Intelligent Construction Industry Brain Construction Method, System, Equipment and Medium

Abstract

The present invention discloses a method and system for constructing an intelligent construction industry brain, which relates to the fields of intelligent construction and information technology. It includes collecting the original data of the construction industry, constructing an intelligent data fusion and standardization system, performing multi-source data fusion and formulating quality assessment rules; based on the intelligent data fusion and standardization system, using deep neural network algorithms and knowledge graph technology to construct an industrial information model that simulates the human brain neural network; according to the demand analysis results of the industrial information model, designing a collaborative platform for the intelligent construction industry brain to complete the construction of the intelligent construction industry brain. The method of the present invention establishes a reliable data fusion and standardization system, improves data quality and analyzability, realizes comprehensive modeling of construction industry information, conducts demand analysis, effectively solves the problems of difficult data integration, insufficient data analysis ability and poor industrial collaboration, promotes the intelligent development of the construction industry, and enhances the overall competitiveness of the industry.

Description

Technical Field

[0001] The present invention relates to the field of intelligent construction and information technology, and specifically to a method, system, device, and medium for constructing an intelligent construction industry brain. Background Art

[0002] In the context of the rapid development of the construction industry, traditional construction methods pose many challenges in terms of efficiency, quality, and safety, and there is an urgent need to introduce advanced technologies to improve overall efficiency. The intelligent construction industry brain emerges as the times require. Through technologies such as the Internet of Things, artificial intelligence, big data, and cloud computing, a data-driven management and decision-making platform is constructed to achieve intelligent management of the entire life cycle of construction projects.

[0003] The intelligent construction industry brain deploys sensors to monitor the progress, quality, and safety of the construction site in real time, collects multi-dimensional data such as temperature, humidity, and vibration, uses big data technology to store, process, and analyze massive data, and mines potential laws. Artificial intelligence technologies such as machine learning and image recognition establish prediction models and decision support systems through learning and analyzing data, and optimize construction plans and resource allocation. Cloud computing provides powerful computing and storage capabilities to support large-scale data processing and real-time computing.

[0004] Such an intelligent system can early warn of potential problems, improve construction efficiency and quality, promote the informatization and digital transformation of the construction industry, enhance overall competitiveness, and through integrating the data resources of the construction industry, applying advanced technologies such as big data and artificial intelligence, establish an industry brain with functions such as intelligent analysis, decision support, and industrial collaboration, aiming to promote the construction industry to develop towards the direction of intelligence and high efficiency and enhance the overall competitiveness of the industry. Summary of the Invention

[0005] In view of the above problems of high data integration difficulty, insufficient data analysis ability, and poor industrial collaboration, the present invention is proposed.

[0006] Therefore, the technical problem solved by the present invention is: by constructing an intelligent data fusion and standardization system, converting massive data into understandable semantic information, establishing an industrial information model that simulates the human brain neural network, and developing an intelligent construction industry brain collaborative platform, to achieve intelligent analysis and decision support for the construction industry.

[0007] To solve the above technical problems, the present invention provides the following technical solutions: A method for constructing an intelligent construction industry brain, including collecting original data of the construction industry, constructing an intelligent data fusion and standardization system, performing multi-source data fusion and formulating quality assessment rules, based on the intelligent data fusion and standardization system, using deep neural network algorithms and knowledge graph technologies to construct an industrial information model that simulates the human brain neural network, designing an intelligent construction industry brain collaborative platform according to the demand analysis results of the industrial information model, and completing the construction of the intelligent construction industry brain; The data fusion and standardization system includes optimizing the MDI calculation formula, expanding the data quality scoring model, introducing a data anomaly detection and correction mechanism, and comprehensively considering the data source correlation coefficient and type difference; The industrial information model includes capturing the complex relationships and temporal characteristics of construction industry projects based on deep neural network and knowledge graph technologies; The intelligent construction industry brain collaborative platform includes implementing dynamic adjustment and optimization, and constructing a brain collaborative neural network based on a visual interface and natural language interaction.

