Intelligent building simulation method and system based on digital twinning

Abstract

The invention provides an intelligent building simulation method and system based on digital twinning, and the method comprises the steps: firstly collecting building structure information in a building, and constructing a twinning mapping model in the building; determining the association constraint of each sensing node in the building simulation process according to the topological structure characteristics among the sensing nodes and the association elements of each sensing node, and further determining the simulation granularity of each sensing node; then determining a dependency relationship of environment change in building simulation; the simulation control quantity of each virtual building scene in the twinning mapping model is determined through the dependency relationship of environment change and the simulation granularity of each sensing node in the building simulation process, and all the simulation control quantities are loaded into the digital twinning platform so as to update the twinning mapping model of the target building. By adopting the scheme of the invention, the collaborative response speed of multiple virtual building scenes in the digital twin system can be improved, so that the data consistency of virtual and real scenes is kept.

Description

Technical Field

[0001] The present application relates to the field of computer simulation technology, and more specifically, to an intelligent building simulation method and system based on digital twins. Background Art

[0002] Computer simulation (i.e. building simulation in computer-aided design) is the use of advanced technology to virtually test and optimize the various performance of buildings, among which digital twin technology plays an important role in this process. Digital twin refers to the creation of a virtual copy of a building through sensors and real-time data, which can dynamically reflect the state of the building under different environments and operating conditions. The application of this technology in building simulation allows designers to make more accurate predictions and adjustments before construction. In energy efficiency simulation, digital twins can monitor energy consumption in real time and optimize the configuration of air conditioning and heating systems. In structural simulation, it can provide real-time feedback on the loads and stresses borne by the building to ensure safety; in ventilation and acoustic simulation, digital twins can help designers analyze indoor air flow and noise control. By combining building simulation and digital twin technology, the building design process can be more efficient and accurate, thereby improving the performance and sustainability of the building.

[0003] Existing digital twin technology is mainly used in the field of smart buildings to monitor the energy efficiency, structural health, environmental control and other aspects of buildings in real time. It collects internal building data through sensor networks and generates virtual models. However, although the intelligent sensor network in the building can collect structural information in real time, due to the huge amount of data, the processing and transmission speeds often cannot meet the real-time requirements, resulting in data synchronization lag, causing delays in the building simulation process, and difficulty in maintaining consistency between multiple virtual scenes and real scenes, affecting the performance of virtual buildings. Therefore, how to improve the coordinated response speed of multiple virtual building scenes in the digital twin system, so as to maintain the data consistency of virtual and real scenes has become a difficult problem faced by the industry. Summary of the invention

[0004] The present application provides a smart building simulation method and system based on digital twins, which can improve the coordinated response speed of multiple virtual building scenes in the digital twin system, thereby maintaining the data consistency of virtual and real scenes.

[0005] In a first aspect, the present application provides a method for simulating an intelligent building based on digital twins, comprising the following steps: Establishing an intelligent sensor network of the target building, collecting the building structure information of the target building through the intelligent sensor network, importing the building structure information into the digital twin platform, and then constructing a twin mapping model of the target building; Determine the association constraints of each sensor node in the building simulation process according to the topological structure characteristics between each sensor node in the intelligent sensor network and the association elements of each sensor node, and determine the simulation granularity of each sensor node in the building simulation process through all the association constraints and the twin mapping model; Acquire environmental parameters of the target building, and perform dependency association on each virtual building scene in the twin mapping model according to the environmental parameters to obtain the dependency relationship of environmental changes in the building simulation; The simulation control quantity of each virtual building scene in the twin mapping model is determined by the dependency of the environmental changes and the simulation granularity of each sensor node in the building simulation process, and all the simulation control quantities are loaded into the digital twin platform to update the twin mapping model of the target building.

[0006] In some embodiments, importing the building structure information into the digital twin platform to construct a twin mapping model of the target building specifically includes: Preprocessing the building structure information to obtain building structure preprocessing information; Uploading the building structure preprocessing information to the digital twin platform; Initializing a building information tool of the digital twin platform; A twin mapping model of the target building is constructed by using the building structure preprocessing information and the building information tool.

[0007] In some embodiments, determining the association constraint of each sensor node in the building simulation process according to the topological structure characteristics between each sensor node in the intelligent sensor network and the association elements of each sensor node specifically includes: Performing topological analysis on the intelligent sensor network to obtain topological structure characteristics between various sensor nodes in the intelligent sensor network; Determine the dynamic correlation degree of each sensor node in the building simulation process according to the correlation factors of all sensor nodes; The association constraint of each sensor node in the building simulation process is determined by the topological structure characteristics and the dynamic association degree of each sensor node in the building simulation process.

