A method and system for engineering supervision data management based on deep learning

Abstract

The present invention relates to the field of risk analysis technology, specifically a method and system for engineering supervision data management based on deep learning, comprising the following steps: obtaining an engineering project data set, obtaining the project name, location, start time, and supervision team information based on the engineering project data set, generating a project metadata archive, performing time series analysis on the project metadata archive, identifying key time nodes, and generating time monitoring data. In the present invention, by integrating deep learning with graph neural network technology, the accuracy of data processing and risk prediction is improved. The graph feature vectors extracted by the graph neural network quantify the risk contribution of each entity, making risk management more dynamic and real-time, thereby ensuring the transparency and responsiveness of the supervision process.

Description

Technical Field

[0001] The present invention relates to the technical field of risk analysis, and in particular to an engineering supervision data management method and system based on deep learning. Background Art

[0002] Risk analysis is a crucial technique in the field of engineering supervision, focusing on identifying, evaluating and managing potential risks that may be encountered during the implementation of construction projects.

[0003] In existing engineering supervision, risk analysis often relies on static data processing methods, lacking the ability to dynamically analyze complex data. This makes it difficult to effectively predict and manage potential risks. Furthermore, existing technologies lack automation and intelligence, making the risk identification and assessment process cumbersome and time-consuming, making it difficult to support efficient risk decision-making. These technical limitations are particularly pronounced when dealing with large-scale or highly dynamic engineering projects, often leading to delayed risk response measures and impacting overall project progress and quality assurance. Summary of the Invention

[0004] The purpose of the present invention is to address the shortcomings of the prior art and to propose a method and system for engineering supervision data management based on deep learning.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: a method for engineering supervision data management based on deep learning, comprising the following steps:

[0006] Obtaining a project data set, obtaining the project name, location, start time, and supervision team information from the project data set, generating a project metadata file, performing time series analysis on the project metadata file, identifying key time nodes, and generating time monitoring data;

[0007] Applying a deep learning model to the time monitoring data to analyze the supervision regulations, construction standards, and safety regulations in the text to generate an entity recognition profile, and using the entity recognition profile to refine the logical relationships and interactions between entities to construct an entity relationship model;

[0008] Apply graph neural networks to the entity relationship model for feature learning, extract the interaction features and risk-related attributes between entities, generate graph feature vectors, determine the contribution of each entity to the project risk based on the graph feature vectors, and establish a risk assessment model;

[0009] The output of the risk assessment model is compared with industry standards, the risk deviation of each entity is calculated, the supervision risk level is determined, and the knowledge graph is adjusted according to the supervision risk level to reflect the current supervision data and risk relationship, generating an updated supervision graph.

[0010] Preferably, the steps for acquiring the time monitoring data are:

[0011] Obtain project name, location, start time, and supervision team information based on the engineering project dataset to generate preliminary project metadata archives;

[0012] Based on the preliminary project metadata archive, a time series analysis was conducted to identify key time nodes. The calculation formula is:

[0013]

[0014] Among them, T k Indicates the key time node, t i represents the date or time of the i-th time point, Z represents the total number of time points, and i is the index of the time point;

[0015] Based on the key time nodes, time monitoring data is generated.

[0016] Preferably, the steps for obtaining the entity identification file are:

[0017] Processing the time monitoring data using a deep learning model to extract text content associated with supervision regulations, construction standards, and safety regulations to generate a set of text feature vectors;

[0018] According to the text feature vector set, entity recognition is performed and the entity matching score is calculated. The calculation formula is:

[0019]

[0020] Among them, E m represents the entity matching score in the text, α k is the feature weight, s k is the score of the kth text feature, K is the total number of features;

[0021] Based on the entity matching scores, an entity recognition profile is organized and formed.

[0022] Preferably, the steps of obtaining the entity relationship model are:

[0023] Extracting identified entities from the entity recognition archive to form an entity list;

[0024] Analyzing the occurrence frequency and contextual dependencies of each entity in the entity list to identify interaction patterns between entities;

[0025] According to the interaction pattern, an entity relationship model is constructed to map and record the logical relationships and interactions between entities.