[0008] As a preferred solution of the method for constructing an intelligent construction industry brain according to the present invention, wherein: The construction of the intelligent data fusion and standardization system includes introducing the construction industry data source correlation coefficient, optimizing the MDI calculation formula, using a standardization method based on kernel density estimation to process outliers, and using construction industry-related data sources to expand the data quality dimension, incorporating accuracy and interpretability into the scoring model, finding the adaptive weight balance point, and constructing a construction industry data model.

[0009] As a preferred solution of the method for constructing an intelligent construction industry brain according to the present invention, wherein: The construction of the construction industry data model includes calculating a multi-source data fusion index, expressed as:

[0010] ;

[0011] ;

[0012] Wherein, is the multi-source data fusion index, is the corresponding weight, is the quality score of the i-th data source, is the data standardization conversion function, is the original data, is the mean value, is the standard deviation; The data quality scoring model, expressed as:

[0013] ;

[0014] Wherein, is the value of the data quality scoring model, is the construction industry data integrity index, is the data timeliness index, is the data consistency index, , and are the corresponding weight coefficients.

[0015] As a preferred solution of the method for constructing the intelligent construction industry brain described in the present invention, wherein: the multi-source data fusion and the formulation of quality assessment rules include setting double thresholds. When the integrity index of construction industry data reaches 85% and the data timeliness index exceeds 90%, the comprehensive assessment of machine, material, method, environment, and measurement is automatically triggered. An abnormal correction coefficient for construction industry data is introduced. When the detected value exceeds 1.5 times the historical average, the intelligent correction program is started to identify key data points and use machine learning for prediction. If the deviation between the detected value and the original value exceeds 30%, it is marked as a status to be verified and the weight is temporarily reduced. A spatio-temporal data quality coordination coefficient is set. When a construction project spans multiple geographical locations and the time span exceeds the preset threshold, the data quality differences in different time periods and geographical locations are analyzed, and the weights of various factors in the data quality scoring model are dynamically adjusted. For geographical locations with low integrity of construction industry data, the data collection frequency and quality requirements are automatically increased. For time periods with low timeliness of construction industry data, data backtracking correction is triggered.

[0016] As a preferred solution of the method for constructing the intelligent construction industry brain described in the present invention, wherein: the construction of an industrial information model simulating the human brain neural network includes setting an outlier detection threshold for the standardized data, combining the five-dimensional data of "machine, material, method, environment, and measurement" to perform multi-dimensional cross-verification on the outliers, and using the deep neural network algorithm to automatically identify and correct the detected outliers. The original data is loaded into a complete industrial information model to output the demand analysis result. Based on the defined ontology structure, an intelligent construction industry knowledge graph is constructed. After converting the structured information of the knowledge graph into low-dimensional vectors using the graph neural network GNN algorithm, the knowledge graph is embedded into the TransE algorithm to map entity relationships and attributes to the low-dimensional vector space. A vectorization threshold is set, and the industrial information model is batch-trained with the original data of the construction industry, and regularization techniques are applied to prevent overfitting.

[0017] As a preferred solution of the method for constructing the intelligent construction industry brain described in the present invention, wherein: the output of the demand analysis result includes calculating the demand analysis result expressed as:

[0018] ;

[0019] wherein, is the demand analysis result, is the Sigmoid activation function, is the knowledge graph embedding The weight matrix, is the knowledge graph embedding information, is an entity, is an attribute, is the neural network feature The weight matrix of, is the original data The weight matrix of, is the normalization function, is the original input data, is the graph structure, is the node feature; When the industrial information model detects that the frequency of a new entity relationship pattern exceeds the preset threshold, the reconstruction program of the intelligent construction industry knowledge graph is triggered. The reconstruction program includes evaluating the consistency between the new pattern and the existing knowledge system; If the consistency is lower than the set standard, the expert review mechanism is started to automatically update the intelligent construction industry knowledge graph and adjust the topology of the graph neural network.

[0020] As a preferred embodiment of the method for constructing the intelligent construction industry brain according to the present invention, wherein: the completion of the construction of the intelligent construction industry brain includes setting the demand weight dynamic index DWDI to dynamically evaluate the influence of the knowledge graph, neural network and original data in real time to adapt to different project types.