[0008] In some embodiments, determining the simulation granularity of each sensor node in the building simulation process through all associated constraints and the twin mapping model specifically includes: Perform linear fitting on all associated constraints to obtain an associated constraint fitting curve; Determine the data feedback of each sensor node during the building simulation process through all digital twins in the twin mapping model; The simulation granularity of each sensor node in the building simulation process is determined according to the associated constraint fitting curve and the data feedback of each sensor node in the building simulation process.

[0009] In some embodiments, the dependency association of each virtual building scene in the twin mapping model is performed according to the environmental parameters to obtain the dependency relationship of the environmental change in the building simulation, specifically including: Extracting the environmental correlation between each sensor node in the intelligent sensor network and each digital twin in each virtual building scene in the twin mapping model from the environmental parameters; Determining visual differences between virtual building scenes within the twin mapping model; The dependencies of environmental changes in building simulation are determined based on all environmental relevance and visual differences between various virtual building scenes.

[0010] In some embodiments, determining the simulation control amount of each virtual building scene in the twin mapping model through the dependency relationship of the environmental changes and the simulation granularity of each sensor node in the building simulation process specifically includes: Clustering all simulation granularities to obtain multiple simulation particle clusters; Selecting a virtual building scene from the twin mapping model as a selected virtual building scene; Determining the simulation control amount of the selected virtual building scene in the twin mapping model according to the simulation granularity of the sensor node corresponding to each digital twin in the selected virtual building scene during the building simulation process, the dependency of the environmental change and all simulation particle clusters; Continue to determine the simulation control quantity of the remaining virtual building scenes in the twin mapping model.

[0011] In some embodiments, the smart sensor network is a topology network composed of multiple sensor nodes.

[0012] In a second aspect, the present application provides an intelligent building simulation system based on digital twins, comprising: A construction module is used to establish an intelligent sensor network of the target building, collect the building structure information of the target building through the intelligent sensor network, import the building structure information into the digital twin platform, and then build a twin mapping model of the target building; A processing module, used for determining the association constraints of each sensor node in the building simulation process according to the topological structure characteristics between each sensor node in the intelligent sensor network and the association elements of each sensor node, and determining the simulation granularity of each sensor node in the building simulation process through all the association constraints and the twin mapping model; The processing module is further used to obtain environmental parameters of the target building, and to perform dependency association on each virtual building scene in the twin mapping model according to the environmental parameters to obtain dependency relationships of environmental changes in the building simulation; An execution module is used to determine the simulation control quantity of each virtual building scene in the twin mapping model through the dependency of the environmental changes and the simulation granularity of each sensor node in the building simulation process, load all the simulation control quantities into the digital twin platform and then update the twin mapping model of the target building.

[0013] In a third aspect, the present application provides a computer device, comprising a memory and a processor, wherein the memory stores codes, and the processor is configured to obtain the codes and execute the above-mentioned digital twin-based smart building simulation method.

[0014] In a fourth aspect, the present application provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the above-mentioned digital twin-based smart building simulation method.

[0015] The technical solution provided by the embodiments disclosed in this application has the following beneficial effects: In the digital twin-based intelligent building simulation method and system provided in the present application, first, an intelligent sensor network of the target building is established, and the building structure information of the target building is collected through the intelligent sensor network, and the building structure information is imported into the digital twin platform, thereby constructing a twin mapping model of the target building; the association constraints of each sensor node in the building simulation process are determined according to the topological structure characteristics between each sensor node in the intelligent sensor network and the association elements of each sensor node, and the simulation granularity of each sensor node in the building simulation process is determined through all the association constraints and the twin mapping model; the environmental parameters of the target building are obtained, and the dependency associations of each virtual building scene in the twin mapping model are performed according to the environmental parameters to obtain the dependency relationship of the environmental changes in the building simulation; the simulation control quantity of each virtual building scene in the twin mapping model is determined through the dependency relationship of the environmental changes and the simulation granularity of each sensor node in the building simulation process, and all the simulation control quantities are loaded into the digital twin platform to update the twin mapping model of the target building.

