Building electromechanical full life cycle management platform based on BIM and optimization strategy thereof

Abstract

The invention discloses a BIM-based building electromechanical full life cycle management platform and an optimization strategy thereof, relates to the technical field of building informatization management, and solves the defects of the existing BIM technology in the aspects of processing efficiency, model precision, data synchronization and change management. According to the invention, a data layer is used for data cleaning and standardization through a machine learning model, and distributed storage is realized through a cloud management system; the model layer constructs and updates a high-precision BIM model; the synchronization collaboration layer ensures the consistency and traceability of data by using a real-time synchronization mechanism and a block chain technology; the decision analysis layer performs data analysis and prediction through a deep learning algorithm; the full life cycle management layer integrates historical data and current data and automatically triggers a management process; according to the method, the data processing efficiency and the model precision are greatly improved, and the seamless sharing capability of information and the transparency of data synchronization and change management are remarkably improved, so that the overall collaborative efficiency and the management level of a project are enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field of building information management, and more specifically to a BIM-based building electromechanical full life cycle management platform and its optimization strategy. Background Art

[0002] With the digital transformation of the construction industry, the building mechanical and electrical full life cycle management platform based on BIM (Building Information Modeling) has gradually become an important means to improve project management efficiency and quality. This platform integrates information from multiple stages such as design, construction, operation and maintenance, realizes seamless connection and sharing of data, and provides strong support for the full life cycle management of building mechanical and electrical systems. Driven by the "new infrastructure" policy, the application of BIM technology in the construction industry continues to deepen, and its advantages in improving project quality and reducing operating costs are becoming increasingly prominent, providing broad space for the development of a BIM-based building mechanical and electrical full life cycle management platform.

[0003] The BIM-based building mechanical and electrical full life cycle management platform integrates a variety of advanced technologies, including 3D modeling, data management, the Internet of Things, and artificial intelligence. The platform uses the BIM model to unify the data of the design, construction, and operation and maintenance stages of the building mechanical and electrical engineering, realizing information sharing and collaborative work. Specifically, the BIM model not only contains the geometric information of the mechanical and electrical equipment, but also contains multi-dimensional data such as the equipment's attribute information, installation location, and maintenance records. Through the cloud platform, all participants can access and update this data in real time, improving the transparency and management efficiency of the project.

[0004] However, in actual applications, BIM technology involves a huge amount of data, including design drawings, construction records, equipment parameters and maintenance history, etc., and it is difficult to achieve seamless sharing and transmission of information; the storage, processing and analysis efficiency of data is not high, resulting in information processing delays, affecting the efficiency of project collaboration; in addition, electromechanical systems usually contain a large number of pipelines, cables, equipment and other elements, and the accurate modeling of these elements requires high-precision data support. However, when dealing with these complex elements, existing BIM software often suffers from precision loss. There are differences in data standards between different manufacturers and different software, which may lead to a decrease in accuracy or incompatible formats of model data during import and export. Secondly, when multiple participants use BIM models at the same time, data synchronization becomes a major challenge. Existing platforms often find it difficult to accurately reflect data changes of all participants in real time. Design changes, equipment replacements and other events often occur in projects, but existing BIM platforms often lack an effective change management mechanism, resulting in the inability to update model data in a timely manner. Summary of the invention

[0005] In view of the shortcomings of the prior art, the present invention discloses a BIM-based building electromechanical full life cycle management platform and its optimization strategy, aiming to solve the problems in the background technology.

[0006] In order to achieve the above technical effects, the present invention adopts the following technical solutions:

[0007] A BIM-based building electromechanical full life cycle management platform, including: data layer, model layer, synchronization and collaboration layer, decision analysis layer and full life cycle management layer;

[0008] The data layer is used to collect at least design drawings, construction records, and equipment parameter data, convert non-standard format data into standard format, and perform data cleaning, denoising, and normalization through a machine learning model; the data layer is also used to perform distributed storage of pre-processed data through a cloud management system, establish a data index and metadata management framework, and output standardized, searchable data sets to the model layer and decision analysis layer;

[0009] The model layer is used to construct and update the BIM model including building structure, electromechanical system and equipment elements based on the output data of the data layer;

[0010] The synchronization and collaboration layer is used to receive the real-time model data output by the model layer, and perform real-time synchronization and conflict detection of model parameter data through the data synchronization mechanism; when the data is changed, the blockchain is used to record the data change history, and the synchronization mechanism is triggered according to the data change, and the changed data is pushed to all relevant participants in real time, and the real-time updated data is output to the decision analysis layer and the full life cycle management layer; at the same time, the change information is fed back to the model layer for updating;

[0011] The decision analysis layer is used to receive the output data of the model layer and the synchronous collaboration layer, perform data analysis, prediction and anomaly detection on the electromechanical system through a comprehensive electromechanical system analysis algorithm based on deep learning, and output analysis reports, early warning information and optimization suggestions to the full life cycle management layer and relevant participants;

[0012] The full life cycle management layer is used to receive the output data of the decision analysis layer, combine the historical data in the data layer, and use the full life cycle fusion algorithm to integrate and manage the full life cycle information; automatically trigger the management process according to the project stage, and output the full life cycle management report, project progress information and BIM model optimization suggestions to relevant participants and decision makers.

[0013] As a further technical solution of the present invention, the working principle of the data layer converting non-standard format data into standard format is as follows: matching specific character sequences in the data through a regular expression-based pattern matching algorithm to identify the format characteristics of the data, and analyzing the words, phrases and sentences in the data through a semantic analysis-based parsing algorithm to further identify the data format type; if the input data format matches any format in a preset standard format library, the identified non-standard data fields are matched with the corresponding fields in the standard format through a format mapping mechanism, and the non-standard data fields are converted one by one into standard format fields through key-value pair mapping and regular expression replacement; if the input data format cannot be directly matched with any item in the standard format library, feature extraction and classification of the input data are performed through clustering analysis to construct a new data format model; after the data format model is successfully constructed, the new data format model is added to the standard format library, and the mapping rules are automatically updated.

[0014] As a further technical solution of the present invention, during the data conversion process, the machine learning model first traverses each data record through an autoencoder to automatically learn and extract effective features in the data, while suppressing noise and redundant information; during the data traversal process, if it is detected that the characteristic value exceeds the preset threshold range, the anomaly detection mechanism is triggered, and the redundant, missing or erroneous information in the abnormal characteristic value is corrected or filled by the statistical outlier detection algorithm; the statistical outlier detection algorithm fills the abnormal characteristic value by the mean, median or interpolation method; then, the machine learning model denoises the data through a generative adversarial network; if the denoised data is normally distributed, the machine learning model uses the Z-score normalization method to convert the data into a standard normal distribution with a mean of 0 and a standard deviation of 1; if the distribution range of the denoised data is within a preset fixed range, the minimum and maximum scaling method is used to linearly map the data to the interval [0,1] or [-1,1].

[0015] As a further technical solution of the present invention, the cloud management system includes a distributed storage module, a data index and metadata management module, a data security module and a data backup module; the distributed storage module includes a data sharding unit and a data synchronization unit; the data sharding unit is used to shard and distribute the data to different storage nodes through a consistent hashing algorithm; the data synchronization unit is used to ensure that the data copies on all storage nodes remain consistent through the Raft consensus algorithm when a data update operation is detected; the data index and metadata management module includes an index construction unit and a metadata management unit; the index construction unit is used to divide the index into multiple small blocks using a block indexing strategy to improve query performance, and calculate the relevance of documents through a vector space model and a TF-IDF algorithm; the metadata management unit is used to dynamically update the metadata model in the graph database when a new data type or data relationship is detected; the data security module is used to reject unauthorized user access requests and record logs through a role-based access control mechanism, and is also used to encrypt sensitive data through an encryption mechanism; the data backup module is used to regularly perform data backup operations through a scheduled task scheduler. During the backup process, the data backup module reduces backup time and storage space through an incremental backup method.

[0016] As a further technical solution of the present invention, the model layer obtains three-dimensional point cloud data through laser scanning, compares and verifies it with the design drawings, and if a difference is detected between the design drawings and the actual scanned data, the model is fine-tuned through the ICP iterative closest point algorithm; when constructing the BIM model, the model layer also assigns parametric attributes to each element in the model through parametric modeling, and if any equipment parameter changes, the relevant geometric shapes and attributes in the model are automatically updated through the parameter update mechanism; during the parameter update process, the parameter update mechanism automatically generates a design plan according to predefined rules through an automated rule engine, and the predefined rules include equipment layout rules and pipeline layout rules; during the parameter update process, the model layer also applies the changed data to the existing BIM model through the model update mechanism; the model update mechanism records the history of each data change through version control Git; during the use of the model, the model layer allows multiple users to edit the same BIM model at the same time through a real-time communication protocol; if multiple users edit the same part of the model at the same time, the conflict is resolved through the synchronous collaboration layer.

[0017] As a further technical solution of the present invention, the working principle of the synchronization collaboration layer is as follows: receiving the real-time model data of the model layer and synchronizing it through a synchronization protocol based on a distributed database; when a data change is detected in the model layer, the change data is processed and distributed through a distributed message queue, and the change information is encapsulated as a block through a blockchain write mechanism, and added to the blockchain after verification by a consensus algorithm. Each block contains the hash value of the previous block to form an unalterable data chain; during the change process, data conflicts are identified through a hash algorithm and data fingerprint, and data conflicts are resolved through a three-way merge algorithm; if the conflict is not resolved, the relevant participants are notified through a real-time communication protocol to manually resolve the conflict and resubmit the change; if it is detected that the data change has been confirmed to be correct, the change data is pushed to all relevant participants in real time through a real-time communication protocol; if it is detected that the data change has been confirmed to be correct and the conflict has been resolved, the real-time updated data is output to the decision analysis layer and the full life cycle management layer through an API interface, and the change information is fed back to the model layer through two-way data binding.