[0021] Another object of the present invention is to provide an intelligent construction industry brain construction system, which can solve the problems of uneven data quality, diverse formats and difficult integration and analysis existing in the current construction industry through an intelligent data fusion and standardization system.

[0022] As a preferred embodiment of the intelligent construction industry brain construction system according to the present invention, wherein: it includes the data governance module 100, which is responsible for collecting the original data of the construction industry, evaluating and standardizing the data quality, setting thresholds, dynamically adjusting weights, and realizing data monitoring and correction; The intelligent analysis module 200 is used for outlier detection, building an intelligent graph and vectorizing the data, and constructing an industrial information model; The collaborative platform module 300 is used for demand weight evaluation and feasibility analysis, allowing all stakeholders to evaluate and correct the demand analysis results in real time, and integrating and collaborating with each module to complete the construction of the intelligent construction industry brain.

[0023] A computer device includes a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, the steps of the method for constructing the intelligent construction industry brain are realized.

[0024] A computer-readable storage medium stores a computer program thereon, and when the computer program is executed by a processor, the steps of the method for constructing the intelligent construction industry brain are realized.

[0025] Advantages of the present invention: The method for constructing the intelligent construction industry brain provided by the present invention improves data quality and analyzability by establishing a reliable data fusion and standardization system, providing a reliable basis for subsequent analysis and decision-making; combining deep learning and knowledge graphs to achieve a comprehensive modeling of construction industry information, effectively capturing complex relationships and temporal characteristics in construction projects, and conducting demand analysis; the development of the intelligent construction industry brain collaborative platform realizes functions such as dynamic adjustment of requirements, project progress prediction, optimal allocation of resources, risk warning, quality control, and cost management, providing intuitive and timely decision-making support for project managers. Description of the Drawings

[0026] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly introduced below. Obviously, the drawings in the following description 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 efforts.

[0027] Figure 1 It is the overall flowchart of the method for constructing the intelligent construction industry brain provided by the first embodiment of the present invention. Detailed Embodiments

[0028] In order to make the above objects, features, and advantages of the present invention more obvious and understandable, the specific embodiments of the present invention will be described in detail below with reference to the drawings of the specification. Obviously, the described embodiments are some embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0029] Embodiment 1, referring to Figure 1 , which is an embodiment of the present invention, provides a method for constructing an intelligent construction industry brain, including:

[0030] S1: Collect the original data of the construction industry, construct an intelligent data fusion and standardization system, and perform multi-source data fusion and formulate quality assessment rules.

[0031] Furthermore, collecting the original data of the construction industry includes collecting the original data of the construction industry from multiple data sources, including but not limited to sensor data, BIM model data, project management software data, supply chain data, and market research data, ensuring the comprehensiveness and diversity of data collection, covering the five dimensions of "machinery, materials, methods, environment, and measurement".

[0032] It should be noted that constructing the intelligent data fusion and standardization system includes introducing the correlation coefficient of the construction industry data source, optimizing the MDI calculation formula, using the standardization method based on kernel density estimation to process outliers, using the relevant data sources of the construction industry to expand the data quality dimension, incorporating accuracy and interpretability into the scoring model, finding the adaptive weight balance point, and constructing the construction industry data model.

[0033] It should also be noted that constructing the construction industry data model includes calculating the multi-source data fusion index, expressed as:

[0034] ;

[0035] ;

[0036] Among them, is the multi-source data fusion index, is the corresponding weight, is the th quality score of the data source, is the data standardization conversion function, is the original data, is the mean, is the standard deviation; the data quality scoring model, expressed as:

[0037] ;

[0038] Among them, is the value of the data quality scoring model, is the construction industry data integrity index, is the data timeliness index, is the data consistency index, , and are the corresponding weight coefficients.