[0016] It can be seen that in this application, the simulation control quantity of each virtual building scene in the twin mapping model can be determined by the dependency of the environmental changes and the simulation granularity of each sensor node in the building simulation process; wherein, firstly, by constructing an intelligent sensor network and collecting building structure information, the scheme realizes the accurate modeling of the target building, imports the structural data of the actual building into the digital twin platform and constructs the twin mapping model, so that the virtual building can accurately reflect the physical characteristics of the actual building; then, by analyzing the topological structure and associated elements of each sensor node in the intelligent sensor network, the associated constraints between the sensor nodes are determined. This link ensures that the interaction between the sensor and the twin mapping model can be precisely controlled by carefully dividing the simulation granularity of each sensor node. This precise simulation granularity enables the building simulation to be fine-grained between different sensor nodes. The degree of coordination and feedback improves the ability of the virtual building scene to accurately simulate the actual building environment; then, the environmental parameters of the target building are obtained, and the various virtual building scenes in the twin mapping model are dependently associated. The establishment of this dependency relationship enables the virtual scene to dynamically respond to environmental changes and adjust the virtual scene in the building simulation in real time. Through dependency association, the twin model can better simulate the performance of the actual building under different environmental conditions, thereby ensuring the sensitivity and adaptability of the virtual building scene to environmental changes; finally, by combining the dependency relationship of environmental changes with the simulation granularity of the sensor nodes, the simulation control quantity of the virtual building scene is determined, and these control quantities are loaded into the digital twin platform, thereby updating the twin mapping model of the target building; in summary, the solution of the present application can improve the coordinated response speed of multiple virtual building scenes in the digital twin system, thereby maintaining the data consistency of the virtual and real scenes. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 is an exemplary flow chart of a digital twin-based intelligent building simulation method according to some embodiments of the present application; Figure 2 is a schematic diagram of a communication network structure of a sensor node according to some embodiments of the present application; Figure 3 is a schematic diagram of a process for determining association constraints in some embodiments of the present application; Figure 4 is a structural schematic diagram of an intelligent building simulation system based on digital twins in some embodiments of the present application; Figure 5 It is a structural schematic diagram of a computer device for implementing a smart building simulation method based on digital twins as shown in some embodiments of the present application. DETAILED DESCRIPTION

[0018] In order to better understand the technical solution of the present application, the technical solution of the present application will be described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0019] refer to Figure 1 , which is an exemplary flow chart of a smart building simulation method based on digital twins according to some embodiments of the present application. The smart building simulation method 100 based on digital twins mainly includes the following steps: In step 101, an intelligent sensor network of a target building is established, and architectural structure information of the target building is collected through the intelligent sensor network, and the architectural structure information is imported into a digital twin platform, thereby constructing a twin mapping model of the target building.

[0020] In the specific implementation, a distributed topology structure is used to cover different areas in the building through multiple sensor nodes to form a centralized network, and the obtained centralized network is used as an intelligent sensor network, and then the building structure information in the target building is collected through the intelligent sensor network. Other methods can also be used in other embodiments, which will not be repeated here.

[0021] It should be noted that the intelligent sensor network described in this application is a topological structure network composed of multiple sensor nodes, and the types of sensor nodes include stereo vision sensors, temperature and humidity sensors, strain gauges, light sensors, etc. Figure 2 , this figure is a schematic diagram of the communication network structure of the sensor node shown in some embodiments of the present application. Among the network communication standards of the sensor nodes, IEEE802.15.4 is the most widely used one, which defines the physical layer and media access control layer for communication between sensor nodes. In order to improve the adaptability and flexibility of the network, the standard also introduces an adaptive layer. Above these basic layers are the network layer and the application layer, which provide network services and application services. In addition, 6LoWPAN (IPv6 Low Power Wireless Personal Area Network) is used as a network protocol, which enables the intelligent sensor network to utilize the IPv6 protocol stack to achieve seamless connection with the Internet. The function realization of the entire network depends on the sensor nodes on the limited nodes in the network. These sensor nodes are responsible for data collection, processing and transmission, and are the core components of the intelligent sensor network.

[0022] In addition, it should be noted that the building structure information described in this application is the geometric information of the internal structure of the building, the strain information of the internal structure of the building, and the pressure information inside the building.

[0023] In some embodiments, the building structure information is imported into the digital twin platform, and then the twin mapping model of the target building is constructed by the following steps: Preprocessing the building structure information to obtain building structure preprocessing information; Uploading the building structure preprocessing information to the digital twin platform; Initializing a building information tool of the digital twin platform; A twin mapping model of the target building is constructed by using the building structure preprocessing information and the building information tool.