[0018] As a further technical solution of the present invention, the prediction method of the electromechanical system comprehensive analysis algorithm based on deep learning is: based on the input feature vector X t , including historical energy consumption data (E t-n ,...,E t-1 ), weather data (M t-n ,...M t ,) and building occupancy data (O t-n ,...O t ,) and equipment status data (R t-n ,...R t ,), where n represents the lag order of the time series; the energy consumption at future time points is predicted by a deep hybrid neural network, which uses a multi-layer long short-term memory network to extract the input feature vector X t The time series features T in r , and calculate the time series features T through the multi-head self-attention mechanism r Correlation between different time steps, output energy consumption prediction value at future time points The prediction formula is:

[0019]

[0020] In formula (1), W out represents the weight of the output layer; Q, K, V are query, key and value matrices respectively; d k represents the dimension of the key vector; α represents the activation function, which is used to introduce nonlinearity and enhance the expressiveness of the model; L represents the number of heads; LSTM represents the output of the LSTM layer; attn represents the output of the self-attention mechanism; WLSTM and b LSTM Respectively represent the parameters and bias items of the LSTM layer; the anomaly detection method of the electromechanical system comprehensive analysis algorithm based on deep learning is: constructing a fault propagation model through a graph neural network, and the fault propagation model is based on the adjacency matrix A of the fault propagation graph and the device feature matrix X dev As input, the fault propagation mode between devices is learned through multi-layer graph convolution operations, and the fault propagation probability P of each device is calculated. fault , the calculation formula is:

[0021]

[0022] In formula (2), is the weight matrix of the kth neighbor in the lth layer of graph convolution, H (l) Represents the node feature matrix after the l-th layer of graph convolution; Represents the attenuation coefficient of the control space influence range, which is used to determine the influence of spatial distance on weight; represents the graph convolution output feature vector of device d at layer l; X dev represents the device feature matrix; Represents the parameters of the GNN model in the lth layer of graph convolution; in the prediction and anomaly detection process, the deep learning-based electromechanical system comprehensive analysis algorithm compares the calculation results with the preset threshold; if the deviation between the predicted energy consumption and the actual energy consumption exceeds the preset threshold, or the fault propagation probability of a certain equipment exceeds the preset threshold, the early warning mechanism is triggered, and the analysis report and early warning information are output to the full life cycle management and relevant participants.

[0023] As a further technical solution of the present invention, the full life cycle management layer includes an information integration module, a management process trigger module, an intelligent report generation module and an optimization suggestion and feedback module; the information integration module is used to adopt a federated learning method to fuse the output data of the decision analysis layer with the historical data in the data layer, and generate a comprehensive data representation through a graph neural network. The information integration module is also used to build a knowledge base of full life cycle information through a knowledge graph, storing data entities and data relationships; the management process trigger module is used to realize process automation through a business process management system and an event-driven architecture based on the data generated by the information integration module; the intelligent report generation module is used to generate a full life cycle management report through natural language processing and a template engine; the optimization suggestion and feedback module is used to generate optimization suggestions through reinforcement learning and operations research methods.

[0024] As a further technical solution of the present invention, the working method of the full life cycle fusion algorithm is:

[0025] S1. Calculate the weight of the time series data and spatial data of each data sample through the Softmax function;

[0026] S2, applying the calculated weights to the time series data and spatial data, and generating a fused multimodal feature vector by element-by-element multiplication;

[0027] S3, calculate the influence factor of the fusion feature through the nonlinear activation function to determine the importance and contribution of different information sources;

[0028] S4, combining the reduced-dimensional data with historical data, and generating an integrated full-life cycle information matrix using a weighted summation method, which includes all relevant historical and current data;

[0029] S5. Calculate the event triggering condition through linear transformation to determine whether the management process needs to be triggered;

[0030] S6. Compare the calculated event triggering condition with the preset threshold value. If the triggering condition exceeds the threshold value, trigger the corresponding management process; otherwise, do not trigger;

[0031] S7. Estimate the expected reward of taking an action in the current state by recursively calculating the state-action value function, select the action with the highest expected reward as the optimal action, and generate optimization suggestions.

[0032] As a further technical solution of the present invention, a BIM-based building electromechanical full life cycle management optimization strategy includes the following steps:

[0033] Step 1: construct a BIM model of the electromechanical system according to the building design drawings and equipment parameters, wherein the BIM model of the electromechanical system integrates at least equipment operation and maintenance data and energy consumption data information through an API interface;

[0034] Step 2: Based on the data integrated by the BIM model, the operation status of the electromechanical system is analyzed, predicted and abnormalities are detected through the electromechanical system comprehensive analysis algorithm based on deep learning;

[0035] Step 3: Based on the output results of step 2, combined with the project management knowledge base, the whole life cycle information is integrated and managed through the whole life cycle fusion algorithm to generate optimization suggestions or decision support reports, including equipment maintenance plans and energy consumption optimization plans;

[0036] Step 4: In the full life cycle management of electromechanical equipment, blockchain is used to record decision-making and change information in the design, construction, and operation and maintenance stages, and smart contracts are used to automatically execute collaborative workflows, including approval, notification, and feedback, to improve collaborative efficiency and information transparency;

[0037] Step 5: Use blockchain to record the collaborative work process and decision-making results of each participant, and use the real-time data stream processing engine to achieve real-time data synchronization and collaborative work in the design, construction, and operation and maintenance stages;

[0038] Step 6. According to the actual operation of the electromechanical system and the optimization decision support report, dynamically adjust equipment parameters and maintenance plans, optimize system performance and reduce operation and maintenance costs, and continuously analyze data through the particle swarm optimization algorithm to continuously iterate and optimize the BIM model and management strategy.

[0039] Based on the above technical solutions, the positive and beneficial effects of the present invention are:

[0040] 1. The data layer of the present invention converts non-standard data into standard format by introducing machine learning models for data cleaning, denoising and normalization, greatly improving the availability and consistency of data. At the same time, the distributed storage and metadata management framework of the cloud management system enables standardized and retrievable data sets to be efficiently shared and transferred between different participants. This solution not only solves the problem of sharing and transferring difficulties caused by the huge amount of data, but also improves the efficiency and quality of data processing, providing strong support for the collaborative management of projects.

[0041] 2. The data layer of the present invention realizes distributed storage of data through the cloud management system, and utilizes the powerful computing power of cloud computing to significantly improve the speed of data storage and processing. At the same time, the application of machine learning models makes data cleaning, denoising and normalization processing more efficient, providing a reliable foundation for subsequent data analysis. In addition, the decision analysis layer can timely discover potential problems and risks through real-time data analysis and prediction, and put forward optimization suggestions to help the project team make more informed decisions; this solution not only improves the efficiency of data storage and processing, but also makes data analysis more accurate and timely, providing strong data support for project decision-making.

[0042] 3. The model layer of the present invention is based on the output data of the data layer, and can build and update a high-precision BIM model. The accuracy and integrity of the model data are ensured through real-time update and synchronization mechanisms. At the same time, the model layer also has the ability to finely model complex elements, avoiding the loss of accuracy or format incompatibility that may occur during the import and export process. This solution not only improves the accuracy and availability of the BIM model, but also provides a reliable foundation for the subsequent management and maintenance of the project. The full life cycle management layer integrates historical data and current data through a full life cycle fusion algorithm to generate a comprehensive full life cycle management report to support effective management and decision-making at all stages of the project. This real-time change management and full life cycle perspective not only improves the flexibility and adaptability of the project, but also ensures the continuous optimization and improvement of the project throughout its life cycle.

[0043] 4. The synchronization and collaboration layer of the present invention records the data change history through the data synchronization mechanism and blockchain, realizing real-time data synchronization and conflict detection among multiple participants. When the data changes, the synchronization mechanism triggers an update and pushes the changed data to all relevant participants in real time. At the same time, the application of blockchain technology ensures the traceability and non-tamperability of the data change history. This solution not only solves the challenge of data synchronization among multiple participants, but also improves the real-time and consistency of model data, providing a strong guarantee for the collaborative management of the project. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative work, among which:

[0045] Figure 1 This is an architecture diagram of a BIM-based building electromechanical full life cycle management platform of the present invention;

[0046] Figure 2 A working principle diagram of the data layer of the present invention for converting non-standard format data into standard format;

[0047] Figure 3 A working principle diagram of the machine learning model of the present invention;

[0048] Figure 4 A working method diagram of the full life cycle fusion algorithm of the present invention;

[0049] Figure 5 This is a flowchart of the steps of the BIM-based building mechanical and electrical full life cycle management optimization strategy of the present invention. DETAILED DESCRIPTION

[0050] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0051] like Figure 1 As shown, a BIM-based building electromechanical full life cycle management platform includes: data layer, model layer, synchronization and collaboration layer, decision analysis layer and full life cycle management layer;

