Smart park optimization method and device based on digital twinborn and BIM

Abstract

The invention provides a smart park optimization method and device based on digital twinning and BIM, and the method comprises the steps: collecting the actual information of a park, and carrying out the preprocessing; based on the configured basic BIM model, integrating the basic BIM model by using actual information; constructing a three-dimensional digital twinborn model, and fusing the integrated BIM model with the three-dimensional digital twinborn model; and analyzing and processing the park operation data by using the current fused three-digit digital twinborn model so as to realize park optimization. The digital mirror image of the smart park is constructed by integrating digital twinning, BIM, Internet of Things, big data, cloud computing, GIS and other frontier technologies, and comprehensive perception, accurate analysis, intelligent decision and efficient management of the park are realized. According to the scheme, the intelligent level, the operation efficiency and the sustainable development capability of the park are improved, and convenient, efficient and intelligent service experience is provided for park managers, settled enterprises and individual users.

Description

Technical Field

[0001] The present invention relates to the technical field of digital twins, and in particular to a smart park optimization method and device based on digital twins and BIM (Building Information Modeling). Background Art

[0002] With the rapid development of information technology, especially the widespread application of technologies such as the Internet of Things, big data, cloud computing, and artificial intelligence, the construction of smart cities has become a global trend. As an important part of smart cities, the construction of smart parks is of great significance to promoting urban digital transformation and sustainable development. Traditional park management faces many problems such as information asymmetry, low resource utilization efficiency, high operating costs, and delayed management decisions, which makes it difficult to meet the needs of modern enterprises and individual users for efficient, convenient, and intelligent services. Summary of the invention

[0003] The technical problem to be solved by the present invention is how to improve the accuracy and efficiency of abnormal analysis of a smart park platform based on digital twin technology; in view of this, the present invention provides a smart park optimization method and device based on digital twin and BIM.

[0004] The technical solution adopted by the present invention is a smart park optimization method based on digital twins and BIM, comprising:

[0005] Step S1, collecting actual information of the park and performing preprocessing;

[0006] Step S2, based on the configured basic BIM model, integrating the basic BIM model using the actual information;

[0007] Step S3, constructing a three-dimensional digital twin model, and fusing the integrated BIM model with the three-dimensional digital twin model;

[0008] Step S4, using the currently integrated three-dimensional digital twin model, analyzes and processes the park operation data to achieve park optimization.

[0009] In one embodiment, the collecting of actual park information includes:

[0010] Through the Internet of Things platform and at least one of the sensors, monitoring equipment and smart terminals in the park, data including environmental parameters, equipment status parameters, personnel flow, vehicle entry and exit information and business management system data are collected.

[0011] In one embodiment, the pre-processing comprises:

[0012] The actual information data collected is cleaned, denoised, and formatted in sequence;

[0013] Perform data compression and encryption on the current data.

[0014] In one embodiment, the basic BIM model is configured based on at least the following data:

[0015] Building structure, equipment layout, pipeline direction, and material properties.

[0016] In one embodiment, integrating the basic BIM model using the actual information includes:

[0017] Adding additional information layers to the basic BIM model;

[0018] In the information layer, an association relationship is established between the actual information and the equipment in the basic BIM model.

[0019] In one embodiment, step S3 specifically includes:

[0020] In the three-dimensional modeling software, the overall framework of the three-dimensional digital twin model is constructed according to the actual layout and facilities of the park;

[0021] Transfer the current BIM model file to the working directory of the 3D modeling software;

[0022] Open the BIM model file in the 3D modeling software to perform format conversion and parameter adjustment;

[0023] In one embodiment, the method further comprises:

[0024] The three-dimensional digital twin model is optimized to improve the realism and operating efficiency of the model.

[0025] Another aspect of the present invention further provides a smart park optimization device based on digital twin and BIM, comprising:

[0026] A collection unit, configured to collect actual information of the park and perform preprocessing;

[0027] An integration unit configured to integrate the basic BIM model based on the configuration using the actual information;

[0028] A fusion unit is configured to construct a three-dimensional digital twin model and fuse the integrated BIM model with the three-dimensional digital twin model;

[0029] The optimization unit is configured to utilize the currently integrated three-dimensional digital twin model to analyze and process the park operation data to achieve park optimization.

[0030] Another aspect of the present invention provides an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the computer program is executed by the processor, the steps of the smart park optimization method based on digital twins and BIM are implemented as described in any one of the above items.