[0039] It should also be noted that multi-source data fusion and the formulation of quality assessment rules include setting double thresholds. When the construction industry data integrity index reaches 85% and the data timeliness index exceeds 90%, an automatic trigger for the comprehensive assessment of machines, materials, methods, environment, and measurement is initiated. An abnormal correction coefficient for construction industry data is introduced. When the detected value exceeds 1.5 times the historical average, an intelligent correction program is started to identify key data points and use machine learning for prediction. If the deviation between the detected value and the original value exceeds 30%, it is marked as a status to be verified and the weight is temporarily reduced. A spatio-temporal data quality coordination coefficient is set. When a construction project spans multiple geographical locations and the time span exceeds the preset threshold, the data quality differences in different time periods and geographical locations are analyzed, and the weights of various factors in the data quality scoring model are dynamically adjusted. For geographical locations with low construction industry data integrity, the data collection frequency and quality requirements are automatically increased. For time periods with low construction industry data timeliness, data backtracking and correction are triggered.

[0040] It should also be noted that multi-source data fusion and the formulation of quality assessment rules include introducing a data consistency fluctuation index DCFI. When the data consistency fluctuation index DCFI shows an upward trend in consecutive evaluation cycles and the latest value exceeds 1.5 times the historical average, a data consistency intelligent correction program is initiated. The data consistency intelligent correction program includes identifying the key data points that cause inconsistencies and using machine learning algorithms to predict the correct value based on historical data patterns and the characteristics of the current construction project. If the deviation between the predicted value and the original value exceeds 30%, the system will automatically mark this data point as a status to be verified and temporarily reduce its weight in the data quality scoring.

[0041] It should also be noted that multi-source data fusion and the formulation of quality assessment rules include setting a spatio-temporal data quality coordination coefficient STDQC. When a construction project spans several geographical locations or the time span exceeds the preset threshold and is activated, the data quality differences in different time periods and geographical locations are analyzed, and the weights of various factors in the data quality scoring model are dynamically adjusted. When it is detected that the data integrity of a certain geographical location is lower than that of other locations, the data collection frequency and quality requirements for this location are automatically increased. If it is detected that the data timeliness of a certain time period is low, a data backtracking mechanism is triggered, and high-quality data from adjacent time periods is used for interpolation and correction.

[0042] S2: Based on intelligent data fusion and the standardization system, use the deep neural network algorithm and knowledge graph technology to construct an industrial information model that simulates the human brain neural network.

[0043] Furthermore, after data preparation, outlier detection and correction, the construction of the industrial information model requires the improvement of steps such as knowledge graph construction, deep neural network construction, and model fusion application.

[0044] It should be noted that knowledge graph construction includes defining the ontology of the industrial information model, including entities, attributes, and relationships. Based on the ontology definition, entities, attributes, and relationships are extracted from the original data to construct an industrial information knowledge graph. The structured information of the knowledge graph is transformed into low-dimensional vectors using the graph neural network GNN algorithm, and the entity relationships and attributes are mapped to the low-dimensional vector space using the TransE algorithm.

[0045] It should also be noted that deep neural network construction includes selecting an appropriate deep neural network model, designing the network structure of the deep neural network, training the deep neural network model using the original data of the construction industry, and optimizing the parameters.

[0046] It should also be noted that model fusion includes fusing the knowledge graph embedding information, deep neural network features, and original input data to construct a comprehensive industrial information model.

[0047] It should also be noted that constructing an industrial information model that simulates the human brain neural network includes, for the standardized data, setting the outlier detection threshold, combining the five-dimensional data of "machine, material, method, environment, and measurement", performing multi-dimensional cross-validation on the outliers, using the deep neural network algorithm to automatically identify and correct the detected outliers, loading the original data into a complete industrial information model, and outputting the demand analysis result; based on the defined ontology structure, constructing an intelligent construction industry knowledge graph, using the graph neural network GNN algorithm to transform the structured information of the knowledge graph into low-dimensional vectors, and then embedding the knowledge graph into the TransE algorithm to map the entity relationships and attributes to the low-dimensional vector space; setting the vectorization threshold, batch training the industrial information model using the original data of the construction industry, and applying regularization techniques to prevent overfitting.