[0052] The data layer is used to collect at least design drawings, construction records, and equipment parameter data, convert non-standard format data into standard format, and perform data cleaning, denoising, and normalization through machine learning models; the data layer is also used to perform distributed storage of pre-processed data through a cloud management system, establish a data index and metadata management framework, and output standardized, retrievable data sets to the model layer and decision analysis layer; wherein, if Figure 2As shown: the working principle of the data layer converting non-standard format data into standard format is: matching the specific character sequence in the data through the pattern matching algorithm based on regular expressions, identifying the format characteristics of the data, and analyzing the words, phrases and sentences in the data through the parsing algorithm based on semantic analysis, and further identifying the data format type; if the input data format matches any format in the preset standard format library, the identified non-standard data field is matched with the corresponding field in the standard format through the format mapping mechanism, and the non-standard data field is converted into the standard format field one by one through the key-value pair mapping and regular expression replacement; if the input data format cannot directly match any item in the standard format library, the input data is feature extracted and classified through cluster analysis to build a new data format model; after the data format model is successfully built, the new data format model is added to the standard format library, and the mapping rules are automatically updated; and then the non-standard data fields are converted into standard format fields one by one through the format mapping mechanism, key-value pair mapping and regular expressions; in a specific embodiment, regular expressions are a powerful text processing tool that can define and match complex character patterns. Through regular expressions, specific character sequences such as dates, timestamps, numbers, etc. can be identified. Regular expressions provide rich syntax and support various complex matching rules. Semantic analysis is a branch of natural language processing (NLP) that aims to understand the meaning of text. Through the analysis of vocabulary, phrases and sentences, semantic analysis can extract deeper information, such as the contextual relationship of data, semantic structure, etc. In the present invention, semantic analysis is used to further identify the format type of data. For example, by analyzing keywords and phrases in the data, the system can determine the type of data (such as equipment parameters, construction records, etc.). Semantic analysis can also handle fuzzy and non-standard data descriptions and improve the accuracy of format recognition. Combined with the preliminary matching of regular expressions, semantic analysis ensures comprehensive and accurate recognition of data formats. The format mapping mechanism is a technology that matches non-standard format data fields with standard format fields. Through the preset standard format library, the system can find the correspondence between non-standard data fields and standard format fields and convert them. Key-value pair mapping is a method for mapping non-standard data fields with standard format fields, and a one-to-one correspondence is established in the form of key-value pairs. Regular expression replacement is used to find and replace specific patterns in text. Cluster analysis is an unsupervised learning method that divides data into different categories by calculating the similarities between data points. Cluster analysis can automatically discover patterns in data and classify them. The construction of a new data format model is the result of cluster analysis. The system generates a new data format description based on the clustering results. The update of the standard format library is to add the new data format model to the existing standard format library and automatically update the mapping rules.

[0053] In practical applications, the working mode of the data layer reflects the efficiency and flexibility of its structural characteristics. First, through the regular expression pattern matching algorithm, the data layer can quickly identify and extract specific format features in the data, such as dates, numbers, text, etc. Then, the semantic analysis and parsing algorithm conducts in-depth analysis of the extracted data, and further determines the format type of the data based on the structure of vocabulary, phrases and sentences. Once the format type of the data is determined, the format mapping mechanism is activated to match the non-standard data fields with the corresponding fields in the standard format. If the match is successful, the non-standard data fields are converted to standard format fields through key-value pair mapping and regular expression replacement; if the match fails, cluster analysis is initiated to extract and classify the input data, build a new data format model, and automatically update the mapping rules. This working method not only improves the efficiency of data processing, but also enhances the adaptability and flexibility of the data format.

[0054] Compared with the prior art, the data layer of the present invention has the following advantages and characteristics in converting non-standard format data into standard format: First, by comprehensively using advanced data processing technologies such as regular expression pattern matching, semantic analysis and parsing, and format mapping mechanism, accurate identification and efficient conversion of data formats are achieved; second, through the clustering analysis algorithm and the function of automatically updating mapping rules, dynamic learning and adaptation of data formats are achieved, and the flexibility and adaptability of data processing are improved; finally, the working mode and logical operation process of the data layer embody high efficiency and accuracy, and can quickly convert non-standard format data into standard format data, providing reliable data support for subsequent processing layers. These advantages and characteristics make the data layer of the present invention have important application value in the BIM-based building electromechanical full life cycle management platform.

[0055] Further, such as Figure 3As shown: During the data conversion process, the machine learning model first traverses each data record through the autoencoder, automatically learns and extracts effective features in the data, and suppresses noise and redundant information; during the data traversal process, if the feature value is detected to exceed the preset threshold range, the abnormal detection mechanism is triggered, and the redundant, missing or erroneous information in the abnormal feature value is corrected or filled by the statistical outlier detection algorithm; the statistical outlier detection algorithm fills the abnormal feature value by the mean, median or interpolation method; then, the machine learning model denoises the data through the generative adversarial network; if the denoised data is normally distributed, the machine learning model uses the Z-score standardization method to convert the data into a standard normal distribution with a mean of 0 and a standard deviation of 1; if the distribution range of the denoised data is within a preset fixed range, the minimum and maximum scaling method is used to linearly map the data to the interval [0,1] or [-1,1]. In the specific implementation, the autoencoder is a neural network structure that compresses the input data into a low-dimensional space (encoding process) and then reconstructs the original data from the low-dimensional space (decoding process) to learn the effective feature representation of the data. In the present invention, the autoencoder is used to traverse each data record, automatically learn and extract effective features from data such as design drawings, construction records, equipment parameters, etc., while suppressing noise and redundant information. This process effectively reduces the data dimension, improves the efficiency of data processing, and retains the key information of the data, providing a more accurate data basis for subsequent data cleaning and anomaly detection. An outlier refers to a value that deviates from most data points in a data set, which may be caused by erroneous records, equipment failures, or abnormal events. In the present invention, a statistical outlier detection algorithm (such as mean, median, or interpolation method) is used to identify and correct these abnormal feature values. When the feature value extracted by the autoencoder exceeds the preset threshold range, the anomaly detection mechanism is triggered, and the algorithm selects a suitable statistical method for correction or filling according to the data distribution characteristics. This step ensures the accuracy and consistency of the data and avoids the interference of abnormal data on subsequent analysis results. Generative adversarial networks GANs consist of two networks, a generator and a discriminator, which generate realistic data samples through competitive learning. In the present invention, GANs are used for data denoising, that is, the generator tries to generate data samples that are similar to the original data but with less noise, while the discriminator tries to distinguish between real data and generated data. Through continuous iterative training, the generator can learn the true distribution of the data and generate high-quality denoised data. This process significantly improves the signal-to-noise ratio of the data and provides a clearer data foundation for subsequent data standardization processing. Z-score standardization converts data into a standard normal distribution with a mean of 0 and a standard deviation of 1, which is suitable for situations where the data distribution is unknown or there are extreme values.The minimum and maximum scaling linearly maps the data to a specified interval (such as [0,1] or [-1,1]), which is suitable for situations where the data distribution range is known and the relative size relationship of the data needs to be maintained. In the present invention, these two normalization methods are used to process the denoised data to ensure the consistency and comparability of the data in subsequent analysis. Z-score normalization helps to eliminate dimensional differences between data, so that different features have the same weight in the model; while the minimum and maximum scaling ensures the distribution of data within a specific range, which is convenient for model training and prediction.

[0056] In actual operation, the data layer uses the cloud management system to achieve distributed storage and management of data, and establishes a data index and metadata management framework to facilitate rapid retrieval and access to data. At the same time, the data layer also provides a rich data preprocessing tool and algorithm library to support users to customize the data processing process design according to actual needs. This highly flexible and scalable working method enables machine learning models to efficiently process large-scale, multi-source heterogeneous building electromechanical data, providing strong data support for subsequent model construction and decision analysis.

[0057] Compared with the prior art, the advantages of the present invention in introducing machine learning models for data preprocessing in the BIM-based building electromechanical full life cycle management platform include: through the application of advanced technologies such as autoencoders and GANs, the present invention realizes efficient feature extraction, noise suppression and denoising of data, and significantly improves the efficiency and quality of data processing. The statistical outlier detection algorithm can accurately identify and correct outliers in the data, ensure the integrity and accuracy of the data, and avoid the interference of abnormal data on subsequent analysis results. According to the distribution of the data, a suitable standardization method (Z-score standardization or minimum and maximum scaling) is selected to ensure the consistency and comparability of the data in subsequent analysis, and enhance the generalization ability and prediction accuracy of the model. The data layer realizes distributed storage and management of data through the cloud management system, provides a wealth of data preprocessing tools and algorithm libraries, supports users to customize the data processing process design according to actual needs, and realizes the automation and intelligence of data processing. In addition, the design of the present invention fully considers the scalability and customizability of the system, supports users to expand and customize functions according to actual needs, and meets the data processing needs in different scenarios.

[0058] In a specific embodiment, the hardware working environment for data cleaning, denoising and normalization processing of the machine learning model mainly includes: high-performance servers for running machine learning algorithms and storing large data sets; graphics processing units (GPUs); storage devices; network interfaces and power supply systems; a group of high-performance servers and GPUs are selected as hardware platforms, and machine learning models (Group A) and traditional processing methods (Group B) are respectively applied for data cleaning, denoising and normalization processing. The traditional processing method uses rule-based data cleaning and manual denoising, as well as manually set normalization rules. The experimental data table is shown in Table 1:

[0059] Table 1 Comparative experimental records

[0060]

[0061] Through comparative experiments, it can be seen that Group A has significantly shortened the data cleaning time, has better denoising effect, and has higher normalization accuracy. This shows that machine learning models can process large amounts of data more quickly and accurately, improve data quality, and provide more reliable data support for the BIM-based building mechanical and electrical full life cycle management platform. Therefore, in practical applications, using machine learning models for data preprocessing will help improve the overall performance and efficiency of the platform.