[0024] In the specific implementation, first, the building structure information is preprocessed. This step includes denoising, formatting and standardization, etc., in order to ensure that the collected building structure information can be smoothly compatible with the digital twin platform; secondly, the building structure preprocessing information is uploaded to the database of the digital twin platform; then, the digital twin platform starts the building information model (Building Information Modeling, BIM) and 3D modeling engine; finally, the virtual model of the target building is generated in combination with the building structure preprocessing information, and mapped with the actual target building to realize the digital twin of the building, and the obtained virtual model is used as the twin mapping model of the target building. In other embodiments, other methods can also be used for implementation, which will not be repeated here.

[0025] It should be noted that the twin mapping model of the target building described in this application represents a digital mapping model that simulates the target building in a virtual environment. The twin mapping model maps the internal structure, function and behavior of the building into the digital space through data integration, and continuously synchronizes the state and behavior changes of the physical building. By establishing a twin mapping model, the various building parameters and dynamic changes of the target building can be monitored and optimized in real time. In addition, the twin mapping model contains multiple virtual building scenes, and each virtual building scene contains multiple digital twins. The digital twin is a virtual mapping model of the sensor node.

[0026] In step 102, the association constraints of each sensor node in the building simulation process are determined according to the topological structure characteristics between each sensor node in the intelligent sensor network and the association elements of each sensor node, and the simulation granularity of each sensor node in the building simulation process is determined through all the association constraints and the twin mapping model.

[0027] In some embodiments, reference Figure 3 As shown, this figure is a schematic diagram of the process of determining association constraints in some embodiments of the present application. In this embodiment, the association constraints of each sensor node in the building simulation process are determined according to the topological structure characteristics between each sensor node in the intelligent sensor network and the association elements of each sensor node, which can be implemented by the following steps: First, in step 1021, a topological analysis is performed on the smart sensor network to obtain topological structure characteristics between each sensor node in the smart sensor network; Secondly, in step 1022, the dynamic correlation degree of each sensor node in the building simulation process is determined according to the correlation elements of all sensor nodes; Then, in step 1023, the association constraint of each sensor node in the building simulation process is determined through the topological structure characteristics and the dynamic association degree of each sensor node in the building simulation process.

[0028] It should be noted that the topological structure characteristics described in this application represent characteristic values ​​of information flow paths in the network topological structure formed by each sensor node.

[0029] In addition, it should be noted that the association elements described in the present application represent the characteristic values ​​of the mutual dependence of sensor nodes in the intelligent sensor network, which can be obtained by performing correlation analysis on the data of different sensor nodes arranged at the same position in the target building. In other embodiments, other methods can be used to achieve this, which is not limited here.

[0030] In the specific implementation, first, the logical connection between each sensor node in the intelligent sensor network is used as an edge to construct a network topology diagram, wherein the logical connection can use the connection information of the sensor node, such as the network bandwidth between the sensor nodes, and then use the Dijkstra shortest path algorithm in graph theory to calculate the shortest path between each sensor node, and the sum of the shortest path lengths between each sensor node is used as the topological structure feature between each sensor node in the intelligent sensor network; secondly, a sensor node is selected as the selected sensor node, and then the association element of the selected sensor node is divided by the association element of the sensor node closest to the selected sensor node, and the value obtained by the division is used as the dynamic association degree of the selected sensor node in the building simulation process, and the dynamic association degree of the remaining sensor nodes in the building simulation process is further determined; then, the dynamic association degree of each sensor node in the building simulation process is multiplied by the topological structure feature, and the value obtained by the division is used as the association constraint of each sensor node in the building simulation process. In other embodiments, other methods can also be used for implementation, which is not limited here.

[0031] It should be noted that the association constraints described in this application represent parameters that constrain the information interaction between different sensor nodes during the building simulation process. The association constraints can clarify the association relationship and interaction boundaries between the sensor nodes, ensure that the sensors can cooperate efficiently, reduce redundant calculations and invalid communications, and thus improve the operating efficiency of the system.