[0031] Another aspect of the present invention provides a computer storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of the smart park optimization method based on digital twins and BIM as described in any one of the above items are implemented.

[0032] Compared with the prior art, the present invention has at least the following advantages:

[0033] The technical solution of the present invention proposes a smart park optimization method and system based on digital twin and BIM, aiming to build a digital mirror of the smart park by integrating cutting-edge technologies such as digital twin, BIM, Internet of Things, big data, cloud computing, GIS, etc., to achieve comprehensive perception, accurate analysis, intelligent decision-making and efficient management of the park. This solution aims to improve the intelligence level, operational efficiency and sustainable development capabilities of the park, and provide convenient, efficient and intelligent service experience for park managers, resident enterprises and individual users. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 A schematic diagram of a flow chart of a smart park optimization method based on digital twins and BIM according to an embodiment of the present invention;

[0035] Figure 2 Another flowchart of a smart park optimization method based on digital twins and BIM according to an embodiment of the present invention;

[0036] Figure 3 A schematic diagram of the composition of a smart park optimization method based on digital twins and BIM according to an embodiment of the present invention;

[0037] Figure 4 The figure is a schematic diagram of the composition of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0038] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined purpose, the present invention is described in detail below in conjunction with the accompanying drawings and preferred embodiments.

[0039] Unless otherwise defined, all terms (including technical terms and scientific terms) used in this article have the same meaning as commonly understood by ordinary technicians in the field to which this application belongs. It should also be understood that terms (such as terms defined in commonly used dictionaries) should be interpreted as having the same meaning as their meaning in the context of the relevant technology, and will not be interpreted in an idealized or overly formal sense unless explicitly defined in this article.

[0040] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0041] The embodiments of the present invention, as Figure 1 As shown in the figure, a smart park optimization method based on digital twin and BIM includes:

[0042] Step S1, collecting actual information of the park and performing preprocessing;

[0043] Step S2, based on the configured basic BIM model, integrating the basic BIM model using the actual information;

[0044] Step S3, constructing a three-dimensional digital twin model, and fusing the integrated BIM model with the three-dimensional digital twin model;

[0045] Step S4, using the currently integrated three-dimensional digital twin model, analyzes and processes the park operation data to achieve park optimization.

[0046] refer to Figure 1 as well as Figure 2 , the method provided in this embodiment will be described in detail step by step below.

[0047] Step S1, collect actual information of the park and perform preprocessing.

[0048] Data collection: As the core of data collection, it is connected to various sensors, monitoring equipment and smart terminals in the park through the Internet of Things platform. These devices collect information such as environmental parameters (such as temperature, humidity, air quality), equipment status (such as operating status, energy consumption data), personnel flow, vehicle entry and exit, etc. in real time, and transmit the data to the server. At the same time, it also integrates the data of the park's existing business management system, such as security system, energy management system, building automation system, etc., to form a comprehensive and multi-dimensional park data resource pool.

[0049] Data preprocessing and integration: Preprocess the collected raw data, including data cleaning, denoising, and format unification, to ensure data accuracy and consistency. Use technical means such as data compression and encryption to ensure the security of data transmission and storage. Integrate the preprocessed data with the BIM model to provide a solid foundation for the construction of digital twins. During the data integration process, ensure the real-time and accuracy of the data so that the digital twin model can truly reflect the actual status of the park.

[0050] Step S2: Based on the configured basic BIM model, the basic BIM model is integrated using the actual information.

[0051] BIM model import: First, collect all relevant BIM model data from the park's BIM library. This data includes detailed information such as building structure, equipment layout, pipeline direction, material properties, etc. Establish a connection with the BIM library through the network interface, verify permissions and download the required model files. Perform version control and consistency checks on the downloaded BIM models to ensure the accuracy and integrity of the model data. Integrate BIM models from different sources onto a unified platform to provide a basis for subsequent processing.

[0052] Integration of BIM model and IoT data: Add additional information layers to the basic BIM model to enrich the connotation of the model. For example, dynamic information such as equipment maintenance history, energy consumption data, and operating status are associated with the corresponding equipment in the BIM model. Use professional BIM processing software to perform geometric correction on the model to eliminate errors that may occur during the modeling process. Supplement and improve the attribute information of the BIM model, such as equipment model, manufacturer, installation date, etc., to provide rich basic data for subsequent data analysis and decision support. Lightweight the BIM model, reduce the rendering burden of the model in the digital twin system by reducing the geometric details and texture complexity of the model, and improve the response speed and fluency of the system. In this way, the digital twin model can not only display the static structure of the park, but also reflect the operating status and energy consumption of the equipment in real time, providing rich data support for subsequent intelligent analysis and optimization management.