[0062] Furthermore, the cloud management system includes a distributed storage module, a data index and metadata management module, a data security module and a data backup module; the distributed storage module includes a data sharding unit and a data synchronization unit; the data sharding unit is used to shard and distribute the data to different storage nodes through a consistent hashing algorithm; the data synchronization unit is used to ensure that the data copies on all storage nodes remain consistent through the Raft consensus algorithm when a data update operation is detected; the data index and metadata management module includes an index construction unit and a metadata management unit; the index construction unit is used to divide the index into multiple small blocks using a block indexing strategy to improve query performance, and calculate the relevance of documents through a vector space model and a TF-IDF algorithm; the metadata management unit is used to dynamically update the metadata model in the graph database when a new data type or data relationship is detected; the data security module is used to reject unauthorized user access requests and record logs through a role-based access control mechanism, and is also used to encrypt sensitive data through an encryption mechanism; the data backup module is used to regularly perform data backup operations through a scheduled task scheduler. During the backup process, the data backup module reduces backup time and storage space through an incremental backup method.

[0063] Among them, the consistent hashing algorithm is a hashing algorithm for distributed systems, which achieves uniform data sharding by mapping data to a fixed annular space and evenly distributing nodes on this ring. When the number of nodes changes, the consistent hashing algorithm can minimize the amount of data migration and ensure the stability and efficiency of the system. Applied in the present invention, the data sharding unit shards and distributes the data to different storage nodes through the consistent hashing algorithm. This sharding method not only improves the storage efficiency of data, but also reduces the load of a single node. The consistent hashing algorithm is combined with the data synchronization unit to ensure the uniform distribution of data on different nodes. For example, when processing a large number of design drawings, the consistent hashing algorithm can evenly distribute these drawings on multiple storage nodes to avoid the problem of overloading a single node. The Raft consensus algorithm is a consistency protocol used in distributed systems, which ensures that the data copies of all nodes are consistent by electing a leader and replicating logs. The Raft algorithm simplifies the complexity of the distributed system through a simple state machine model and clear role definition. Applied in the present invention, the data synchronization unit ensures that the data copies on all storage nodes are consistent through the Raft consensus algorithm. This synchronization mechanism ensures the consistency and reliability of data. When a data update operation is detected, the Raft algorithm will automatically synchronize and update to all nodes to ensure that the data copies of all nodes remain consistent. The block index strategy is a method of dividing the index into multiple small blocks to improve query performance. By dividing the index into multiple small blocks, the size of the index can be reduced and the query speed can be accelerated. The block index strategy is combined with the vector space model and the TF-IDF algorithm to further improve the relevance and accuracy of the query. For example, when querying specific equipment parameters, the block index strategy can quickly locate the relevant data blocks, while the vector space model and the TF-IDF algorithm calculate the relevance of the document to ensure the accuracy and relevance of the query results. The vector space model is a method of representing documents as multi-dimensional vectors, with each dimension representing a term. The TF-IDF (Term Frequency-Inverse Document Frequency) algorithm is used to calculate the importance of a term in a document. The higher the TF-IDF value, the greater the importance of the term in the document. When searching for specific design drawings or construction records, the vector space model and the TF-IDF algorithm can quickly calculate the importance of related documents, thereby providing more accurate query results. A graph database is a database that stores data in a graph structure, which represents entities and their relationships through nodes and edges. The dynamic metadata model can automatically update the metadata model in the graph database when new data types or data relationships are detected. Role-based access control (RBAC) is a security management mechanism that controls access to resources by defining user roles and permissions.The RBAC mechanism manages user access rights through a role-authority mapping table. The encryption mechanism protects the security of data during transmission and storage by encrypting the data. Common encryption algorithms include AES, RSA, etc. As used in the present invention, the data security module encrypts sensitive data through the encryption mechanism AES to ensure the security of data during transmission and storage. The scheduled task scheduler is used to perform data backup operations regularly to ensure the reliability and integrity of the data. The incremental backup method only backs up data that has changed since the last backup, thereby reducing the backup time and storage space requirements.

[0064] The model layer is used to construct and update the BIM model including building structure, electromechanical system and equipment elements based on the output data of the data layer; the model layer obtains three-dimensional point cloud data through laser scanning, compares and verifies it with the design drawings, and if it is detected that there is a difference between the design drawings and the actual scanned data, the model is fine-tuned through the ICP iterative closest point algorithm; when constructing the BIM model, the model layer also assigns parametric properties to each element in the model through parametric modeling, and if any equipment parameter changes, the relevant geometric shapes and properties in the model are automatically updated through the parameter update mechanism; during the parameter update process, the parameter update mechanism automatically generates a design plan according to predefined rules through an automated rule engine, and the predefined rules include equipment layout rules and pipeline layout rules; during the parameter update process, the model layer also applies the changed data to the existing BIM model through the model update mechanism; the model update mechanism records the history of each data change through the version control Git; during the use of the model, the model layer allows multiple users to edit the same BIM model at the same time through a real-time communication protocol; if multiple users edit the same part of the model at the same time, the conflict is resolved through the synchronization collaboration layer.

[0065] In a specific embodiment, laser scanning is a high-precision spatial measurement technology that generates three-dimensional point cloud data by emitting a laser beam and receiving the reflected signal. These point cloud data can accurately represent the geometric shape and position of the actual object. In the application of the present invention, the model layer obtains the three-dimensional point cloud data of the scene through laser scanning, and compares and verifies it with the design drawings. This comparison process ensures the consistency between the design drawings and the actual situation, and improves the accuracy and reliability of the model. Laser scanning is combined with the ICP iterative closest point algorithm to further improve the accuracy of the model. For example, when a difference is detected between the design drawings and the actual scan data, the ICP algorithm can fine-tune the model to ensure that the model is highly consistent with the actual environment. The ICP (Iterative Closest Point) algorithm is an algorithm for point cloud registration, which finds the best alignment between two groups of point clouds in an iterative manner. The ICP algorithm gradually adjusts the position and posture of the target point cloud to align it with the reference point cloud by minimizing the distance error between the point clouds. In the application of the present invention, when a difference is detected between the design drawings and the actual scan data, the ICP algorithm can fine-tune the model to ensure that the model is highly consistent with the actual environment. This fine-tuning process not only improves the accuracy of the model, but also reduces the workload of manual adjustments. Parametric modeling is a method of describing the geometry and properties of a model by defining parameters. Parametric modeling allows each element in the model to be controlled by parameters, allowing for flexible design and modification. Parametric modeling combined with an automated rule engine enables efficient parameter updates and design solution generation. For example, when equipment parameters change, parametric modeling can automatically update the geometry and properties in the model, while the automated rule engine automatically generates new design solutions based on predefined rules to ensure design consistency and rationality.

[0066] Compared with the prior art, the model layer of the present invention can obtain high-precision three-dimensional point cloud data through laser scanning and point cloud data processing, and compare and verify it with the design drawings. This comparison process ensures the consistency between the design drawings and the actual situation, and improves the accuracy and reliability of the model. Compared with the traditional manual measurement and modeling methods, laser scanning and point cloud data processing can significantly improve the accuracy and efficiency of the model. In addition, through the ICP iterative closest point algorithm, the present invention can fine-tune the model when a difference is detected between the design drawings and the actual scanned data, ensuring that the model is highly consistent with the actual environment. This fine-tuning process not only improves the accuracy of the model, but also reduces the workload of manual adjustment. Compared with the traditional manual adjustment method, the ICP algorithm can complete the fine-tuning of the model more quickly and accurately. Through parametric modeling and automated rule engines, the present invention can achieve efficient parameter updating and design scheme generation. Parametric modeling allows each element in the model to be controlled by parameters, thereby achieving flexible design and modification. The automated rule engine automatically generates new design schemes according to predefined rules to ensure the consistency and rationality of the design. Compared with traditional manual design change methods, this parametric modeling and automated rules engine can significantly improve the efficiency and quality of design changes.

[0067] The synchronization and collaboration layer is used to receive the real-time model data output by the model layer, and perform real-time synchronization and conflict detection of model parameter data through the data synchronization mechanism; when the data is changed, the blockchain is used to record the data change history, and the synchronization mechanism is triggered according to the data change, and the changed data is pushed to all relevant participants in real time, and the real-time updated data is output to the decision analysis layer and the full life cycle management layer; at the same time, the change information is fed back to the model layer for updating; the working principle of the synchronization and collaboration layer is as follows: receiving the real-time model data of the model layer, and synchronizing it through the synchronization protocol based on the distributed database; when the data change in the model layer is detected, the changed data is processed and distributed through the distributed message queue, and the blockchain is written to the machine The change information is encapsulated into blocks, which are added to the blockchain after verification using a consensus algorithm. Each block contains the hash value of the previous block to form an unalterable data chain. During the change process, data conflicts are identified through a hash algorithm and data fingerprint, and resolved through a three-way merge algorithm. If the conflict is not resolved, the relevant parties are notified through a real-time communication protocol to manually resolve the conflict and resubmit the change. If it is detected that the data change has been confirmed to be correct, the change data is pushed to all relevant parties in real time through a real-time communication protocol. If it is detected that the data change has been confirmed to be correct and the conflict has been resolved, the real-time updated data is output to the decision analysis layer and the full life cycle management layer through the API interface, and the change information is fed back to the model layer through two-way data binding.