[0032] In some embodiments, determining the simulation granularity of each sensor node in the building simulation process through all associated constraints and the twin mapping model can be implemented by the following steps: Perform linear fitting on all associated constraints to obtain an associated constraint fitting curve; Determine the data feedback of each sensor node during the building simulation process through all digital twins in the twin mapping model; The simulation granularity of each sensor node in the building simulation process is determined according to the associated constraint fitting curve and the data feedback of each sensor node in the building simulation process.

[0033] In the specific implementation, first, an existing linear fitting algorithm (such as a least squares support vector machine algorithm) is used to perform linear fitting on all associated constraints, and the curve obtained by fitting is used as an associated constraint fitting curve, wherein each value on the associated constraint fitting curve is used as an associated constraint fitting value, and each associated constraint fitting value corresponds to an associated constraint; secondly, a Kalman filter algorithm is used to perform state estimation on the data obtained from all digital twins in the twin mapping model, and the value obtained by the state estimation is used as the data feedback of the sensor node corresponding to each digital twin in the building simulation process; then, a sensor node is selected as the selected sensor node, and the absolute value is taken after subtracting the associated constraint fitting value corresponding to the associated constraint of the selected sensor node from the associated constraint of the selected sensor node, and the value obtained by taking the absolute value is divided by the data feedback of the selected sensor node in the building simulation process, and the value obtained by the division is used as the simulation granularity of the selected sensor node in the building simulation process, and the simulation granularity of the remaining sensor nodes in the building simulation process is continued to be determined. In other embodiments, other methods can also be used for implementation, which will not be repeated here.

[0034] It should be noted that the data feedback described in this application represents the parameters of the operating status of the digital twin corresponding to the monitoring sensor node during the building simulation process; the simulation granularity represents the degree of fineness of the decomposition and processing of the data collected by the sensor node during the building simulation process. The simulation granularity determines the accuracy and refinement of various parameters, feedback and regulation in the building simulation system. It not only reflects the detailed accuracy of the physical environment inside the building, but also affects the coordinated response speed and data consistency of each building environment in multiple virtual scenes.

[0035] In step 103, the environmental parameters of the target building are obtained, and dependency association is performed on each virtual building scene in the twin mapping model according to the environmental parameters to obtain the dependency relationship of the environmental changes in the building simulation.

[0036] In specific implementation, the environmental parameters of the target building are obtained through the management system of the intelligent sensor network. The environmental parameters refer to the environmental change data perceived by each sensor node in the building environment. The environmental change data include temperature, humidity, and light intensity data at one-minute intervals in the past hour.

[0037] It should be noted that each digital twin in each virtual building scene in the twin mapping model in this application contains virtual environment change data, where the virtual environment change data represents virtual data composed of temperature, humidity, and light intensity at one-minute intervals in the past hour in the twin mapping model.

[0038] In some embodiments, the dependency association of each virtual building scene in the twin mapping model is performed according to the environmental parameters to obtain the dependency relationship of the environmental changes in the building simulation, which can be achieved by the following steps: Extracting the environmental correlation between each sensor node in the intelligent sensor network and each digital twin in each virtual building scene in the twin mapping model from the environmental parameters; Determining visual differences between virtual building scenes within the twin mapping model; The dependencies of environmental changes in building simulation are determined based on all environmental relevance and visual differences between various virtual building scenes.

[0039] It should be noted that the visual differences described in this application represent the different characteristics of visual performance of different virtual building scenes.

[0040] During the specific internship, first, obtain the virtual environment change data of each digital twin in each virtual building scene in the twin mapping model, select a sensor node from the intelligent sensor network as the selected sensor node, calculate the Pearson correlation coefficient between the environmental change data of the selected sensor node and the virtual environment change data of the digital twin corresponding to the selected sensor node, continue to determine the Pearson correlation coefficient between the environmental change data of the remaining sensor nodes and the virtual environment change data of the digital twin corresponding to the selected sensor node, then sum all the Pearson correlation coefficients, and use the summed value as the global correlation coefficient, and further calculate the Pearson correlation coefficient between the virtual environment change data of the digital twin corresponding to each sensor node. The coefficients are all divided by the global correlation coefficient, and the value obtained by the division is used as the environmental correlation between each sensor node and the corresponding digital twin in each virtual building scene in the twin mapping model; secondly, the image difference detection technology (such as structural similarity) is used to compare the virtual building scenes at different times in the twin mapping model, and then the values ​​obtained by comparing each virtual building scene at different times are averaged, and the averages are further summed, and the summed value is used as the visual difference between the virtual building scenes; then, each environmental correlation is divided by the visual difference, and then all the divided values ​​are summed, and the summed value is used as the dependency of environmental changes in building simulation.