[0048] It should also be noted that outputting the demand analysis result includes calculating the demand analysis result expressed as:

[0049] ;

[0050] Where, is the demand analysis result, is the Sigmoid activation function, is the weight matrix of the knowledge graph embedding of, is the knowledge graph embedding information, is the entity, is the attribute, is the neural network feature of the weight matrix, is the original data of the weight l matrix, is the normalization function, is the original input data, is a graph structure, and is the node feature; when the industrial information model detects that the occurrence frequency of a new entity relationship pattern exceeds a preset threshold, the reconstruction program of the intelligent construction industry knowledge graph is triggered. The reconstruction program includes evaluating the consistency between the new pattern and the existing knowledge system; if the consistency is lower than the set standard, the expert review mechanism is started to automatically update the intelligent construction industry knowledge graph and adjust the topology of the graph neural network.

[0051] S3: According to the demand analysis results of the industrial information model, design the collaborative platform of the intelligent construction industry brain to complete the construction of the intelligent construction industry brain.

[0052] Furthermore, completing the construction of the intelligent construction industry brain includes setting the demand weight dynamic index DWDI to evaluate the influence of knowledge graph embedding, neural network features, and raw data in demand analysis in real time; when the demand weight dynamic index DWDI detects a change in the contribution degree of a certain component, the weight allocation algorithm is triggered, a domain-specific factor is introduced, and the weight ratio of each component is dynamically adjusted according to different building project types.

[0053] It should be noted that completing the construction of the intelligent construction industry brain includes setting the demand feasibility threshold RFT for implementation difficulty, resource investment, and market acceptance; when the demand analysis result reaches the demand feasibility threshold RFT, the refined analysis program is triggered, and a threshold adaptive mechanism is introduced to regularly update the demand feasibility threshold RFT according to historical project data and industry development trends.

[0054] It should also be noted that completing the construction of the intelligent construction industry brain includes developing a demand feedback intelligent interface RFII to allow all stakeholders to evaluate and correct the demand analysis results in real time; setting the demand consensus index RCI, and when the set demand consensus index RCI is lower than the preset value, the multi-party negotiation mechanism is automatically started to promote the unification and optimization of demands.

[0055] Embodiment 2 is an embodiment of the present invention, which provides a method for constructing an intelligent construction industry brain. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0056] Specifically, a large-scale construction project is used as the test object. This project is a comprehensive commercial center with a floor area of 50,000 square meters, including an office building, a shopping mall, and an underground parking lot. The total construction area of the project is about 200,000 square meters, and the planned construction period is 24 months.

[0057] First, collect various types of data related to the construction project, including information on design drawings, construction progress, material procurement, personnel allocation, equipment usage, safety management, and quality control. The data sources include the project management system, BIM model, IoT sensors, on-site video surveillance, and supply chain management system. To ensure the accuracy and integrity of the data, a method of cross-verifying multi-source data is adopted.

[0058] Furthermore, construct an intelligent data fusion and standardization system. Adopt a distributed storage architecture and combine blockchain technology to ensure the security and traceability of data; through unified data standards and interface specifications, achieve seamless integration of data from different sources and in different formats; introduce natural language processing technology to convert unstructured data into structured information.

[0059] Even further, on the basis of data fusion and standardization, use deep neural network algorithms and knowledge graph technology to construct an industrial information model that simulates the human brain neural network. Among them, a multi-layer convolutional neural network structure is adopted, combined with an attention mechanism and long short-term memory network (LSTM), which can effectively capture the complex relationships and temporal characteristics in the construction project; the knowledge graph incorporates the professional knowledge and experience of the construction industry, enhancing the interpretability and reasoning ability of the model. Based on the demand analysis results of the industrial information model, design and develop an intelligent construction industry brain collaborative platform, which adopts a microservices architecture and supports multi-terminal access and real-time data update. The core functional modules include project progress prediction, resource optimization allocation, risk warning, quality control, and cost management. Through the visual interface and natural language interaction, the platform can provide intuitive and timely decision-making support for project managers.

[0060] Specifically, as shown in Table 1, by comparing the experimental data, it can be clearly seen that the intelligent construction industry brain is superior to traditional project management methods in many aspects; in terms of the project completion time, after adopting the intelligent construction industry brain, the project completion time is shortened from 26 months to 22 months, advancing by 15.38%. This improvement benefits from the accurate prediction and dynamic adjustment capabilities of the industry brain for the project progress. By analyzing historical data and real-time information, the system can timely identify potential delay factors and provide optimization solutions to improve the execution efficiency of the project.