[0068] In a specific implementation, a distributed database synchronization protocol is a protocol for ensuring data consistency between multiple nodes. It enables data of different nodes to be synchronized by defining a set of rules and mechanisms. Common distributed database synchronization protocols include Raft, Paxos, etc. Applied in the present invention, when data changes are detected in the model layer, the distributed database synchronization protocol can ensure data consistency of all nodes, while the distributed message queue is responsible for processing and distributing the changed data. A distributed message queue is a middleware for processing and distributing messages. It achieves asynchronous communication by storing messages in a queue and consuming them on demand by consumers. Common distributed message queues include Kafka, RabbitMQ, etc. Applied in the present invention, when data changes are detected in the model layer, the changed data is processed and distributed through a distributed message queue. This mechanism not only improves the efficiency of data processing, but also reduces the coupling between systems.

[0069] The blockchain writing mechanism is a technology for recording and verifying data changes. It encapsulates the change information into blocks and adds them to the blockchain after verification using a consensus algorithm. Each block contains the hash value of the previous block to form an unalterable data chain. In the present invention, when a data change is detected in the model layer, the change information is encapsulated into blocks through the blockchain writing mechanism, and added to the blockchain after verification using a consensus algorithm. This mechanism ensures the transparency and unalterability of the changed data. The hash algorithm is an algorithm that converts data of any length into a hash value of a fixed length. The data fingerprint is a unique identifier obtained by hashing the data. The hash algorithm is used in combination with the data fingerprint to quickly identify data changes and conflicts. In the present invention, when a data change is detected in the model layer, the data conflict is identified by the hash algorithm and the data fingerprint. This mechanism ensures the consistency and integrity of the data. The hash algorithm is combined with the data fingerprint and the three-way merge algorithm to effectively solve the problem of data conflict. For example, when a data conflict is detected, the hash algorithm and the data fingerprint can quickly identify the conflicting part, and the three-way merge algorithm is used to resolve the conflict and ensure the consistency of the data. The three-way merge algorithm is an algorithm for resolving data conflicts. It compares three versions of data (baseline version, local version and remote version), finds differences and automatically merges them. Applied in the present invention, when data conflicts are detected, the data conflicts are resolved through a three-way merge algorithm. This mechanism ensures the consistency and integrity of the data. Two-way data binding is a technology used to achieve data synchronization. It establishes a two-way connection between the data source and the view so that changes in the data source can be reflected in the view in real time, and vice versa. Applied in the present invention, when it is detected that the data change has been confirmed to be correct and the conflict has been resolved, the real-time updated data is output to the decision analysis layer and the full life cycle management layer through the API interface, and the change information is fed back to the model layer through two-way data binding. This mechanism ensures the consistency and real-time nature of the data.

[0070] Compared with the prior art, the present invention can achieve efficient data synchronization through a distributed database synchronization protocol and a distributed message queue. The distributed database synchronization protocol ensures the data consistency of all participants, while the distributed message queue improves the efficiency of data processing and the decoupling of the system. Compared with the traditional centralized synchronization mechanism, this distributed synchronization mechanism can better cope with the needs of large-scale data processing and improve the stability and scalability of the system. Through the blockchain writing mechanism, the present invention can record and verify data changes to ensure the transparency and non-tamperability of the changed data. The introduction of blockchain technology not only improves the security of data, but also enhances the traceability of data. Compared with the traditional logging mechanism, the blockchain writing mechanism can provide higher data security and credibility. Through the hash algorithm, data fingerprint and three-way merge algorithm, the present invention can intelligently detect and resolve data conflicts. The hash algorithm and data fingerprint can quickly identify data conflicts, while the three-way merge algorithm can automatically or assist in manually resolving conflicts. Compared with the traditional manual conflict resolution method, this intelligent data conflict detection and resolution mechanism can significantly improve the efficiency and accuracy of conflict resolution. Through the real-time communication protocol and two-way data binding, the present invention can achieve real-time data push and feedback. The real-time communication protocol ensures timely notification of data changes, while the two-way data binding ensures data consistency and real-time performance. Compared with the traditional batch processing mode, this real-time data push and feedback mechanism can better support multi-user collaboration and improve team work efficiency.

[0071] The decision analysis layer is used to receive the output data of the model layer and the synchronous coordination layer, perform data analysis, prediction and abnormality detection on the electromechanical system through the electromechanical system comprehensive analysis algorithm based on deep learning, and output analysis reports, early warning information and optimization suggestions to the full life cycle management layer and relevant participants; the prediction method of the electromechanical system comprehensive analysis algorithm based on deep learning is: based on the input feature vector X t , including historical energy consumption data (E t-n ,...,E t-1 ), weather data (M t-n ,...M t ,) and building occupancy data (O t-n ,...O t ,) and equipment status data (R t-n ,...R t ,), where n represents the lag order of the time series; the energy consumption at future time points is predicted by a deep hybrid neural network, which uses a multi-layer long short-term memory network to extract the input feature vector X t The time series features T in r , and calculate the time series features T through the multi-head self-attention mechanism rCorrelation between different time steps, output energy consumption prediction value at future time points The prediction formula is:

[0072]

[0073] In formula (1), W out represents the weight of the output layer; Q, K, V are query, key and value matrices respectively; d k represents the dimension of the key vector; α represents the activation function, which is used to introduce nonlinearity and enhance the expressiveness of the model; L represents the number of heads; LSTM represents the output of the LSTM layer; attn represents the output of the self-attention mechanism; W LSTM and b LSTM Respectively represent the parameters and bias items of the LSTM layer; the anomaly detection method of the electromechanical system comprehensive analysis algorithm based on deep learning is: constructing a fault propagation model through a graph neural network, and the fault propagation model is based on the adjacency matrix A of the fault propagation graph and the device feature matrix X dev As input, the fault propagation mode between devices is learned through multi-layer graph convolution operations, and the fault propagation probability P of each device is calculated. fault , the calculation formula is:

[0074]

[0075] In formula (2), is the weight matrix of the kth neighbor in the lth layer of graph convolution, H (l) Represents the node feature matrix after the l-th layer of graph convolution; Represents the attenuation coefficient of the control space influence range, which is used to determine the influence of spatial distance on weight; represents the graph convolution output feature vector of device d at layer l; X dev represents the device feature matrix; Represents the parameters of the GNN model in the lth layer of graph convolution; in the prediction and anomaly detection process, the deep learning-based electromechanical system comprehensive analysis algorithm compares the calculation results with the preset threshold; if the deviation between the predicted energy consumption and the actual energy consumption exceeds the preset threshold, or the fault propagation probability of a certain equipment exceeds the preset threshold, the early warning mechanism is triggered, and the analysis report and early warning information are output to the full life cycle management and relevant participants.

[0076] In a specific embodiment, LSTM is used to predict energy consumption at future time points. By combining multiple layers of LSTM, complex time series patterns can be better captured and prediction accuracy can be improved. LSTM is combined with a multi-head self-attention mechanism to further improve the accuracy and robustness of the prediction. For example, when predicting energy consumption at future time points, LSTM can capture long-term trends and periodic changes in the time series, while the multi-head self-attention mechanism can focus on the importance of different time steps, thereby improving the accuracy of the prediction. The multi-head self-attention mechanism is a technology for enhancing the model's attention to different parts of the input sequence. It extracts information from different subspaces through multiple parallel self-attention mechanisms and weightedly combines this information. This mechanism enables the model to better capture important features in the input sequence. In the present invention, the multi-head self-attention mechanism is used to enhance the performance of LSTM in time series prediction. Through the multi-head self-attention mechanism, the model can better focus on the importance of different time steps, thereby improving the accuracy of the prediction. Graph neural network is a neural network used to process graph structured data. It transmits information through the connection relationship between nodes and learns the representation of nodes through multi-layer graph convolution operations. GNN can effectively capture local and global features in the graph structure, and is suitable for modeling the relationship between devices and fault propagation analysis. In the present invention, GNN is used to build a fault propagation model. Through multi-layer graph convolution operations, GNN can learn the propagation mode of faults between devices and calculate the fault propagation probability of each device. GNN combined with multi-layer graph convolution operations improves the accuracy and interpretability of the fault propagation model. For example, when building a fault propagation model, GNN can learn the fault propagation mode between devices through multi-layer graph convolution operations, thereby calculating the fault propagation probability of each device. This mechanism not only improves the accuracy of fault detection, but also provides a visual explanation of fault propagation between devices. The fault propagation model is a model for describing and analyzing the propagation of faults between devices. It represents the connection relationship between devices through a graph structure, and learns the propagation mode of faults through a graph neural network. In the present invention, the fault propagation model learns the propagation mode of faults between devices through multi-layer graph convolution operations, and calculates the fault propagation probability of each device. This model can help identify the source of faults and propagation paths, and provide a basis for fault prevention and maintenance. The fault propagation model is combined with the graph neural network to improve the ability of fault detection and prevention. For example, when detecting equipment failures, the fault propagation model can learn the fault propagation pattern through the graph neural network and calculate the fault propagation probability of each device. This mechanism not only improves the accuracy of fault detection, but also provides a visual explanation of fault propagation between devices.