[0026] Preferably, the steps of obtaining the graph feature vector are:

[0027] Inputting the entity relationship model into a graph neural network for feature learning;

[0028] Execute the forward propagation and feature extraction process of the graph neural network to calculate the neighborhood aggregation information of each entity node. The calculation formula is:

[0029]

[0030] Among them, F i represents the aggregated feature vector of node i, N(i) represents the set of adjacent nodes of node i, and f j is the eigenvector of adjacent node j, W is the transformation matrix;

[0031] Based on the aggregated feature vector, the last layer of the network is used to extract the interaction features and risk association attributes between entities to generate a graph feature vector for each entity.

[0032] Preferably, the steps for obtaining the risk assessment model are:

[0033] Collect the graph feature vector of each entity, analyze the graph feature vector of each entity, and calculate the contribution of the graph feature vector to the engineering risk. The calculation formula is:

[0034]

[0035] Among them, R i Indicates the contribution of entity i to engineering risk, g h is the importance coefficient of feature h, X ih is the value of entity i on feature h, and H is the total number of features;

[0036] A risk assessment model is established based on the contribution degree.

[0037] Preferably, the steps for obtaining the updated supervision map are:

[0038] Comparing the output of the risk assessment model with industry standards, quantifying the risk level of each entity, and calculating the risk deviation of each entity;

[0039] Determine the supervision risk level based on the risk deviation of each entity;

[0040] Based on the supervision risk level, the existing knowledge graph is adjusted and updated, the relationships and interactions between entities and the connections between entities and risk factors are redefined, and an updated supervision graph is generated.

[0041] The present invention provides a project supervision data management system, comprising:

[0042] The data collection module collects project name, location, start time and supervision team information, and generates a project metadata file; performs time series analysis on the project metadata file, identifies key time nodes, and obtains time monitoring data;

[0043] An entity relationship construction module utilizes the time monitoring data and applies a deep learning model to analyze supervision regulations, construction standards, and safety procedures; generates an entity recognition file through text analysis, refines the logical relationships between entities through the entity recognition file, constructs an entity relationship model, and obtains the entity interaction feature model;

[0044] The risk assessment module, based on the entity interaction feature model, uses a graph neural network to learn the interaction features between entities, extracts risk-related attributes, constructs a graph feature vector, evaluates the risk contribution of each entity based on the graph feature vector, establishes a risk assessment model, compares the output of the risk assessment model with the industry standard, calculates the risk deviation of each entity, and determines the supervision risk level.

[0045] Compared with the prior art, the advantages and positive effects of the present invention are:

[0046] This invention improves the accuracy of data processing and risk prediction by integrating deep learning and graph neural network technologies. Time series analysis helps identify potential project risks and delays in advance, while the deep learning model's efficient parsing of supervision regulations and safety procedures allows for a clear mapping of complex logical relationships within data, thereby optimizing the decision support system. Furthermore, the graph feature vectors extracted through the graph neural network quantify the risk contribution of each entity, making risk management more dynamic and real-time, thereby ensuring transparency and responsiveness in the supervision process. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 Schematic diagram of the steps of the present invention. DETAILED DESCRIPTION

[0048] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0049] See also Figure 1 The present invention provides a technical solution, a method for engineering supervision data management based on deep learning, comprising the following steps:

[0050] Select the engineering project data set, obtain the project name, location, start time and supervision team information, generate the project metadata file, perform time series analysis on the project metadata file, identify key time nodes, and generate time monitoring data;

[0051] Apply deep learning models to time monitoring data, analyze supervision regulations, construction standards, and safety procedures in the text, generate entity recognition files, use the entity recognition files to refine the logical relationships and interactions between entities, and build an entity relationship model;

[0052] Apply graph neural networks to learn features of entity-relationship models, extract interaction features and risk-related attributes between entities, generate graph feature vectors, determine the contribution of each entity to project risk based on the graph feature vectors, and establish a risk assessment model.

[0053] The output of the risk assessment model is compared with industry standards, the risk deviation of each entity is calculated, the supervision risk level is determined, and the knowledge graph is adjusted according to the supervision risk level to reflect the current supervision data and risk relationship, generating an updated supervision graph.