[0061] Table 1 Comparison table between the present invention and traditional methods

[0062]

[0063] Furthermore, in terms of cost savings, the intelligent construction industry brain helps projects save 8.5% of costs. This achievement is mainly due to the intelligent optimization of resource allocation and the refined management of the procurement process. By predicting material requirements and price fluctuations, procurement can be carried out at the optimal time, avoiding the additional costs brought by inventory backlogs and emergency purchases. In terms of resource utilization rate, under the traditional method, the resource utilization rate was 72%, while after adopting the intelligent construction industry brain, this indicator increased to 89%, with an increase of 23.61%. The industry brain can accurately allocate resources by monitoring and predicting the usage of various resources in real time, reducing resource idleness and waste.

[0064] Even further, in terms of the quality problem discovery rate, it has increased from 65% to 92%, with an increase of up to 41.54%. The intelligent construction industry brain uses deep learning algorithms to identify tiny quality anomalies from a vast amount of construction data. Combining with the professional knowledge in the knowledge graph, it can not only detect potential quality problems early but also provide targeted solutions, greatly improving the project quality control level. In terms of the incidence rate of safety accidents, in terms of safety management, the intelligent construction industry brain also performs excellently. The incidence rate of safety accidents has decreased from 1.8% to 0.5%, with a decrease of 72.22%. By analyzing historical accident data, monitoring the construction site in real time, identifying dangerous behavior patterns, etc., it can early warn of potential safety risks and automatically generate safety management measures, effectively preventing the occurrence of various safety accidents.

[0065] Embodiment 3, an embodiment of the present invention, provides an intelligent construction industry brain construction system, including a data governance module 100, an intelligent analysis module 200, and a collaborative platform module 300.

[0066] Wherein S4: The data governance module 100 includes a data collection sub-module 101, a data quality assessment sub-module 102, a data standardization sub-module 103, a weight dynamic sub-module 104, a data monitoring and correction sub-module 105, and a data fusion sub-module 106.

[0067] Furthermore, the data collection sub-module 101 is used to collect the original construction industry data from multiple data sources. The data quality assessment sub-module 102 is used to evaluate the quality of the collected data, including data integrity, accuracy, consistency, and timeliness, and set the threshold for data quality assessment. The data standardization sub-module 103 is used to uniformly process the data from different data sources using standardization methods to eliminate the dimension differences. The weight dynamic sub-module 104 is used to dynamically adjust the weights of different data sources based on the data quality assessment results to ensure the rationality of data fusion. The data monitoring and correction sub-module 105 is used to monitor the data quality in real time, identify and correct abnormal data to ensure the continuous stability of data quality. The data fusion sub-module 106 is used to fuse the data from multiple data sources to form a complete and unified data set.

[0068] It should be noted that the data collection sub-module 101 is the data basis among the sub-modules. The data quality assessment sub-module 102 provides data quality information for the data standardization sub-module 103, and guides the data monitoring and correction sub-module 105 to perform data correction, provides weight information for the weight dynamic sub-module 104, and provides the corrected data for the data standardization sub-module 103. The data fusion sub-module 106 provides the fused data for the intelligent analysis module 200.

[0069] It should also be noted that the data governance module 100 provides a reliable data basis for the intelligent analysis module 200, which is a prerequisite for the effective operation of the entire system.

[0070] S5: The intelligent analysis module 200 includes an outlier detection sub-module 201, a knowledge graph construction sub-module 202, a deep neural network construction sub-module 203, and a model training sub-module 204.

[0071] It should be noted that the outlier detection sub-module 201 detects outliers in the standardized data. The knowledge graph construction sub-module 202 extracts entities, attributes, and relationships from the data collection sub-module 101 based on ontology definitions to construct an industrial information knowledge graph. The deep neural network construction sub-module 203 is used to select a suitable deep neural network model, and the model training sub-module 204 uses the original construction industry data to train the deep neural network model and optimize the parameters.

[0072] It should also be noted that the intelligent analysis module 200 depends on the data provided by the data governance module 100, outputs an industrial information model, and provides decision-making support for the collaborative platform module 300.