[0041] It should be noted that the dependency relationship of environmental changes described in the present application represents the parameters of the coordinated environmental changes generated when each virtual building scene in the twin mapping model is driven by the environmental change data in the target building.

[0042] In step 104, the simulation control quantity of each virtual building scene in the twin mapping model is determined by the dependency of the environmental changes and the simulation granularity of each sensor node in the building simulation process, and all the simulation control quantities are loaded into the digital twin platform to update the twin mapping model of the target building.

[0043] In some embodiments, determining the simulation control amount of each virtual building scene in the twin mapping model through the dependency relationship of the environmental changes and the simulation granularity of each sensor node in the building simulation process can be achieved by the following steps: Clustering all simulation granularities to obtain multiple simulation particle clusters; Selecting a virtual building scene from the twin mapping model as a selected virtual building scene; Determining the simulation control amount of the selected virtual building scene in the twin mapping model according to the simulation granularity of the sensor node corresponding to each digital twin in the selected virtual building scene during the building simulation process, the dependency of the environmental change and all simulation particle clusters; Continue to determine the simulation control quantity of the remaining virtual building scenes in the twin mapping model.

[0044] During the specific practice, first, the DBSCAN clustering algorithm is used to cluster all simulation granularities to obtain multiple clusters, and the obtained clusters are all used as simulation particle clusters, wherein each simulation particle cluster is composed of multiple simulation granularities; secondly, a digital twin is selected from the selected virtual building scene as the selected digital twin, and all the simulation granularities in the simulation particle cluster to which the simulation granularity of the sensor node corresponding to the selected digital twin belongs in the building simulation process are summed, and then the summed value is divided by the dependency of the environmental change, and the divided value is used as the simulation synergy degree of the selected digital twin, and the simulation synergy degree of the remaining digital twins in the selected digital twin is continued to be determined, and the simulation synergy degrees of all digital twins in the selected virtual building scene are further differentiated, and then all the differentiated values ​​are summed, and the summed value is used as the simulation control quantity of the selected virtual building scene, and the simulation control quantity of the remaining virtual building scenes in the twin mapping model is continued to be determined. Other methods can also be used in other embodiments, which are not limited here.

[0045] It should be noted that the simulation control quantity described in this application represents the parameter value that controls the collaborative work between the digital twin and the environmental condition elements in the virtual building scene in the twin mapping model. The simulation control quantity can improve the response speed between these virtual scenes and ensure that changes occurring in one scene can be quickly and accurately transmitted and reflected in other scenes.

[0046] In specific implementation, all simulation control quantities are loaded into the digital twin platform and then the twin mapping model of the target building is updated. This can be achieved in the following manner, namely: all simulation control quantities are transmitted to the digital twin platform using the RESTful API interface, and the simulation control quantity of each virtual building scene in the twin mapping model is used as the compensation coefficient of each digital twin in each virtual building scene. The delay of each digital twin data in each virtual building scene is compensated using delay estimation technology (such as a synchronous clock protocol), thereby completing the update of the twin mapping model of the target building. Other methods may also be used in other embodiments, which will not be repeated here.

[0047] In addition, in another aspect of the present application, in some embodiments, the present application provides an intelligent building simulation system based on digital twins, referring to Figure 4 , which is a structural schematic diagram of an intelligent building simulation system based on digital twins according to some embodiments of the present application. The intelligent building simulation system based on digital twins 400 includes: a construction module 401, a processing module 402 and an execution module 403, which are respectively described as follows: Construction module 401: In the present application, acquisition module 401 is mainly used to build an intelligent sensor network of a target building, collect building structure information of the target building through the intelligent sensor network, import the building structure information into the digital twin platform, and then build a twin mapping model of the target building; Processing module 402, in the present application, the processing module 402 is used to determine the association constraints of each sensor node in the building simulation process according to the topological structure characteristics between each sensor node in the intelligent sensor network and the association elements of each sensor node, and determine the simulation granularity of each sensor node in the building simulation process through all the association constraints and the twin mapping model; It should be noted that the processing module 402 in the present application is also used to obtain the environmental parameters of the target building, and to perform dependency association on each virtual building scene in the twin mapping model according to the environmental parameters to obtain the dependency relationship of the environmental changes in the building simulation; Execution module 403. In the present application, execution module 403 is mainly used to determine the simulation control quantity of each virtual building scene in the twin mapping model through the dependency relationship of the environmental changes and the simulation granularity of each sensor node in the building simulation process, load all the simulation control quantities into the digital twin platform and then update the twin mapping model of the target building.