[0053] Step S3, constructing a three-dimensional digital twin model, and fusing the integrated BIM model with the three-dimensional digital twin model.

[0054] 3D modeling technology selection: According to the actual situation and needs of the park, select appropriate 3D modeling technology to build a digital twin model. Analyze the characteristics of the park, such as scale, complexity, application scenarios, etc., to determine the appropriate 3D modeling technology. Select 3D modeling software or engines with high precision, high efficiency, and scalability, such as Unity, Unreal Engine, etc. Configure the 3D modeling environment, including hardware resource allocation, software parameter settings, etc., to ensure the smooth progress of the modeling process.

[0055] Import and convert BIM models: Import the preprocessed BIM model into the 3D modeling software, and perform necessary format conversion and parameter adjustments to meet the requirements of the digital twin system. Transfer the BIM model file to the working directory of the 3D modeling software. Open the BIM model file in the 3D modeling software and perform format conversion and parameter adjustments. This includes the unification of coordinate systems, conversion of unit systems, mapping of materials, etc. Perform a preliminary rendering test on the BIM model to check the display effect and performance of the model in the 3D scene.

[0056] Digital twin model construction and optimization: Based on BIM model and 3D modeling technology, a digital twin model of the park is constructed and optimized to improve the realism and operation efficiency of the model. In the 3D modeling software, the overall framework of the digital twin model is constructed according to the actual layout and facilities of the park. Detailed elements such as vegetation, roads, vehicles, characters, etc. are gradually added to enrich the scene content of the digital twin model. The digital twin model is optimized, including lighting adjustment, shadow rendering, texture mapping, etc., to improve the realism and visual effects of the model. Performance optimization is performed to improve the operation efficiency of the digital twin model in the system by reducing the number of unnecessary polygons, optimizing material settings, adjusting rendering order, etc.

[0057] Step S4, using the currently integrated three-dimensional digital twin model, analyzes and processes the park operation data to achieve park optimization.

[0058] Data analysis and mining: Use big data analysis technology to conduct in-depth mining of park operation data. By identifying operating rules, predicting development trends, and discovering potential problems, we can propose optimization suggestions such as energy saving and consumption reduction, and safety assurance. For example, according to the characteristics and needs of the park, determine the goals and scope of data analysis. Select appropriate data analysis algorithms and tools, such as machine learning, data mining, and statistical analysis. Develop data analysis algorithm models, and train and verify them to ensure the accuracy and effectiveness of the algorithms. Deploy the trained algorithm models to the server to analyze and process park data in real time.

[0059] Intelligent decision support: Based on the results of data analysis, decision suggestions are automatically generated and sent to relevant equipment or management personnel. For example, in terms of energy consumption management, energy-saving strategies are formulated based on real-time energy consumption data forecast analysis and sent to relevant equipment for execution. In terms of safety management, emergency plans are automatically triggered based on risk warning information to improve emergency response speed. Through intelligent decision-making and instruction issuance, precise management and optimized control of the park can be achieved.

[0060] In this section, a series of detailed steps can be used to implement the use of big data analysis technology to conduct in-depth mining of park operation data. By identifying operation rules, predicting development trends, discovering potential problems, etc., optimization suggestions such as energy saving and consumption reduction, safety assurance, etc. can be put forward.

[0061] Combine the following Figure 2 , providing a detailed step similar to the technical solution of the above embodiment, which can be referred to as follows:

[0062] Step 1: Clarify the goals and scope of data analysis

[0063] Obtain information about the park's daily operations, management processes, and current needs. Determine the park's operating model and data characteristics based on the park's physical layout, facility distribution, and personnel and logistics flows.

[0064] After collecting enough information, it is necessary to conduct an in-depth analysis of the park's historical operating data. This data may include energy consumption data, equipment maintenance records, personnel entry and exit records, etc. By analyzing this data, it is possible to identify the problems and bottlenecks in the park's operation process, thereby determining the goal and scope of data analysis.

[0065] Specifically, the goals of data analysis may include improving energy efficiency, optimizing equipment maintenance plans, improving personnel access management, etc. The scope of data analysis may cover the park's energy consumption data, equipment status data, personnel access data, etc.