[0073] S6: The collaborative platform module 300 includes a demand weight assessment sub-module 301, a feasibility analysis sub-module 302, and a module integration and collaboration sub-module 303.

[0074] It should be noted that the requirement weight evaluation sub-module 301 evaluates the weights of different requirements, determines the priorities, and provides requirement weight information for the feasibility analysis sub-module 302. The feasibility analysis sub-module 302 conducts a feasibility analysis on the requirement analysis results, evaluates the feasibility of implementation and potential risks, and provides an integration basis for the module integration and collaboration sub-module 303. The module integration and collaboration sub-module 303 depends on the results of the data governance module 100, the intelligent analysis module 200, and the collaboration platform module 300, and finally realizes the construction of the intelligent construction industrial brain.

[0075] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods of the various embodiments of the present invention. The aforementioned storage medium includes: USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs, etc., which can store program codes.

[0076] The logic and / or steps represented in the flowchart or described in other ways herein, for example, can be considered as a definite sequence list of executable instructions for implementing logical functions, and can be specifically implemented in any computer-readable medium for use by an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processor, or other systems that can fetch instructions from the instruction execution system, apparatus, or device and execute the instructions), or in combination with these instruction execution systems, apparatuses, or devices. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transmit a program for use by or in combination with an instruction execution system, apparatus, or device.

[0077] More specific examples (non-exhaustive list) of computer-readable media include the following: electrical connections (electronic devices) having one or more wirings, portable computer diskettes (magnetic devices), random access memory (RAM), read only memory (ROM), erasable programmable read only memory (EPROM or flash memory), optical fiber devices, and portable compact disc read only memory (CDROM). Additionally, the computer-readable media can even be paper or other suitable media on which a program can be printed, as the program can be obtained electronically, for instance, by optically scanning the paper or other media, followed by editing, interpretation, or otherwise processing it in a suitable manner if necessary, and then storing it in a computer memory.

[0078] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one of the following techniques known in the art or a combination thereof can be used: discrete logic circuits having logic gate circuits for implementing logical functions on data signals, application specific integrated circuits having appropriate combinational logic gate circuits, programmable gate arrays (PGA), field programmable gate arrays (FPGA), etc. It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered by the scope of the claims of the present invention.