[0077] In actual work, the deep learning-based electromechanical system comprehensive analysis algorithm realizes data analysis, prediction and anomaly detection of electromechanical systems through the following specific implementation methods:

[0078] The decision analysis layer receives output data from the model layer and the synchronization and coordination layer, including the real-time status, historical data, and change information of the electromechanical system. These data are pre-processed, including data cleaning, denoising, and normalization steps, to ensure data quality and consistency.

[0079] The decision analysis layer predicts the energy consumption of the electromechanical system through multi-layer LSTM and multi-head self-attention mechanism. Multi-layer LSTM captures long-term dependencies in the time series, while the multi-head self-attention mechanism focuses on the importance of different time steps. During the model training process, historical energy consumption data is used as input to predict energy consumption at future time points. The predicted results are compared with the actual energy consumption. If the deviation exceeds the preset threshold, the early warning mechanism is triggered.

[0080] Through the graph neural network, the decision analysis layer builds a fault propagation model. First, the devices in the electromechanical system are represented as nodes in the graph structure, and the connection relationship between devices is represented as edges. Then, through multi-layer graph convolution operations, the fault propagation mode between devices is learned. During the model training process, historical fault data is used as input to calculate the fault propagation probability of each device. If the fault propagation probability of a device exceeds the preset threshold, the early warning mechanism is triggered.

[0081] During the prediction and anomaly detection process, the decision analysis layer compares the calculated results with the preset thresholds. If the deviation between the predicted energy consumption and the actual energy consumption exceeds the preset threshold, or the probability of fault propagation of a certain device exceeds the preset threshold, the system will trigger the early warning mechanism. The early warning mechanism sends analysis reports and early warning information to the full life cycle management and relevant participants through real-time communication protocols. At the same time, the system will record the history of early warning events for subsequent analysis and improvement.

[0082] Based on the prediction results and anomaly detection results, the decision analysis layer generates optimization suggestions. The optimization suggestions include energy consumption optimization measures, fault prevention measures and maintenance plans. These optimization suggestions are output to the full life cycle management and relevant participants through the API interface to help them make more scientific decisions.

[0083] Compared with the existing technology, the combination of deep hybrid neural network and multi-head self-attention mechanism can achieve accurate prediction of future energy consumption. Compared with the traditional time series prediction method, the prediction accuracy is significantly improved, providing a more reliable basis for energy management. In addition, the fault propagation model is constructed using graph neural network, which can accurately capture the propagation mode of faults between devices and calculate the fault propagation probability of each device. This helps to timely discover potential fault risks and improve the stability and reliability of the system. Secondly, by setting reasonable thresholds, real-time monitoring and early warning of the status of the electromechanical system are achieved. When the system is abnormal, analysis reports and early warning information can be generated in time to provide timely decision support for managers. The entire algorithm is data-driven and can make full use of the real-time monitoring data and historical data of the electromechanical system to improve the scientific and intelligent level of management. At the same time, the algorithm has good scalability and flexibility, and can adapt to building electromechanical systems of different sizes and types. At the same time, with the continuous accumulation of data, the performance of the algorithm can be continuously optimized and improved.

[0084] In specific implementation, the hardware working environment of the deep learning-based electromechanical system comprehensive analysis algorithm applied to the BIM-based building electromechanical full life cycle management platform mainly includes: server: used to run deep learning models and BIM platforms, with powerful computing power and large memory; graphic workstation: equipped with high-performance graphics card for rendering and visualization of BIM models; data storage devices: such as SSD and NAS, used to store data of BIM models and deep learning models; sensors and Internet of Things devices: used to monitor the operating status of electromechanical systems in real time and provide data input; network communication equipment: such as switches and routers, to ensure real-time transmission and sharing of data.

[0085] A group of hardware was selected to compare the electromechanical system comprehensive analysis algorithm based on deep learning (Group A) and the traditional method (Group B). The traditional method mainly analyzes and diagnoses the electromechanical system based on rules and experience, such as fault warning through threshold judgment and expert system. The experiment was carried out under the same conditions, and each group conducted five experiments to record the fault diagnosis accuracy, analysis time and resource consumption indicators of the electromechanical system. The experimental record data table is shown in Table 2:

[0086] Table 2 Algorithm comparison experiment record table

[0087]

[0088] Through comparative experiments, we found that the fault diagnosis accuracy of the electromechanical system comprehensive analysis algorithm based on deep learning (Group A) is significantly higher than that of the traditional method (Group B), while the analysis time is shorter and the resource consumption is lower. This shows that the deep learning algorithm has high efficiency and accuracy in the comprehensive analysis of electromechanical systems, which helps to improve the intelligence level of the building electromechanical full life cycle management platform.

[0089] The full life cycle management layer is used to receive the output data of the decision analysis layer, and integrate and manage the full life cycle information by using the full life cycle fusion algorithm in combination with the historical data in the data layer; automatically trigger the management process according to the project stage, and output the full life cycle management report, project progress information and BIM model optimization suggestions to relevant participants and decision makers. The full life cycle management layer includes an information integration module, a management process triggering module, an intelligent report generation module and an optimization suggestion and feedback module; the information integration module is used to fuse the output data of the decision analysis layer with the historical data in the data layer by using the federated learning method, and generate a comprehensive data representation through the graph neural network. The information integration module is also used to build a knowledge base of the full life cycle information through the knowledge graph, and store data entities and data relationships; the management process triggering module is used to realize process automation through the business process management system and event-driven architecture based on the data generated by the information integration module; the intelligent report generation module is used to generate a full life cycle management report through natural language processing and template engine; the optimization suggestion and feedback module is used to generate optimization suggestions through reinforcement learning and operations research methods. In the full life cycle management layer, if the output data of the decision analysis layer is received, these data are fused with the historical data in the data layer through the federated learning algorithm. Federated learning allows joint training of models without sharing the original data, thereby protecting data privacy. At the same time, graph neural networks are used to capture complex relationships between data and generate more comprehensive data representations. If it is detected that there is a correlation between the data but the format is inconsistent, the graph neural network is used to align and transform the features so that the data can be represented uniformly. Next, the information management unit performs information management and knowledge discovery through the knowledge graph and semantic reasoning engine. If the data fusion unit generates a unified data representation, a knowledge base of the entire life cycle information is constructed through the knowledge graph. The knowledge graph not only stores data entities and their relationships, but also performs logical inferences through the semantic reasoning engine to discover implicit relationships and patterns. If a new data type or data relationship is detected, the knowledge graph is dynamically updated to maintain the integrity and consistency of the information. In addition, new knowledge and insights can be automatically generated through the semantic reasoning engine to provide valuable insights for decision makers.

[0090] In the automatic management process trigger module, if the data generated by the full life cycle information integration module is received, the business processes of each stage of the project are defined and managed through the BPM system. The BPM system automatically triggers the corresponding management process according to the preset rules and conditions. If a specific event is detected (such as a change in the project stage, completion of a key node, etc.), the corresponding process task is triggered through EDA. For example, if the design stage is detected to be completed, the construction preparation process is automatically triggered; if the construction stage is detected to be completed, the acceptance process is automatically triggered. The task scheduling unit optimizes the task execution order through the DAG scheduling algorithm to ensure that tasks are executed in the correct order and maximize resource utilization.

[0091] If a full life cycle management report needs to be generated, key information and data points are extracted through NLP technology, and a structured report is generated through the template engine. NLP technology can automatically identify and extract important information from text, such as project progress, cost analysis, risk assessment, etc. The template engine fills the extracted information into the report according to the preset template format. If new report requirements or format changes are detected, the template is dynamically adjusted to adapt to different reporting requirements. The visualization unit generates interactive charts and dashboards through data visualization tools to help decision makers quickly understand project status and trends.

[0092] If optimization suggestions need to be generated, specific optimization strategies are generated through reinforcement learning algorithms and operations research methods. If changes in project status or new constraints are detected, the optimization strategy is recalculated to adapt to the new situation. The feedback collection unit collects user feedback through feedback collection mechanisms (such as questionnaires, user feedback systems). If it is detected that the user has submitted feedback, the feedback content is analyzed through natural language processing technology to extract key information. If problems or suggestions mentioned in the feedback are detected, corresponding improvement measures are triggered to improve system performance and user experience.

[0093] Further, such as Figure 4As shown: The working method of the full life cycle fusion algorithm is as follows: S1. Calculate the weights of the time series data and spatial data of each data sample through the Softmax function; S2. Apply the calculated weights to the time series data and spatial data, and generate the fused multimodal feature vector through element-by-element multiplication; S3. Calculate the influence factor of the fused feature through a nonlinear activation function to determine the importance and contribution of different information sources; S4. Combine the reduced-dimensional data with the historical data, and generate an integrated full life cycle information matrix through a weighted summation method, which contains all relevant historical and current data; S5. Calculate the event trigger condition through linear transformation to determine whether the management process needs to be triggered; S6. Compare the calculated event trigger condition with the preset threshold. If the trigger condition exceeds the threshold, the corresponding management process is triggered; otherwise, it is not triggered; S7. Estimate the expected reward of taking an action in the current state by recursively calculating the state-action value function, and select the action with the highest expected reward as the optimal action to generate optimization suggestions.

