historical energy consumption data and time series curve for this VID;

[0081] Generate a difference table by comparing the parameters of the old and new equipment;

[0082] The theoretical energy consumption after the renovation is calculated based on historical data (considering topology / time correction factors);

[0083] Calculate the payback period based on the renovation costs;

[0084] Generate a preliminary assessment report including energy consumption comparisons;

[0085] (5) Verification and feedback of the transformation effect

[0086] After the modification, the new device is bound to the original VID;

[0087] Collect actual energy consumption data (voltage / current / cumulative energy consumption);

[0088] Aggregate and store data to a temporary database on a daily / monthly basis;

[0089] Calculate the actual energy saving and compare it with the pre-evaluated theoretical energy saving: when the error is >5%, reverse-calibrate the historical database;

[0090] Generate a curve comparing actual and theoretical energy consumption;

[0091] Update the baseline parameters for subsequent simulations;

[0092] (6) Dynamic monitoring and maintenance

[0093] When the device is moved: update the coordinates via UWB and rematch the VID;

[0094] When adding a new device: Generate a new VID via QR code / RFID and bind it;

[0095] Regularly generate multi-dimensional energy consumption analysis reports (by equipment type / time range / topology loop).

[0096] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

Claims

1. A building intelligent energy management system based on BIM technology, characterized in that, The system consists of: a BIM virtual identification generation module, an equipment positioning and calibration module, a component energy consumption database module, a renovation simulation engine module, and a renovation verification and feedback module. The BIM virtual identifier generation module is used to generate a unique digital virtual device identifier (VID) for all energy-consuming devices in the building based on the geometric topology and equipment attribute information of the BIM model. The device positioning and calibration module uses RFID scanners, UWB positioning base stations and QR code identification to collect the location information of physical devices, and binds the real-time energy consumption data stream of physical devices to the corresponding VID through multi-source positioning technology and collaborative collection rules. The component energy consumption database module stores real-time / historical energy consumption data of all bound VID devices in layers, and establishes a device energy consumption time series curve library according to VID, providing a data foundation for the pre-evaluation of the renovation plan; The transformation simulation engine module calls the historical operating data of the specified VID in the component energy consumption database module, and combines it with the transformation component parameters added by the user interaction to calculate the energy consumption change after the transformation and generate a pre-assessment report including energy saving and investment payback period. The modification verification feedback module automatically receives real-time energy consumption data of the modified equipment after the modification is implemented. It generates an error analysis by comparing the predicted value with the actual value in the pre-assessment report, and inputs the correction parameters back into the component energy consumption database module to calibrate historical data.

2. The building intelligent energy management system based on BIM technology according to claim 1, characterized in that, In the BIM virtual identifier generation module, VID is used to associate the geometric parameters, attribute information, and topological relationships of energy-consuming equipment, wherein: The geometric parameters include dimensions and spatial coordinates; The attribute information includes model and power; The topology includes the associated loops and connected devices.

3. The building intelligent energy management system based on BIM technology according to claim 2, characterized in that, The generation rules for the VID are as follows: Extract the geometric parameters and attribute information of energy-consuming equipment components in the BIM model as input; Generate topology codes based on the topological relationships of the equipment within the building; The geometric parameters, attribute information, and topology code are concatenated into a string, and a unique 128-bit virtual device identifier (VID) is generated using the SHA-256 hash algorithm. When a new device is added, a VID is automatically generated for it and the topology relationship is inherited. When a device is deleted, its VID is marked as invalid, while retaining the historical data association; All VID-related spatial coordinates are based on the building origin coordinate system and are consistent with the coordinate matching reference of the equipment positioning calibration module. After generating VIDs, the equipment components are highlighted in the BIM model, and a mapping table between VIDs and equipment parameters is output for user verification.

4. The building intelligent energy management system based on BIM technology according to claim 1, characterized in that, The RFID scanner, UWB positioning base station, and QR code recognition in the device positioning calibration module specifically include: The RFID scanner is used to read the initial installation coordinates and device ID stored in the RFID tag of the physical device; The UWB positioning base station is used to receive location signals sent by UWB tags on physical devices in real time and parse them into real-time coordinates. The QR code recognition device is used to scan the QR code on the physical device to obtain the device's unique code and associated installation coordinates.

5. The building intelligent energy management system based on BIM technology according to claim 1, characterized in that, The multi-source positioning technology collaborative acquisition rules in the device positioning calibration module are as follows: The coordinates of physical devices obtained by RFID, UWB, and QR codes are converted into coordinate values ​​in the building origin coordinate system in real time. Calculate the Euclidean distance between the coordinates of the physical equipment and the spatial coordinates associated with VID in the BIM model under the transformed building origin coordinate system; When the distance is ≤0.1m, the mapping relationship between physical devices and corresponding VIDs is automatically established. The real-time energy consumption data stream collected by the physical device through the sensor is bound to the VID. The binding information includes the device's unique code, VID, binding time and coordinate deviation value. At the same time, the binding result is synchronized to the component energy consumption database module. The real-time energy consumption data stream includes voltage, current, power and cumulative energy consumption value. When the distance is greater than 0.1m, the binding is paused and the location information is re-collected; When adding new equipment during the renovation, VID binding is performed through QR code identification and RFID tag installation location. When the equipment is moved, its coordinates are dynamically updated and VID is rematched based on UWB positioning data.