Claims

1. Method for constructing intelligent construction industry brain, characterized in that, Including: Collecting the original data of the construction industry, constructing an intelligent data fusion and standardization system, performing multi-source data fusion, and formulating quality assessment rules; Based on the intelligent data fusion and standardization system, using deep neural network algorithms and knowledge graph technologies to construct an industrial information model that simulates the human brain neural network; According to the demand analysis results of the industrial information model, designing a collaborative platform for the intelligent construction industrial brain and completing the construction of the intelligent construction industrial brain; The data fusion and standardization system includes optimizing the MDI calculation formula, expanding the data quality scoring model, introducing a data anomaly detection and correction mechanism, and comprehensively considering the correlation coefficient and type difference of data sources; The industrial information model includes capturing the complex relationships and temporal characteristics of construction industry projects based on deep neural network and knowledge graph technologies; The collaborative platform for the intelligent construction industrial brain includes implementing dynamic adjustment and optimization, constructing a brain collaborative neural network based on a visual interface and natural language interaction; Constructing the intelligent data fusion and standardization system includes Introducing the correlation coefficient of the construction industry data source, optimizing the MDI calculation formula, and using a standardization method based on kernel density estimation to process outliers; And using relevant data sources in the construction industry to expand the data quality dimension, incorporating accuracy and interpretability into the scoring model, finding the balance point of adaptive weights, and constructing a construction industry data model; Constructing the construction industry data model includes Calculating the multi-source data fusion index, expressed as: MDI = ∑(w i * D i ) / ∑w i X std = (X - μ) / σ Among them, MDI is the multi-source data fusion index, w i is the corresponding weight, D i is the quality score of the i-th data source, X std is the data standardization transformation function, X is the original data, μ is the mean, and σ is the standard deviation; The data quality scoring model, expressed as: DQ = (α1 * A + ɑ2 * T + ɑ3 * C') * MDI Where DQ is the value of the data quality scoring model, A is the construction industry data integrity index, T is the data timeliness index, C' is the data consistency index, and ɑ1, ɑ2, and ɑ3 are the corresponding weight coefficients; Multi-source data fusion and formulating quality assessment rules include Setting double thresholds. When the construction industry data integrity index reaches 85% and the data timeliness index exceeds 90%, automatically trigger the comprehensive assessment of man, machine, material, method, environment, and measurement; Introducing a construction industry data anomaly correction coefficient. When the detected value exceeds 1.5 times the historical average, start the intelligent correction program, identify key data points, and use machine learning for prediction; If the deviation between the detected value and the original value exceeds 30%, it is marked as a status to be verified and the weight is temporarily reduced. Set the spatio-temporal data quality coordination coefficient. When a construction project spans multiple geographical locations and the time span exceeds the preset threshold, analyze the data quality differences in different time periods and geographical locations, and dynamically adjust the weights of various factors in the data quality scoring model; For geographical locations with low construction industry data integrity, automatically increase the data collection frequency and quality requirements. For time periods with low construction industry data timeliness, trigger data backtracking and correction; Constructing an industrial information model that simulates the human brain neural network includes Setting an outlier detection threshold for the standardized data, combining the five-dimensional data of "man, machine, material, method, environment, and measurement", performing multi-dimensional cross-validation on the outliers, and using deep neural network algorithms to automatically identify and correct the detected outliers, loading the original data into a complete industrial information model, and outputting the demand analysis results; Based on the defined ontology structure, construct a knowledge graph for the intelligent construction industry. After converting the structured information of the knowledge graph into low-dimensional vectors using the graph neural network GNN algorithm, embed the knowledge graph into the TransE algorithm to map entity relationships and attributes into the low-dimensional vector space; Set the vectorization threshold, batch-train the industry information model with the original data of the construction industry, and apply regularization techniques to prevent overfitting; The output demand analysis results include, The calculation of the demand analysis results is expressed as: NA = θ(W k · Trans E(e, r, t)) + W n · GNN(G, X) + W d · Norm(Raw Data) It should be noted that there may be some inaccuracies in the original text, especially in "Teans" which might be a misspelling. It could potentially be "Trans". Among them, NA is the result of requirement analysis, θ is the Sigmoid activation function, and W k is the weight matrix of the knowledge graph embedding k, TeansE(e, r, t) is the knowledge graph embedding information, e is the entity, r is the attribute, and W n is the weight matrix of the neural network feature n, W d is the weight matrix of the raw data d, Norm is the normalization function, Raw Data is the original input data, G is the graph structure, and X is the node feature; When the industry information model detects that the occurrence frequency of a new entity relationship pattern exceeds the preset threshold, the reconstruction program of the intelligent construction industry knowledge graph is triggered. The reconstruction program includes evaluating the consistency of the new pattern with the existing knowledge system; if the consistency is lower than the set standard, the expert review mechanism is started to automatically update the intelligent construction industry knowledge graph and adjust the topology of the graph neural network.

2. The method for constructing an intelligent construction industry brain according to claim 1, characterized in that: The completion of the construction of the intelligent construction industry brain includes, Set the dynamic weight index DWDI for demand, and dynamically evaluate the influence of the knowledge graph, neural network, and original data in real time to adapt to different project types.

3. Intelligent construction industry brain construction system, characterized in that: It includes a data governance module (100), an intelligent analysis module (200), and a collaborative platform module (300); The data governance module (100) is responsible for collecting the original data of the construction industry, standardizing the evaluation of data quality, setting thresholds, dynamically adjusting weights, and realizing data monitoring and correction; The intelligent analysis module (200) is used for outlier detection, building an intelligent graph and vectorizing the data, and constructing an industry information model; The collaborative platform module (300) is used for demand weight evaluation and feasibility analysis, allowing all stakeholders to conduct real-time evaluation and correction of the demand analysis results, integrating and collaborating on each module, and completing the construction of the intelligent construction industry brain.

4. A computer device, comprising a memory and a processor, the memory storing a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method for constructing an intelligent construction industry brain according to any one of claims 1 to 2.

5. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the method for constructing an intelligent construction industry brain according to any one of claims 1 to 2.

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