[0094] In a specific embodiment, the Softmax function is used to calculate the weights of the time series data and spatial data of each data sample. Through the Softmax function, the importance of different types of data can be determined, thereby providing a basis for subsequent data fusion. The Softmax function is combined with element-by-element multiplication to generate a fused multimodal feature vector. For example, after calculating the weights of the time series data and the spatial data, the Softmax function can apply these weights to the original data and generate a fused multimodal feature vector through element-by-element multiplication. Element-by-element multiplication is used to apply the calculated weights to the time series data and the spatial data to generate a fused multimodal feature vector. Through element-by-element multiplication, different types of feature data can be weighted and combined to form a more representative feature vector. After calculating the influencing factors of the fused features, the nonlinear activation function can enhance the expressive power of the feature vector, while the weighted summation method combines the reduced-dimensional data with the historical data to generate an integrated full life cycle information matrix. The weighted summation method is combined with a linear transformation to determine whether a management process needs to be triggered. For example, after generating an integrated full life cycle information matrix, a linear transformation can calculate event trigger conditions to determine whether a management process needs to be triggered. Recursively calculating the state-action value function is a method that estimates the expected reward of taking an action in the current state through recursive calculation. It is usually used in reinforcement learning to update the state-action value function through recursive calculation to select the optimal action. In the present invention, the recursive calculation of the state-action value function is used to estimate the expected reward of taking an action in the current state, and select the action with the highest expected reward as the optimal action to generate optimization suggestions. By recursively calculating the state-action value function, the optimal management strategy can be found to improve management efficiency.

[0095] In actual work, the working method of the full life cycle fusion algorithm is as follows: receive the output data from the decision analysis layer, including energy consumption prediction results, fault detection results and optimization suggestions. At the same time, obtain historical data from the data layer, such as design drawings, construction records and equipment parameters. These data are preliminarily processed, including data cleaning, denoising and normalization steps to ensure the quality and consistency of the data. Then, the multidimensional vector is converted into a probability distribution through the Softmax function to determine the importance of different types of data. For example, when processing energy consumption prediction data, the Softmax function can calculate the weights of time series data and spatial data to determine the relative importance of each type of data. Third, when generating the fused multimodal feature vector, element-by-element multiplication can weightedly combine the time series data and spatial data according to the weights. Fourth, when calculating the influencing factors of the fused features, the nonlinear activation function is used to enhance the expressive power of the feature vector so that it can better reflect the importance and contribution of different information sources. Fifth, when generating the integrated full life cycle information matrix, the weighted summation method is used to weight the reduced dimensionality data and historical data according to the weights. Sixth, when calculating event trigger conditions, the full life cycle information matrix can be mapped to a simple trigger condition through linear transformation. Seventh, when judging whether a management process needs to be triggered, the system will compare the calculated trigger condition with the preset threshold. If the trigger condition exceeds the threshold, the corresponding management process will be automatically triggered. When generating optimization suggestions, the state action value function is recursively calculated to select the action with the highest expected reward and generate the corresponding optimization suggestions.

[0096] In the specific implementation, the hardware working environment of the full life cycle fusion algorithm includes: server, BIM model server, data acquisition equipment, visualization terminal equipment, network communication equipment; a group of identical hardware environments are selected to apply the full life cycle fusion algorithm (Group A) and traditional methods (Group B, i.e. existing commonly used algorithms, such as threshold monitoring, trend analysis, statistical control and expert systems, etc.) for comparative experiments. During the experiment, both groups received the same electromechanical system operating status data and processed and analyzed them under the same experimental conditions. The experiment recorded three key indicators: data processing time, accuracy and decision-making efficiency to evaluate the performance of the two methods; among them, the traditional method is based on a series of preset rules and simple mathematical models to monitor and analyze the operating status of building electromechanical systems. These methods include:

[0097] Threshold monitoring: Set upper and lower limits for key parameters and trigger alarms when data exceeds these thresholds.

[0098] Trend analysis: Observe the performance trends of electromechanical systems over time by plotting charts of historical data.

[0099] Statistical control: Use statistical methods (such as mean, standard deviation, etc.) to evaluate the stability of system performance.

[0100] Expert system: Provides decision support through rule-based reasoning based on expert knowledge and experience.

[0101] The experimental results are recorded in tabular form, as shown in Table 3:

[0102] Table 3. Comparative experimental records of the full life cycle fusion algorithm

[0103]

[0104] Through comparative experiments, it can be seen that the full life cycle fusion algorithm is significantly superior to traditional methods (existing commonly used algorithms) in terms of data processing time, accuracy and decision-making efficiency. The average data processing time of Group A is only about 120 seconds, which is much lower than the 300 seconds of Group B. At the same time, the accuracy of Group A is as high as more than 97%, which can more accurately reflect the true state of the electromechanical system. In addition, the decision-making efficiency of Group A is also significantly higher than that of Group B, and it can provide effective decision support faster. Therefore, it can be concluded that the full life cycle fusion algorithm has significant advantages in the BIM-based building electromechanical full life cycle management platform, which can significantly improve the efficiency and accuracy of data processing, and provide more powerful and intelligent support for project management and decision-making.

[0105] A BIM-based optimization strategy for building mechanical and electrical full life cycle management, such as Figure 5 As shown: the following steps are included: Step 1, constructing a BIM model of the electromechanical system according to the architectural design drawings and equipment parameters, wherein the BIM model of the electromechanical system integrates at least equipment operation and maintenance data and energy consumption data information through an API interface;

[0106] Step 2: Based on the data integrated by the BIM model, the operation status of the electromechanical system is analyzed, predicted and abnormalities are detected through the electromechanical system comprehensive analysis algorithm based on deep learning;

[0107] Step 3: Based on the output results of step 2, combined with the project management knowledge base, the whole life cycle information is integrated and managed through the whole life cycle fusion algorithm to generate optimization suggestions or decision support reports, including equipment maintenance plans and energy consumption optimization plans;

[0108] Step 4: In the whole life cycle management of electromechanical equipment, the blockchain is used to record the decision-making and change information in the design, construction, and operation and maintenance stages, and the collaborative workflow, including approval, notification, and feedback, is automatically executed through smart contracts to improve collaborative efficiency and information transparency;

[0109] Step 5: Use blockchain to record the collaborative work process and decision-making results of each participant, and use the real-time data stream processing engine to achieve real-time data synchronization and collaborative work in the design, construction, and operation and maintenance stages;

[0110] Step 6. According to the actual operation of the electromechanical system and the optimization decision support report, dynamically adjust equipment parameters and maintenance plans, optimize system performance and reduce operation and maintenance costs, and continuously analyze data through the particle swarm optimization algorithm to continuously iterate and optimize the BIM model and management strategy.

[0111] Although the specific embodiments of the present invention are described above, it should be understood by those skilled in the art that these specific embodiments are only illustrative, and those skilled in the art may omit, replace, and change the details of the above methods and systems in various ways without departing from the principles and essence of the present invention. For example, merging the above method steps so as to perform substantially the same functions in substantially the same manner to achieve substantially the same results is within the scope of the present invention. Therefore, the scope of the present invention is limited only by the appended claims.

Claims

1. A BIM-based building electromechanical full life cycle management platform, characterized in that: include: Data layer, model layer, synchronization and collaboration layer, decision analysis layer and full life cycle management layer; The data layer is used to collect at least design drawings, construction records, and equipment parameter data, convert non-standard format data into standard format, and perform data cleaning, denoising, and normalization through a machine learning model; the data layer is also used to perform distributed storage of pre-processed data through a cloud management system, establish a data index and metadata management framework, and output standardized, searchable data sets to the model layer and decision analysis layer; The model layer is used to construct and update the BIM model including building structure, electromechanical system and equipment elements based on the output data of the data layer; The synchronization and collaboration layer is used to receive the real-time model data output by the model layer, and perform real-time synchronization and conflict detection of model parameter data through the data synchronization mechanism; when the data is changed, the blockchain is used to record the data change history, and the synchronization mechanism is triggered according to the data change, and the changed data is pushed to all relevant participants in real time, and the real-time updated data is output to the decision analysis layer and the full life cycle management layer; at the same time, the change information is fed back to the model layer for updating; The decision analysis layer is used to receive the output data of the model layer and the synchronous collaboration layer, perform data analysis, prediction and anomaly detection on the electromechanical system through a comprehensive electromechanical system analysis algorithm based on deep learning, and output analysis reports, early warning information and optimization suggestions to the full life cycle management layer and relevant participants; The full life cycle management layer is used to receive the output data of the decision analysis layer, combine the historical data in the data layer, and use the full life cycle fusion algorithm to integrate and manage the full life cycle information; automatically trigger the management process according to the project stage, and output the full life cycle management report, project progress information and BIM model optimization suggestions to relevant participants and decision makers.

2. A BIM-based building electromechanical full life cycle management platform according to claim 1, characterized in that: The working principle of the data layer for converting non-standard format data into standard format is as follows: matching specific character sequences in the data through a pattern matching algorithm based on regular expressions to identify the format characteristics of the data, and analyzing the words, phrases and sentences in the data through a parsing algorithm based on semantic analysis to further identify the data format type; If the input data format matches any format in the preset standard format library, the identified non-standard data fields are matched with the corresponding fields in the standard format through the format mapping mechanism, and the non-standard data fields are converted into standard format fields one by one through key-value pair mapping and regular expression replacement; if the input data format cannot be directly matched with any item in the standard format library, the input data is feature extracted and classified through clustering analysis to build a new data format model; after the data format model is successfully built, the new data format model is added to the standard format library and the mapping rules are automatically updated.