[0048] In addition, the present application also provides a computer device, which includes a memory and a processor, the memory stores code, and the processor is configured to obtain the code and execute the above-mentioned digital twin-based smart building simulation method.

[0049] In some embodiments, reference Figure 5 , which is a schematic diagram of the structure of a computer device for implementing a digital twin-based intelligent building simulation method according to some embodiments of the present application. The digital twin-based intelligent building simulation method in the above embodiment can be Figure 5 The computer device 500 shown in the figure is implemented, and the computer device 500 includes at least one processor 501, a communication bus 502, a memory 503 and at least one communication interface 504.

[0050] Processor 501 can be a general-purpose central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more for controlling the execution of the digital twin-based smart building simulation method in the present application.

[0051] The communication bus 502 may be used to transmit information between the above-mentioned components.

[0052] The memory 503 may be a read-only memory (ROM) or other types of static storage devices that can store static information and instructions, a random access memory (RAM) or other types of dynamic storage devices that can store information and instructions, or an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, an optical disc storage (including a compressed optical disc, a laser disc, an optical disc, a digital versatile disc, a Blu-ray disc, etc.), a magnetic disk or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of an instruction or data structure and can be accessed by a computer, but is not limited thereto. The memory 503 may exist independently and be connected to the processor 501 via the communication bus 502. The memory 503 may also be integrated with the processor 501.

[0053] The memory 503 is used to store the program code for executing the solution of the present application, and the execution is controlled by the processor 501. The processor 501 is used to execute the program code stored in the memory 503. The program code may include one or more software modules. The method described in the above method embodiment can be implemented by the processor 501 and one or more software modules in the program code in the memory 503.

[0054] The communication interface 504 uses any transceiver or other device for communicating with other devices or communication networks, such as Ethernet, radio access network (RAN), wireless local area networks (WLAN), etc.

[0055] In a specific implementation, as an embodiment, a computer device may include multiple processors, each of which may be a single-CPU processor or a multi-CPU processor. The processor here may refer to one or more devices, circuits, and / or processing cores for processing data (e.g., computer program instructions).

[0056] The above-mentioned computer device may be a general-purpose computer device or a special-purpose computer device. In a specific implementation, the computer device may be a desktop computer, a portable computer, a network server, a personal digital assistant (PDA), a mobile phone, a tablet computer, a wireless terminal device, a communication device or an embedded device. The embodiment of the present application does not limit the type of computer device.

[0057] In addition, the present application also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the above-mentioned digital twin-based intelligent building simulation method.

[0058] In summary, in the digital twin-based intelligent building simulation method and system disclosed in the embodiment of the present application, first, an intelligent sensor network of the target building is established, and the building structure information of the target building is collected through the intelligent sensor network, and the building structure information is imported into the digital twin platform, thereby constructing a twin mapping model of the target building; the association constraints of each sensor node in the building simulation process are determined according to the topological structure characteristics between each sensor node in the intelligent sensor network and the associated elements of each sensor node, and the simulation granularity of each sensor node in the building simulation process is determined through all the associated constraints and the twin mapping model; the environmental parameters of the target building are obtained, and the dependency association of each virtual building scene in the twin mapping model is performed according to the environmental parameters to obtain the dependency relationship of the environmental change in the building simulation; the simulation control amount of each virtual building scene in the twin mapping model is determined through the dependency relationship of the environmental change and the simulation granularity of each sensor node in the building simulation process, and all the simulation control amounts are loaded into the digital twin platform to update the twin mapping model of the target building. The scheme of the present application can improve the coordinated response speed of multiple virtual building scenes in the digital twin system, thereby maintaining the data consistency of the virtual and real scenes.