[0054] The steps for obtaining time monitoring data are as follows:

[0055] Obtain project name, location, start time, and supervision team information based on the engineering project dataset to generate preliminary project metadata files;

[0056] Based on the preliminary project metadata archive, a time series analysis was conducted to identify key time nodes. The calculation formula is:

[0057]

[0058] Among them, T k Indicates the key time node, t i represents the date or time of the i-th time point, Z represents the total number of time points, and i is the index of the time point;

[0059] Generate time monitoring data based on key time nodes.

[0060] Specifically, relevant information is selected from the engineering project data set. First, the project name, location, start time and supervision team information are obtained through database query and data screening process. The data fields involved in this process include but are not limited to project number, geographic location code, planned start date and team member list. These data are efficiently retrieved through structured query language to ensure the accuracy and real-time nature of the acquired data. Then, these data are combined into a project metadata file to ensure that the metadata file contains complete project description information and the configuration details of its supervision team. This is crucial for subsequent project supervision and data analysis, and a preliminary project metadata file is generated.

[0061] formula The benefit of is that by multiplying each point in the time series by its sequence position and averaging it, the time points can be effectively weighted, so that the time points in the later stages of the sequence have a greater impact on the calculation of the key time nodes, which reflects the cumulative effect of time and the increase in criticality in the progress of the project. The parameter T k It is obtained by calculating the weighted average of the time points in the time series and their sequence positions; the parameter t i It is obtained from the date or time of each time point recorded in chronological order from the actual project data; the parameter i is the index of the time point in the time series, obtained from the beginning of the project to the current timing position; the parameter Z is the total number of time points, obtained by counting the total number of data points contained in the entire time series.

[0062] Calculation process: Specific data: t i =[1,2,3,4,5], sequence position i = [1,2,3,4,5], total number Z = 5; the calculation process is as follows:

[0063]

[0064] The result shows that the key time node is 11, which means that in the time series, the average time node position calculated by weight is 11, which points to a key stage or milestone in the project.

[0065] Based on the identified key time nodes, time monitoring data is generated. The process includes extracting data from the key time node archives and applying data conversion and processing technology to convert these time points into monitoring indicators. These indicators reflect the progress of the project from its initiation to the current state. Through continuous monitoring of these time points, the project status and the completion of key dependencies can be tracked in real time to ensure the real-time and effectiveness of the supervision work, thereby forming a time monitoring data archive. These archives will provide support for project management and ensure that supervision activities can be adjusted according to the actual project progress.

[0066] The steps to obtain the entity recognition file are:

[0067] Use deep learning models to process time monitoring data, extract text content related to supervision regulations, construction standards, and safety procedures, and generate a set of text feature vectors;

[0068] According to the text feature vector set, entity recognition is performed and the entity matching score is calculated. The calculation formula is:

[0069]

[0070] Among them, E mrepresents the entity matching score in the text, α k is the feature weight, s k is the score of the kth text feature, K is the total number of features;

[0071] Based on the entity matching scores, organize and form entity recognition files.

[0072] Specifically, a deep learning model is used to process time monitoring data, and automatic content extraction is performed on text data in engineering supervision projects, such as supervision regulations, construction standards, and safety regulations. This process relies on the model's pre-trained word embedding and context capture capabilities. The deep learning model uses a multi-layer neural network structure to identify and parse relevant text content. The model first converts the text input into a numerical feature vector. This step is achieved through an embedding layer, which maps each word to a vector of a fixed size. The numerical value in the vector is obtained through training. These vectors will serve as input to the neural network. The network captures deep semantic relationships and patterns in the text through layer-by-layer transmission and nonlinear transformation of activation functions to generate a preliminary set of text feature vectors. The entire process is automatically executed and relies on a large amount of supervision-related text data for training to ensure that the model can accurately process and parse professional texts in the field of engineering supervision.

[0073] formula The calculation process:

[0074] First, define the feature weight α k is the importance index of each feature, these weights are learned through the previous training data set, and the feature score s k is the actual observed value of each feature in the current document. For example, a feature may be the TF-IDF value of a word or phrase in the document. The square of the score of each feature is calculated to amplify the impact on the result, and then multiplied by the corresponding weight. This operation is performed for all features and summed up all items. Finally, the square root is taken to get the entity matching score E. m ,This score is used to quantify the degree of match between a text entity and ,supervision regulations, construction standards and safety procedures.