6. The building intelligent energy management system based on BIM technology according to claim 1, characterized in that, The component energy consumption database module is used to store various types of data associated with the virtual device identifier (VID) in a hierarchical manner, specifically divided into a basic information layer, a real-time data stream layer, and a historical data layer. The basic information layer stores the device geometric parameters, attribute information, and topology relationships corresponding to the VID; The real-time data stream layer receives the real-time energy consumption data stream output by the device positioning and calibration module through the data interface; The historical data layer aggregates and stores real-time energy consumption data according to daily and monthly time dimensions, and establishes a multi-dimensional index system with VID as the index key, including time index, device type index and topology relationship index. The time index is associated with the data collection timestamp, the device type index is associated with the type code in the device attribute information, and the topology relationship index is associated with the loop ID and the connected device VID. It supports quick querying of all associated data by VID and filtering energy consumption data corresponding to VID by device type and time range conditions. The equipment energy consumption time-series curve library is based on the energy consumption data stored in the historical data layer. It generates equipment energy consumption time-series curves according to VID to provide historical operating data benchmarks for the transformation simulation engine module. The time-series curves include real-time fluctuation curves, daily load curves, and monthly trend curves.

7. The building intelligent energy management system based on BIM technology according to claim 1, characterized in that, The transformation simulation engine module is based on the virtual device identifier (VID). It retrieves the historical operating data corresponding to the specified VID from the historical data layer of the component energy consumption database module. This includes real-time fluctuation curves, daily load curves, and monthly trend curves generated by the device energy consumption time series curve library. At the same time, it extracts the device geometric parameters, attribute information, and topological relationships associated with the VID from the basic information layer as the basic simulation data. The system receives modification component parameters added by users through interaction with the BIM model processing module. These parameters include the new equipment model, rated power, and energy efficiency ratio of the equipment being replaced. The system then compares the modification component parameters with the equipment attribute information associated with the corresponding VID to generate a parameter difference table. Based on retrieved historical operating data, basic information layer data, and parameter difference tables, and using historical energy consumption data as a baseline, the theoretical energy consumption value after the modification is calculated according to the changes in the modification parameters in the parameter difference table and the topological relationship of the equipment in the building. E all P is the historical average annual energy consumption of the equipment before the renovation, retrieved from the component energy consumption database module. x P is the rated power of the modified equipment. j η is the rated power of the equipment before the modification. j The energy efficiency ratio before the retrofit is the ratio of the effective output energy to the input energy of the equipment before the retrofit, η. x K represents the energy efficiency ratio after the upgrade, which is the ratio of the effective output energy to the input energy of the upgraded equipment. xs K is the topological influence coefficient, ranging from 0.9 to 1.

1. sjxs It is a time correction factor with a value range of 0.98-1.02; The theoretical energy consumption value E output nh Based on the user-input cost data of the modified components, the investment payback period formula is used. Calculate the payback period, where C zfy The total cost of the renovation is given by ΔE, where ΔE represents the energy saved and ΔE = E. all -E nh C dnjz S represents the value of a unit of electricity. wh The annual maintenance cost savings, which is the difference between the average annual maintenance cost before and after the renovation, will generate a pre-assessment report that includes the location of VID-associated devices, renovation parameters, energy consumption comparison, and investment payback period. The report will also be associated with the corresponding VID and synchronized to the interactive display module.

8. The building intelligent energy management system based on BIM technology according to claim 1, characterized in that, After the modification is implemented, the modification verification feedback module receives, in real time, the actual energy consumption data stream collected by sensors from the physical device associated with the new device VID bound to the device positioning and calibration module. The actual energy consumption data stream includes actual voltage, actual current, actual power, and actual cumulative energy consumption value E. sjnh The data is aggregated and stored in a temporary database according to daily and monthly time dimensions. At the same time, it is associated with the parameters of the new equipment, such as model, rated power, and energy efficiency ratio, to calculate the actual energy saving ΔE. sj =E all -E sjnh The system retrieves the energy saving ΔE from the corresponding VID pre-assessment report in the component energy consumption database module and compares it with the actual energy consumption data ΔE. sj Comparative analysis was conducted, and the energy saving error Δδ=|ΔE was calculated. sj -ΔE|×100%, when Δδ>5%, the new equipment VID, the comparison curve of actual energy consumption and theoretical energy consumption, and the energy saving error are input in reverse into the component energy consumption database module to calibrate the historical energy consumption data associated with the VID and the benchmark parameters for subsequent simulation calculations.

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