[0059] Although the preferred embodiments of the present application have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0060] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

Claims

1. A method for simulating an intelligent building based on digital twins, characterized in that: The steps include: Establishing an intelligent sensor network of the target building, collecting the building structure information of the target building through the intelligent sensor network, importing the building structure information into the digital twin platform, and then constructing a twin mapping model of the target building; Determine the association constraints of each sensor node in the building simulation process according to the topological structure characteristics between each sensor node in the intelligent sensor network and the association elements of each sensor node, and determine the simulation granularity of each sensor node in the building simulation process through all the association constraints and the twin mapping model; Acquire environmental parameters of the target building, and perform dependency association on each virtual building scene in the twin mapping model according to the environmental parameters to obtain the dependency relationship of environmental changes in the building simulation; The simulation control quantity of each virtual building scene in the twin mapping model is determined by the dependency of the environmental changes and the simulation granularity of each sensor node in the building simulation process, and all the simulation control quantities are loaded into the digital twin platform to update the twin mapping model of the target building.

2. The method according to claim 1, characterized in that Importing the building structure information into the digital twin platform and then constructing a twin mapping model of the target building specifically includes: Preprocessing the building structure information to obtain building structure preprocessing information; Uploading the building structure preprocessing information to the digital twin platform; Initializing a building information tool of the digital twin platform; A twin mapping model of the target building is constructed by using the building structure preprocessing information and the building information tool.

3. The method according to claim 1, characterized in that Determining the association constraints of each sensor node in the building simulation process according to the topological structure characteristics between each sensor node in the intelligent sensor network and the association elements of each sensor node specifically includes: Performing topological analysis on the intelligent sensor network to obtain topological structure characteristics between various sensor nodes in the intelligent sensor network; Determine the dynamic correlation degree of each sensor node in the building simulation process according to the correlation factors of all sensor nodes; The association constraint of each sensor node in the building simulation process is determined by the topological structure characteristics and the dynamic association degree of each sensor node in the building simulation process.

4. The method according to claim 1, characterized in that The simulation granularity of each sensor node in the building simulation process is determined by all associated constraints and the twin mapping model, specifically including: Perform linear fitting on all associated constraints to obtain an associated constraint fitting curve; Determine the data feedback of each sensor node during the building simulation process through all digital twins in the twin mapping model; The simulation granularity of each sensor node in the building simulation process is determined according to the associated constraint fitting curve and the data feedback of each sensor node in the building simulation process.

5. The method according to claim 1, characterized in that Dependency association is performed on each virtual building scene in the twin mapping model according to the environmental parameters to obtain the dependency relationship of the environmental change in the building simulation, which specifically includes: Extracting the environmental correlation between each sensor node in the intelligent sensor network and each digital twin in each virtual building scene in the twin mapping model from the environmental parameters; Determining visual differences between virtual building scenes within the twin mapping model; The dependencies of environmental changes in building simulation are determined based on all environmental relevance and visual differences between various virtual building scenes.

6. The method according to claim 1, characterized in that Determining the simulation control amount of each virtual building scene in the twin mapping model through the dependency relationship of the environmental changes and the simulation granularity of each sensor node in the building simulation process specifically includes: Clustering all simulation granularities to obtain multiple simulation particle clusters; Selecting a virtual building scene from the twin mapping model as a selected virtual building scene; Determining the simulation control amount of the selected virtual building scene in the twin mapping model according to the simulation granularity of the sensor node corresponding to each digital twin in the selected virtual building scene during the building simulation process, the dependency of the environmental change and all simulation particle clusters; Continue to determine the simulation control quantity of the remaining virtual building scenes in the twin mapping model.

7. The method according to claim 1, characterized in that The intelligent sensor network is a topological structure network composed of multiple sensor nodes.

8. An intelligent building simulation system based on digital twins, characterized in that: include: A construction module is used to establish an intelligent sensor network of the target building, collect the building structure information of the target building through the intelligent sensor network, import the building structure information into the digital twin platform, and then build a twin mapping model of the target building; A processing module, used for determining the association constraints of each sensor node in the building simulation process according to the topological structure characteristics between each sensor node in the intelligent sensor network and the association elements of each sensor node, and determining the simulation granularity of each sensor node in the building simulation process through all the association constraints and the twin mapping model; The processing module is further used to obtain environmental parameters of the target building, and to perform dependency association on each virtual building scene in the twin mapping model according to the environmental parameters to obtain dependency relationships of environmental changes in the building simulation; An execution module is used to determine the simulation control quantity of each virtual building scene in the twin mapping model through the dependency of the environmental changes and the simulation granularity of each sensor node in the building simulation process, load all the simulation control quantities into the digital twin platform and then update the twin mapping model of the target building.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the digital twin-based intelligent building simulation method described in any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the digital twin-based intelligent building simulation method as described in any one of claims 1 to 7 is implemented.

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