[0075] There are three features with weights of 0.5, 0.3, and 0.2, and feature scores of 1, 2, and 3, respectively. The calculation process is as follows:

[0076]

[0077] The results show that the score E m =1.87 indicates that the entities obtained from the extracted text features have a high degree of matching with the supervision regulations and construction standards. m The value represents a stronger association and consistency, which further guides the generation of entity recognition profiles.

[0078] Based on the entity matching score, the entity recognition archive is organized and formed. This process involves linking each identified entity with its corresponding regulations, standards or procedures, and recording the relationship model of each entity and its interaction in detail. This step not only includes the entity score E m After comparison and confirmation with existing entities in the database, manual verification and adjustment are required to ensure the accuracy and practicality of the file. The generated entity identification file will be used for subsequent supervision review and quality control to ensure that all construction activities are carried out in accordance with relevant regulations and standards, and provide accurate guidance and reference for the supervision team.

[0079] The steps to obtain the entity relationship model are:

[0080] Extract identified entities from the entity recognition archive to form an entity list;

[0081] Analyze the frequency of occurrence and contextual dependencies of each entity in the entity list to identify interaction patterns between entities;

[0082] Based on the interaction pattern, an entity relationship model is constructed to map and record the logical relationships and interactions between entities.

[0083] Specifically, data collected from entity identification archives, including text information on supervision regulations, construction standards, and safety regulations, is used as the starting point for entity analysis. The data is initially screened and classified to accurately identify each independent entity. Each entity is assigned corresponding labels and attributes based on the context in which it appears and its connection with other entities. Through the comprehensive organization of these data, a detailed entity list is formed, which contains various attributes of the entity such as definition, scope of application, and associated regulations or standards. No preset weights or complex data processing methods are used in this process. Instead, a simple classification and matching process is used to ensure the purity of the data and the traceability of the processing process, forming a direct mapping of the original monitoring data. These preparatory work lays the foundation for subsequent in-depth analysis.

[0084] Based on the formed entity list, an in-depth analysis of the interaction patterns between entities was conducted. By calculating the frequency of each entity appearing in the document and its contextual association with other entities, the main interaction patterns were analyzed. For example, some entities are frequently linked together because they appear together in specific supervision regulations or safety standards. This analysis reveals the dependency and influence relationships between entities. It does not involve complex mathematical models or algorithms, but identifies these patterns through simple statistical and logical reasoning. These relationship patterns provide data support for understanding how entities interact with each other in actual supervision work and determine a preliminary model of entity relationships.

[0085] An entity relationship model is constructed based on the identified interaction patterns, which maps the logical relationships and interactions between entities in detail. The position and connection of each entity in the model are strictly defined, ensuring the accuracy and practicality of the model. This model directly reflects how entities play a role in project supervision. No preset calculation formulas or weight adjustments are used in the model construction process. Instead, it relies on the entity interaction data obtained from the aforementioned analysis. This approach ensures the practical application value of the model and the ease of operation, forming a complete entity relationship model.

[0086] The steps to obtain the graph feature vector are:

[0087] Input the entity relationship model into the graph neural network for feature learning;

[0088] Execute the forward propagation and feature extraction process of the graph neural network to calculate the neighborhood aggregation information of each entity node. The calculation formula is:

[0089]

[0090] Among them, F i represents the aggregated feature vector of node i, N(i) represents the set of adjacent nodes of node i, and f j is the eigenvector of adjacent node j, W is the transformation matrix;

[0091] Based on the aggregated feature vector, the last layer of the network is used to extract the interaction features and risk association attributes between entities to generate a graph feature vector for each entity.

[0092] Specifically, the entity relationship model is input into the graph neural network, ensuring that the network configuration correctly receives the entity relationship as input and prepares for feature learning. The key to this step is to ensure that all entity relationship data formats match the input requirements of the graph neural network. The correctness of the network configuration directly affects the subsequent learning efficiency and accuracy. The structural design of the network must be able to handle complex entity relationship data, including different types of nodes and diverse edge connections. The input of entity relationships not only involves data reading, but also includes pre-processing steps such as data cleaning and format conversion to ensure that the data quality meets the learning requirements. In addition, the integrity and accuracy of the entity relationship data are also crucial to the results of model training.