[0066] Step 2: Select data analysis algorithms and tools

[0067] After determining the goals and scope of data analysis, it is necessary to select appropriate data analysis algorithms and tools.

[0068] It is necessary to evaluate the applicability and effectiveness of various data analysis algorithms. For predictive problems, such as energy consumption prediction, time series analysis algorithms or regression algorithms in machine learning can be selected. For classification problems, such as equipment fault type identification, classification algorithms such as decision trees and random forests can be selected. For association problems, such as analyzing the relationship between personnel entry and exit and energy consumption, association rule mining algorithms can be selected.

[0069] When choosing a specific algorithm, you need to consider the complexity of the algorithm, the computing resource requirements, and whether it is easy to implement and deploy. At the same time, you also need to give priority to algorithms that have successful application cases and have been verified to ensure the reliability and effectiveness of the selected algorithm.

[0070] In addition to algorithm selection, it is also necessary to select appropriate data analysis tools or platforms. These tools or platforms should be able to support the development, training and verification of the selected algorithms, and have good usability, performance and scalability. At the same time, it is also necessary to consider the integration capabilities of the tools or platforms with the existing systems of the park to ensure the smooth output and application of data analysis results.

[0071] Step 3: Data analysis algorithm model development

[0072] After selecting the appropriate data analysis algorithms and tools, you need to start developing the data analysis algorithm model. This step includes data preprocessing, feature selection and extraction, model construction and training, and model verification and optimization.

[0073] First, the raw data needs to be cleaned, converted, and normalized. The purpose of this step is to ensure the quality and consistency of the data so that subsequent analysis and modeling can be better performed. During the preprocessing process, it is necessary to remove duplicate, missing, or abnormal data records, convert the data format for calculation and analysis, and normalize the data to ensure that data from different sources or different scales can be used in the same model.

[0074] Next, you need to select or extract features that have an impact on the analysis target from the preprocessed data. This step is a very critical part of the modeling process, because the selection and extraction of features directly affects the effect and accuracy of the subsequent model. It is necessary to analyze the correlation between the features and the target, and select features with higher correlation for modeling. At the same time, you can also use feature selection algorithms to further screen features to improve the performance and stability of the model.

[0075] After feature selection and extraction, it is necessary to start building a data analysis model. Based on the selected algorithms and tools, the corresponding model framework can be built and the model can be trained using training data. During the training process, the model parameters need to be adjusted to optimize its performance, and methods such as cross-validation are used to evaluate the model's stability and generalization ability.

[0076] Finally, the trained model needs to be verified and tested to ensure its accuracy and effectiveness. This step is very important because only models that have passed verification and testing can be deployed in actual production environments for application. The model needs to be verified using test data, and the model's accuracy, recall and other indicators need to be evaluated. The performance of the model during the verification process should be analyzed to identify possible problems or deficiencies. If the model's performance does not meet expectations, the model needs to be optimized and improved to improve its performance and accuracy.

[0077] Step 4: Model deployment and real-time data processing

[0078] After the model development and verification is completed, the trained model needs to be deployed to the actual production environment for application. This step includes model deployment preparation, real-time data processing process design, and real-time data processing implementation.

[0079] First, you need to package the model files and related dependencies into a deployable format and configure the environment to ensure that it has the computing resources and software environment required to run the model. During the deployment process, you also need to perform preliminary testing on the model to ensure that it can run properly and produce accurate results.

[0080] Next, a real-time data processing process needs to be designed to ensure that the park data can be input into the model and processed in real time. This step includes determining the frequency and method of data input, designing the data processing process, and ensuring the efficiency and stability of the data processing process. It is necessary to ensure that the data processing process can meet the real-time data processing needs of the park and can perform operations such as preprocessing, feature extraction, model inference, and result output on the input data.

[0081] Finally, it is necessary to implement the real-time data processing process and input the park data into the model in real time for processing. This step includes writing data processing code, integrating the data processing code with the model, and testing and optimizing the real-time data processing process. It is necessary to ensure the smooth operation of the real-time data processing process and be able to accurately process and analyze the input data to produce valuable results.

[0082] Step 5: Result output and application

[0083] After the real-time data processing is completed, the model processing results need to be output to the park's management system or related applications to provide decision support for the park's operation and management. This step includes multiple links such as result output format and interface design, result output and integration, and result visualization and further analysis.

[0084] First, the result output format and interface need to be designed for integration with other systems or applications. This step is very important because only through the appropriate format and interface can the model processing results be smoothly output to other systems or applications and provide decision support for the operation and management of the park.