3. The BIM-based building electromechanical full life cycle management platform according to claim 1 is characterized by: During the data conversion process, the machine learning model first traverses each data record through an autoencoder to automatically learn and extract effective features in the data, while suppressing noise and redundant information; during the data traversal process, if it is detected that the feature value exceeds the preset threshold range, the anomaly detection mechanism is triggered, and the redundant, missing or erroneous information in the abnormal feature value is corrected or filled by the statistical outlier detection algorithm; the statistical outlier detection algorithm fills the abnormal feature value by the mean, median or interpolation method; then, the machine learning model denoises the data through a generative adversarial network; if the denoised data is normally distributed, the machine learning model uses the Z-score normalization method to convert the data into a standard normal distribution with a mean of 0 and a standard deviation of 1; if the distribution range of the denoised data is within a preset fixed range, the minimum and maximum scaling method is used to linearly map the data to the interval [0,1] or [-1,1].

4. According to the BIM-based building electromechanical full life cycle management platform of claim 1, it is characterized by: The cloud management system includes a distributed storage module, a data index and metadata management module, a data security module and a data backup module; the distributed storage module includes a data sharding unit and a data synchronization unit; the data sharding unit is used to shard and distribute data to different storage nodes through a consistent hashing algorithm; the data synchronization unit is used to ensure that the data copies on all storage nodes remain consistent through the Raft consensus algorithm when a data update operation is detected; the data index and metadata management module includes an index construction unit and a metadata management unit; the index construction unit is used to divide the index into multiple small blocks using a block index strategy to improve query performance, and calculate the relevance of documents through a vector space model and a TF-IDF algorithm; the metadata management unit is used to dynamically update the metadata model in the graph database when a new data type or data relationship is detected; the data security module is used to reject unauthorized user access requests and record logs through a role-based access control mechanism, and is also used to encrypt sensitive data through an encryption mechanism; the data backup module is used to regularly perform data backup operations through a scheduled task scheduler. During the backup process, the data backup module reduces backup time and storage space through an incremental backup method.

5. The BIM-based building electromechanical full life cycle management platform according to claim 1 is characterized by: The model layer obtains three-dimensional point cloud data through laser scanning, compares and verifies it with the design drawings, and if a difference is detected between the design drawings and the actual scanned data, the model is fine-tuned through the ICP iterative closest point algorithm; When constructing a BIM model, the model layer also assigns parametric properties to each element in the model through parametric modeling. If any equipment parameter changes, the relevant geometric shapes and attribute parameters in the model are automatically updated through the parameter update mechanism. During the update process, the parameter update mechanism automatically generates a design plan according to predefined rules through an automated rule engine. The predefined rules include equipment layout rules and pipeline layout rules. During the parameter update process, the model layer also applies the changed data to the existing BIM model through the model update mechanism. The model update mechanism records the history of each data change through version control Git. During the use of the model, the model layer allows multiple users to edit the same BIM model at the same time through a real-time communication protocol; if multiple users edit the same part of the model at the same time, the conflict is resolved through the synchronization collaboration layer.

6. The BIM-based building electromechanical full life cycle management platform according to claim 1 is characterized by: The working principle of the synchronization and collaboration layer is as follows: receiving the real-time model data of the model layer and synchronizing it through a synchronization protocol based on a distributed database; when detecting data changes in the model layer, processing and distributing the changed data through a distributed message queue, and encapsulating the changed information into blocks through a blockchain writing mechanism, adding them to the blockchain after verification using a consensus algorithm, and each block contains the hash value of the previous block, forming an unalterable data chain; During the change process, data conflicts are identified through hash algorithms and data fingerprints, and resolved through a three-way merge algorithm; If the conflict is not resolved, the relevant participants are notified through the real-time communication protocol to manually resolve the conflict and resubmit the change. If it is detected that the data change has been confirmed to be correct, the changed data is pushed to all relevant participants in real time through the real-time communication protocol; If it is detected that the data change has been confirmed and the conflict has been resolved, the real-time updated data will be output to the decision analysis layer and the full life cycle management layer through the API interface, and the change information will be fed back to the model layer through two-way data binding.

7. The BIM-based building electromechanical full life cycle management platform according to claim 1 is characterized by: The prediction method of the electromechanical system comprehensive analysis algorithm based on deep learning is: based on the input feature vector X t , including historical energy consumption data (E t-n， ..., E t-1 ), weather data (M t-n， ...M t ,) building occupancy data (O t-n , ... t ,) and equipment status data (R t-n , ... R t ,), where n represents the lag order of the time series; the energy consumption at future time points is predicted by a deep hybrid neural network, which uses a multi-layer long short-term memory network to extract the input feature vector X t The time series features T in r , and calculate the time series features T through the multi-head self-attention mechanism r Correlation between different time steps, output energy consumption prediction value at future time points The prediction formula is: In formula (1), W out represents the weight of the output layer; Q, K, V are query, key and value matrices respectively; d k represents the dimension of the key vector; α represents the activation function, which is used to introduce nonlinearity and enhance the expressiveness of the model; L represents the number of heads; LSTM represents the output of the LSTM layer; attn represents the output of the self-attention mechanism; W LSTM and b LSTM Respectively represent the parameters and bias items of the LSTM layer; the anomaly detection method of the electromechanical system comprehensive analysis algorithm based on deep learning is: constructing a fault propagation model through a graph neural network, and the fault propagation model is based on the adjacency matrix A of the fault propagation graph and the device feature matrix X dev As input, the fault propagation mode between devices is learned through multi-layer graph convolution operations, and the fault propagation probability P of each device is calculated. fault , the calculation formula is: In formula (2), is the weight matrix of the kth neighbor in the lth layer of graph convolution, H (l) Represents the node feature matrix after the l-th layer of graph convolution Represents the attenuation coefficient of the control space influence range, which is used to determine the influence of spatial distance on weight; represents the graph convolution output feature vector of device d at layer l; X dev represents the device feature matrix; Represents the parameters of the GNN model in the lth layer of graph convolution; in the prediction and anomaly detection process, the deep learning-based electromechanical system comprehensive analysis algorithm compares the calculation results with the preset threshold; if the deviation between the predicted energy consumption and the actual energy consumption exceeds the preset threshold, or the fault propagation probability of a certain equipment exceeds the preset threshold, the early warning mechanism is triggered, and the analysis report and early warning information are output to the full life cycle management and relevant participants.

8. The BIM-based building electromechanical full life cycle management platform according to claim 1 is characterized by: The full life cycle management layer includes an information integration module, a management process trigger module, an intelligent report generation module and an optimization suggestion and feedback module; the information integration module is used to adopt a federated learning method to fuse the output data of the decision analysis layer with the historical data in the data layer, and generate a comprehensive data representation through a graph neural network. The information integration module is also used to build a knowledge base of full life cycle information through a knowledge graph, and store data entities and data relationships; the management process trigger module is used to realize process automation through a business process management system and an event-driven architecture based on the data generated by the information integration module; the intelligent report generation module is used to generate a full life cycle management report through natural language processing and a template engine; the optimization suggestion and feedback module is used to generate optimization suggestions through reinforcement learning and operations research methods.

9. The BIM-based building electromechanical full life cycle management platform according to claim 1 is characterized by: The working method of the full life cycle fusion algorithm is: S1. Calculate the weight of the time series data and spatial data of each data sample through the Softmax function; S2, applying the calculated weights to the time series data and spatial data, and generating a fused multimodal feature vector by element-by-element multiplication; S3, calculate the influence factor of the fusion feature through the nonlinear activation function to determine the importance and contribution of different information sources; S4, combining the reduced-dimensional data with historical data, and generating an integrated full-life cycle information matrix using a weighted summation method, which includes all relevant historical and current data; S5. Calculate the event triggering condition through linear transformation to determine whether the management process needs to be triggered; S6. Compare the calculated event triggering condition with the preset threshold value, and if the triggering condition exceeds the threshold value, trigger the corresponding management process; Otherwise, it is not triggered; S7. Estimate the expected reward of taking an action in the current state by recursively calculating the state-action value function, select the action with the highest expected reward as the optimal action, and generate optimization suggestions.

10. A BIM-based building electromechanical full life cycle management optimization strategy, characterized by: A BIM-based building electromechanical full life cycle management platform as described in any one of claims 1 to 9 comprises the following steps: Step 1: construct a BIM model of the electromechanical system according to the building design drawings and equipment parameters, wherein the BIM model of the electromechanical system integrates at least equipment operation and maintenance data and energy consumption data information through an API interface; Step 2: Based on the data integrated by the BIM model, the operation status of the electromechanical system is analyzed, predicted and abnormalities are detected through the electromechanical system comprehensive analysis algorithm based on deep learning; Step 3: Based on the output results of step 2, combined with the project management knowledge base, the whole life cycle information is integrated and managed through the whole life cycle fusion algorithm to generate optimization suggestions or decision support reports, including equipment maintenance plans and energy consumption optimization plans; Step 4: In the whole life cycle management of electromechanical equipment, the blockchain is used to record the decision-making and change information in the design, construction, and operation and maintenance stages, and the collaborative workflow, including approval, notification, and feedback, is automatically executed through smart contracts to improve collaborative efficiency and information transparency; Step 5: Use blockchain to record the collaborative work process and decision-making results of each participant, and use the real-time data stream processing engine to achieve real-time data synchronization and collaborative work in the design, construction, and operation and maintenance stages; Step 6. According to the actual operation of the electromechanical system and the optimization decision support report, dynamically adjust equipment parameters and maintenance plans, optimize system performance and reduce operation and maintenance costs, and continuously analyze data through the particle swarm optimization algorithm to continuously iterate and optimize the BIM model and management strategy.

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