[0093] formula The benefit of this is that it can effectively aggregate the neighborhood information of entity nodes. Through the application of the weight matrix W, it not only emphasizes the importance of the features of each adjacent node, but also allows the network to automatically learn the optimal information aggregation method, thereby better capturing the complex relationships between entities.

[0094] Derivation process: In the graph neural network, the feature vector F of each node i i By aggregating the feature vectors f of all its neighboring nodes j j To update, the transformation matrix W used in this aggregation process is learned by the network during training. For example, node i has three adjacent nodes, and the feature vector of each node is f1 = [0.2, 0.3], f2 = [0.4, 0.5], f3 = [0.1, 0.1]. The transformation matrix W is, for example, [1.0, 0.5; 0.5, 1.0]. The contribution of each adjacent node is calculated as:

[0095] [0.2,0.3]·[1.0,0.5;0.5,1.0]=[0.25,0.35]

[0096] [0.4,0.5]·[1.0,0.5;0.5,1.0]=[0.45,0.65]

[0097] [0.1,0.1]·[1.0,0.5;0.5,1.0]=[0.15,0.15]

[0098] Aggregate all contributions:

[0099]

[0100] Therefore, the updated feature vector F of node i is i The result shows that the feature vector of node i is the weighted average of the features of its neighboring nodes, reflecting the influence of neighboring nodes on i. This helps to propagate and fuse information in the graph, providing a basis for subsequent feature learning and data analysis.

[0101] Based on the aggregated feature vector, the last layer of the network is used to extract the interaction features and risk-related attributes between entities, and generate the graph feature vector of each entity. The key to this step is how to effectively utilize the learned feature vector F i To capture deeper interactions and potential risk connections between entities, the last layer of the network can perform a final transformation and abstraction on the features of each entity, thereby obtaining high-level feature vectors that can represent the complex relationships between entities. These feature vectors not only contain the entity’s own information, but also integrate the relationship information with other entities, enabling the model to more comprehensively understand the dynamic relationships and risk factors between entities when making predictions or classifications. The resulting graph feature vectors provide strong data support for deep learning and analysis of entity relationships, ensuring the accuracy and reliability of the model output.

[0102] The steps to obtain the risk assessment model are:

[0103] Collect the graph feature vector of each entity, analyze the graph feature vector of each entity, and calculate the contribution of the graph feature vector to the engineering risk. The calculation formula is:

[0104]

[0105] Among them, R i Indicates the contribution of entity i to engineering risk, g h is the importance coefficient of feature h, X ih is the value of entity i on feature h, and H is the total number of features;

[0106] Based on the contribution, a risk assessment model is established.

[0107] Specifically, the graph feature vectors of each entity are collected to ensure that these vectors contain all relevant feature information obtained from the previous graph neural network analysis. This step first requires extracting all data points related to each entity from the dataset, including but not limited to the entity's interaction data, historical behavior data, and possible risk indicator data. Through feature engineering and dimensionality reduction, representative feature vectors are extracted from complex data. These vectors will serve as input for graph neural network learning, dynamically evaluate the risk contribution of each entity, and provide accurate input for subsequent risk assessment models.

[0108] formula The benefit is that through the weight parameter g h , which can dynamically weight each feature according to its actual importance, ensuring the accuracy and adaptability of risk assessment.

[0109] Calculation process: The entity has three feature scores, namely X i1 =40,X i2 =30,X i3 =20, and the feature importance weights are set to g1=0.5, g2=0.3, g3=0.2 respectively; then:

[0110] R i =(0.5×40)+(0.3×30)+(0.2×20)

[0111] R i =20+9+4

[0112] R i =33

[0113] The result shows that the total risk contribution of the entity is 33. The higher the value, the greater the contribution of the entity to the project risk and the more it needs to be monitored and managed.

[0114] Based on the calculated risk contribution of each entity, a risk assessment model is established, which can output the risk level of each entity. This step involves converting the previously calculated risk contribution into an actual risk level. By setting different risk thresholds, such as low risk, medium risk and high risk, each threshold corresponds to a different risk level. These levels will help project managers develop corresponding risk response strategies, such as strengthening supervision, adjusting resource allocation or implementing preventive measures, to ensure that project risks are effectively managed and reduced.