[0085] Next, the model processing results need to be output to the park's management system or related applications through the interface. During the output process, the accuracy and real-time nature of the data need to be ensured so that park managers can obtain the latest data analysis results in a timely manner and make corresponding decisions.

[0086] Finally, the output results can be visualized or further analyzed so that park managers can better understand and use these results. For example, energy consumption data can be displayed to managers in the form of charts so that they can more intuitively understand the energy consumption and energy saving potential of the park. At the same time, the output results can be further analyzed and mined to discover more valuable information and patterns.

[0087] Through the above detailed step-by-step process plan, the goal and scope of data analysis can be determined according to the characteristics and needs of the park, and appropriate data analysis algorithms and tools can be selected for model development, training and verification. Finally, the trained algorithm model is deployed to the server to analyze and process the park data in real time, providing intelligent decision support for the operation and management of the park. At the same time, the model needs to be continuously maintained and updated to adapt to changes in park data and new business needs, and to ensure the accuracy and real-time nature of data analysis results.

[0088] 5. Automatic control and optimization adjustment

[0089] Automated control: Develop automated control strategies based on decision-making instructions and the actual needs of the park. For example, automatically adjust air conditioning temperature and lighting brightness based on environmental parameters; automatically issue maintenance tasks based on equipment status predictive maintenance plans, etc. Remote control and automated adjustment of equipment in the park can be achieved through the Internet of Things platform to improve the operating efficiency and comfort of the park.

[0090] Optimization, adjustment and feedback: After executing the control strategy, continuously monitor the changes in the park's operating status and evaluate the control effect. By comparing and analyzing the data changes before and after execution, evaluate the actual effects of optimization strategies such as energy saving and consumption reduction, safety assurance, etc. According to the evaluation results, make necessary optimization adjustments to the control strategy to ensure the continuous optimization and improvement of the park's operation.

[0091] VI. User Experience and Interactive Services

[0092] Mobile applications and personalized services: To improve user experience and interactivity, we develop mobile applications for the park. Users can easily query park information, make appointments, and report equipment repairs through mobile applications. At the same time, personalized park service information is intelligently pushed based on user behavior and preference analysis. For example, we push information about surrounding restaurants, fitness facilities, and other life services to corporate employees; and provide visitors with convenient services such as navigation and parking.

[0093] User feedback and continuous improvement: Collect user feedback and continuously improve and optimize mobile applications and interactive services. Promote continuous innovation and improvement of park services through user participation and co-creation. For example, optimize the interface design and functional layout of mobile applications based on user feedback; develop new interactive service functions based on user needs. Improve user experience and satisfaction through continuous improvement and innovation.

[0094] The second embodiment of the present invention corresponds to the first embodiment, and can be referred to Figure 3 This embodiment introduces a smart park optimization method and device based on digital twin and BIM, including the following components:

[0095] A collection unit, configured to collect actual information of the park and perform preprocessing;

[0096] An integration unit configured to integrate the basic BIM model based on the configuration using the actual information;

[0097] A fusion unit is configured to construct a three-dimensional digital twin model and fuse the integrated BIM model with the three-dimensional digital twin model;

[0098] The optimization unit is configured to utilize the currently integrated three-dimensional digital twin model to analyze and process the park operation data to achieve park optimization.

[0099] In this embodiment, the actual information of the park is collected including:

[0100] Through the Internet of Things platform and at least one of the sensors, monitoring equipment and smart terminals in the park, data including environmental parameters, equipment status parameters, personnel flow, vehicle entry and exit information and business management system data are collected.

[0101] In this embodiment, the preprocessing includes:

[0102] The actual information data collected is cleaned, denoised, and formatted in sequence;

[0103] Perform data compression and encryption on the current data.

[0104] In this embodiment, the basic BIM model is obtained based on at least the following data configuration:

[0105] Building structure, equipment layout, pipeline direction, and material properties.

[0106] In this embodiment, integrating the basic BIM model using the actual information includes:

[0107] Adding additional information layers to the basic BIM model;

[0108] In the information layer, an association relationship is established between the actual information and the equipment in the basic BIM model.

[0109] In this embodiment, the optimization unit is specifically configured as follows:

[0110] In the three-dimensional modeling software, the overall framework of the three-dimensional digital twin model is constructed according to the actual layout and facilities of the park;

[0111] Transfer the current BIM model file to the working directory of the 3D modeling software;

[0112] Open the BIM model file in the 3D modeling software to perform format conversion and parameter adjustment;

[0113] A rendering test is performed on the BIM model file.