[0115] The steps to obtain the updated supervision map are as follows:

[0116] Compare the output of the risk assessment model with industry standards, quantify the risk level of each entity, and calculate the risk deviation of each entity;

[0117] Determine the supervision risk level based on the risk bias of each entity;

[0118] Based on the supervision risk level, the existing knowledge graph is adjusted and updated, and the relationships and interactions between entities and the connections between entities and risk factors are redefined to generate an updated supervision graph.

[0119] Specifically, when comparing the output of the risk assessment model with industry standards, it is first necessary to extract specific risk level standards from the industry standard documents, which include definitions and quantitative indicators of various risks. The data output by the risk assessment model is formatted through professional software, and then compared with the risk indicators of the industry standards one by one. The entity risk deviation is calculated by comparing the difference between the assessed risk value of each entity and the corresponding level of the industry standard. The risk level of each entity is calibrated by the specific numerical difference. For example, if the assessed value of an entity exceeds the safety range of the industry standard, the risk level of the entity will be increased. This calculation process needs to ensure that all data processing steps are strictly performed in accordance with industry specifications to ensure the accuracy and reliability of each assessment. Ultimately, risk deviation data for each entity is generated.

[0120] For the risk deviation data of each entity, the next step is to determine the supervision risk level. In this process, it is necessary to follow the pre-defined risk level classification standards, such as classifying entities with smaller risk deviations as low risk and entities with larger risk deviations as high risk. This classification process involves detailed analysis and classification of risk deviation data, and the classification of entities is achieved through mathematical models such as quantile analysis or cluster analysis. After the classification is completed, the system will adjust the subsequent risk management strategies and supervision activities based on the classification results to ensure that entities of each risk level can be properly managed and monitored. The core of this step is to accurately convert risk deviation data into actual and operational risk levels.

[0121] Once the supervision risk level is determined, the next step is to adjust and update the knowledge graph. In this process, the core task is to redefine the relationships and interactions between entities based on the new risk level. This includes adjusting the weights of nodes and edges in the graph, and updating the association rules between entities. These adjustments are based on the latest risk data obtained from the risk management database. Using graph editing tools, the connections between entities are rebuilt according to predetermined rules, such as priority rules based on risk levels. At the same time, the links between entities and risk factors need to be updated to ensure that the graph can accurately reflect the current supervision data and risk relationships. Finally, after this step is completed, a fully updated supervision graph is generated, which will be used to guide subsequent supervision activities and risk decisions.

[0122] The present invention provides a project supervision data management system, comprising:

[0123] The data collection module collects project name, location, start time, and supervision team information, and generates project metadata archives. It also performs time series analysis on the project metadata archives to identify key time nodes and obtain time monitoring data.

[0124] The entity relationship construction module uses time monitoring data and deep learning models to analyze supervision regulations, construction standards, and safety procedures. It generates entity recognition files through text analysis, refines the logical relationships between entities through the entity recognition files, constructs an entity relationship model, and obtains an entity interaction feature model.

[0125] The risk assessment module, based on the entity interaction feature model, uses graph neural networks to learn the interaction features between entities, extract risk-related attributes, construct graph feature vectors, evaluate the risk contribution of each entity based on the graph feature vectors, establish a risk assessment model, compare the output of the risk assessment model with industry standards, calculate the risk deviation of each entity, and determine the supervision risk level.

[0126] The above are merely preferred embodiments of the present invention and do not limit the present invention in any other form. Any technician familiar with the profession may use the technical content disclosed above to change or modify it into an equivalent embodiment with equivalent changes and apply it to other fields. However, any simple modification, equivalent change and modification made to the above embodiment based on the technical essence of the present invention without departing from the content of the technical solution of the present invention shall still fall within the scope of protection of the technical solution of the present invention.

Claims

1. A method for engineering supervision data management based on deep learning, characterized in that: The following steps are involved: Obtaining a project data set, obtaining the project name, location, start time, and supervision team information from the project data set, generating a project metadata file, performing time series analysis on the project metadata file, identifying key time nodes, and generating time monitoring data; Applying a deep learning model to the time monitoring data to analyze the supervision regulations, construction standards, and safety regulations in the text to generate an entity recognition profile, and using the entity recognition profile to refine the logical relationships and interactions between entities to construct an entity relationship model; Apply graph neural networks to the entity relationship model for feature learning, extract the interaction features and risk-related attributes between entities, generate graph feature vectors, determine the contribution of each entity to the project risk based on the graph feature vectors, and establish a risk assessment model; Compare the output of the risk assessment model with industry standards, calculate the risk deviation of each entity, determine the supervision risk level, adjust the knowledge graph based on the supervision risk level to reflect the current supervision data and risk relationships, and generate an updated supervision graph; The steps for obtaining the entity identification file are: Processing the time monitoring data using a deep learning model to extract text content associated with supervision regulations, construction standards, and safety regulations to generate a set of text feature vectors; According to the text feature vector set, entity recognition is performed and the entity matching score is calculated. The calculation formula is: Among them, E m represents the entity matching score in the text, α k is the feature weight, s k is the score of the kth text feature, K is the total number of features; Arranging and forming an entity recognition profile based on the entity matching scores; The steps for obtaining the entity relationship model are: Extracting identified entities from the entity recognition archive to form an entity list; Analyzing the occurrence frequency and contextual dependencies of each entity in the entity list to identify interaction patterns between entities; According to the interaction pattern, an entity relationship model is constructed to map and record the logical relationships and interactions between entities; The steps for obtaining the atlas feature vector are: Inputting the entity relationship model into a graph neural network for feature learning; Execute the forward propagation and feature extraction process of the graph neural network to calculate the neighborhood aggregation information of each entity node. The calculation formula is: Among them, F i represents the aggregated feature vector of node i, N(i) represents the set of adjacent nodes of node i, and f j is the eigenvector of adjacent node j, W is the transformation matrix; Based on the aggregated feature vector, the last layer of the network is used to extract the interaction features and risk association attributes between entities to generate a graph feature vector for each entity.

2. The engineering supervision data management method based on deep learning according to claim 1 is characterized in that: The steps for obtaining the time monitoring data are as follows: Obtain project name, location, start time, and supervision team information based on the engineering project dataset to generate preliminary project metadata archives; Based on the preliminary project metadata archive, a time series analysis was conducted to identify key time nodes. The calculation formula is: Among them, T k Indicates the key time node, t i represents the date or time of the i-th time point, Z represents the total number of time points, and i is the index of the time point; Based on the key time nodes, time monitoring data is generated.

3. The engineering supervision data management method based on deep learning according to claim 1 is characterized in that: The steps for obtaining the risk assessment model are: Collect the graph feature vector of each entity, analyze the graph feature vector of each entity, and calculate the contribution of the graph feature vector to the engineering risk. The calculation formula is: Among them, R i Indicates the contribution of entity i to engineering risk, g h is the importance coefficient of feature h, X ih is the value of entity i on feature h, and H is the total number of features; A risk assessment model is established based on the contribution degree.

4. The engineering supervision data management method based on deep learning according to claim 1 is characterized in that: The steps for obtaining the updated supervision map are as follows: Comparing the output of the risk assessment model with industry standards, quantifying the risk level of each entity, and calculating the risk deviation of each entity; Determine the supervision risk level based on the risk deviation of each entity; Based on the supervision risk level, the existing knowledge graph is adjusted and updated, the relationships and interactions between entities and the connections between entities and risk factors are redefined, and an updated supervision graph is generated.

5. An engineering supervision data management system according to the engineering supervision data management method based on deep learning according to any one of claims 1 to 4, characterized in that: include: The data collection module collects project name, location, start time and supervision team information, and generates project metadata files; Performing time series analysis on the project metadata archive to identify key time nodes and obtain time monitoring data; An entity relationship building module utilizes the time monitoring data and applies a deep learning model to analyze supervision regulations, construction standards, and safety regulations; Generate entity recognition files through text analysis, refine the logical relationships between entities through the entity recognition files, build an entity relationship model, and obtain the entity interaction feature model; The risk assessment module, based on the entity interaction feature model, uses a graph neural network to learn the interaction features between entities, extracts risk-related attributes, constructs a graph feature vector, evaluates the risk contribution of each entity based on the graph feature vector, establishes a risk assessment model, compares the output of the risk assessment model with the industry standard, calculates the risk deviation of each entity, and determines the supervision risk level.

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