[0114] In this embodiment, the device is further used for:

[0115] The three-dimensional digital twin model is optimized to improve the realism and operating efficiency of the model.

[0116] A third embodiment of the present invention is an electronic device, such as Figure 4 As shown, it can be understood as a physical device, including a processor and a memory storing instructions executable by the processor. When the instructions are executed by the processor, the following operations are performed:

[0117] Step S1, collecting actual information of the park and performing preprocessing;

[0118] Step S2, based on the configured basic BIM model, integrating the basic BIM model using the actual information;

[0119] Step S3, constructing a three-dimensional digital twin model, and fusing the integrated BIM model with the three-dimensional digital twin model;

[0120] Step S4, using the currently integrated three-dimensional digital twin model, analyzes and processes the park operation data to achieve park optimization.

[0121] The fourth embodiment of the present invention, the process of the smart park optimization method based on digital twins and BIM in this embodiment is the same as that of the first, second or third embodiment, the difference is that in engineering implementation, this embodiment can be implemented by means of software plus a necessary general hardware platform, of course, it can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the method of the present invention can be embodied in the form of a computer software product, which is stored in a storage medium (such as ROM / RAM, disk, CD), including a number of instructions for a device to execute the method described in the embodiment of the present invention.

[0122] Through the description of the specific implementation methods, a deeper and more specific understanding of the technical means and effects adopted by the present invention to achieve the predetermined purpose should be obtained. However, the accompanying drawings are only for reference and illustration purposes and are not intended to limit the present invention.

Claims

1. A smart park optimization method based on digital twins and BIM, characterized in that: include: Step S1, collecting actual information of the park and performing preprocessing; Step S2, based on the configured basic BIM model, integrating the basic BIM model using the actual information; Step S3, constructing a three-dimensional digital twin model, and fusing the integrated BIM model with the three-dimensional digital twin model; Step S4, using the currently integrated three-dimensional digital twin model, analyzes and processes the park operation data to achieve park optimization.

2. The smart park optimization method based on digital twin and BIM according to claim 1 is characterized in that: The actual information of the park collected includes: Through the Internet of Things platform and at least one of the sensors, monitoring equipment and smart terminals in the park, data including environmental parameters, equipment status parameters, personnel flow, vehicle entry and exit information and business management system data are collected.

3. The smart park optimization method based on digital twin and BIM according to claim 2 is characterized in that: The pre-processing comprises: The actual information data collected is cleaned, denoised, and formatted in sequence; Perform data compression and encryption on the current data.

4. The smart park optimization method based on digital twin and BIM according to claim 1 is characterized in that: The basic BIM model is obtained based on at least the following data configuration: Building structure, equipment layout, pipeline direction, and material properties.

5. The smart park optimization method based on digital twin and BIM according to claim 4 is characterized in that: The integrating the basic BIM model by using the actual information includes: Adding additional information layers to the basic BIM model; In the information layer, an association relationship is established between the actual information and the equipment in the basic BIM model.

6. The smart park optimization method based on digital twin and BIM according to claim 5 is characterized in that: The step S3 specifically includes: In the three-dimensional modeling software, the overall framework of the three-dimensional digital twin model is constructed according to the actual layout and facilities of the park; Transfer the current BIM model file to the working directory of the 3D modeling software; Open the BIM model file in the 3D modeling software to perform format conversion and parameter adjustment; A rendering test is performed on the BIM model file.

7. The smart park optimization method based on digital twin and BIM according to claim 6 is characterized in that: The method further comprises: The three-dimensional digital twin model is optimized to improve the realism and operating efficiency of the model.

8. A smart park optimization device based on digital twin and BIM, characterized in that: include: A collection unit, configured to collect actual information of the park and perform preprocessing; An integration unit configured to integrate the basic BIM model based on the configuration using the actual information; A fusion unit is configured to construct a three-dimensional digital twin model and fuse the integrated BIM model with the three-dimensional digital twin model; The optimization unit is configured to utilize the currently integrated three-dimensional digital twin model to analyze and process the park operation data to achieve park optimization.

9. An electronic device, characterized in that: The electronic device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor. When the computer program is executed by the processor, the steps of the smart park optimization method based on digital twins and BIM are implemented as described in any one of claims 1 to 7.

10. A computer storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the smart park optimization method based on digital twins and BIM as described in any one of claims 1 to 7.

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