Intelligent planning system and method for construction projects

Abstract

The present invention relates to an intelligent planning system and method for a construction project, and relates to the technical field of construction management, wherein the system comprises: a data receiving module for receiving initial data of a construction project; a data processing module for performing preprocessing; a material statistics module for determining various types of building materials used in the construction project; a material feature acquisition module for acquiring physical property parameters and usage requirements of various types of building materials used in the construction project; a material consumption analysis module for determining the allowable consumption deviation range of various types of building materials; a personalized analysis module for generating a material use control plan for the construction project through a large artificial intelligence language model; and a plan output module for transmitting the material use control plan for the construction project to a user terminal. The system has the advantages of personalized analysis and determination of reasonable consumption levels of various types of materials according to the actual situation of the project, thereby optimizing material use management.

Description

Technical Field

[0001] The present invention relates to the technical field of construction management, and in particular to an intelligent planning system and method for construction projects. Background Art

[0002] Construction projects often involve collaboration and coordination across multiple disciplines. To ensure smooth project execution, detailed planning schemes are often developed before construction begins to guide and coordinate work at each stage. Furthermore, construction projects often face various risks during the construction process, including those related to quality, schedule, and material consumption. Effective management of project planning information allows project management teams to better identify and manage potential risks and take appropriate measures to mitigate their impact.

[0003] Traditional construction project management methods primarily rely on the experience and expertise of project managers to achieve project goals through the development of detailed project plans, monitoring construction progress, managing resources, and controlling costs. However, these methods suffer from the following limitations: They are highly subjective, relying on managers' experience and judgment, susceptible to personal influence, and lacking scientific evidence. They also suffer from insufficient data utilization. Construction projects generate a vast amount of data, including design drawings, progress reports, and resource usage, but traditional methods often fail to fully utilize this data for decision support. Third, they suffer from weak adaptability: Faced with complex and volatile construction environments, traditional methods struggle to quickly respond and adjust, easily leading to project delays and cost overruns. In particular, the use of various building materials in construction projects directly impacts project quality and cost control. Traditional material management methods rely primarily on manual experience, lacking scientific analysis and personalized guidance, leading to problems such as material waste and increased rework. Furthermore, due to the complex construction site environment, material consumption is subject to a certain range of normal variation. Managing material consumption based on fixed theoretical usage limits makes it difficult to ensure optimal use.

[0004] Therefore, it is necessary to provide an intelligent planning system and method for construction projects, which can be used to analyze and determine the reasonable consumption level of various materials according to the actual situation of the project, thereby optimizing material use management. Summary of the Invention

[0005] The present invention provides an intelligent planning system for construction projects, comprising: a data receiving module for receiving initial data of a construction project; a data processing module for preprocessing the initial data of the construction project; a material statistics module for determining various types of building materials used in the construction project based on the preprocessed initial data of the construction project; a material feature acquisition module for acquiring physical property parameters and usage requirements of various types of building materials used in the construction project; a material consumption analysis module for determining, based on the physical property parameters and usage requirements of various types of building materials used in the construction project, an allowable consumption deviation range of various types of building materials used in the construction project; a personalized analysis module for generating, through a large artificial intelligence language model, a material use control plan for the construction project based on the allowable consumption deviation range and usage requirements of various types of building materials used in the construction project; and a plan output module for transmitting the material use control plan for the construction project to a user terminal.

[0006] Furthermore, the data processing module pre-processes the initial data of the construction project, including: cleaning the initial data of the construction project; and formatting the initial data after data cleaning.

[0007] Furthermore, the material feature acquisition module is also used to: obtain the physical property parameters of each sample building material; obtain the usage requirements of each sample building material in different sample construction scenarios, wherein the sample construction scenarios include sample construction environments and sample construction processes; establish a knowledge graph based on the physical property parameters of each sample building material and the usage requirements in different construction scenarios; the material feature acquisition module obtains the physical property parameters and usage requirements of various building materials used in the construction project, including: obtaining the physical property parameters and usage requirements of various building materials used in the construction project based on the knowledge graph.

[0008] Furthermore, the material feature acquisition module obtains the physical property parameters and usage requirements of various types of building materials used in the construction project based on the knowledge graph, including: for each type of building material used in the construction project, based on the basic information of the building material and the knowledge graph, determining the target sample building material, and obtaining the physical property parameters of the target sample building material; for each type of building material used in the construction project, based on the construction scenario of the building material and the knowledge graph, determining the target construction scenario, and based on the target construction scenario, obtaining the usage requirements of the building material.

[0009] Furthermore, the material consumption analysis module determines the allowable consumption deviation range of various types of building materials used in the construction project based on the physical property parameters and usage requirements of various types of building materials used in the construction project, including: establishing a consumption prediction model; through the consumption prediction model, determining the allowable consumption deviation range of various types of building materials used in the construction project based on the physical property parameters and usage requirements of various types of building materials used in the construction project.

[0010] Furthermore, the material consumption analysis module determines the allowable consumption deviation range of various building materials used in the construction project based on the physical property parameters and usage requirements of various building materials used in the construction project through a consumption prediction model, including: determining the initial allowable consumption deviation range of various building materials used in the construction project based on the physical property parameters and usage requirements of various building materials used in the construction project through a consumption prediction model; obtaining consumption information of various building materials used in multiple historical construction projects; for each type of building material used in the construction project, calculating the consumption deviation fluctuation parameter of the building material based on the consumption information of various building materials used in the multiple historical construction projects; when the consumption deviation fluctuation parameter of the building material is greater than or equal to a preset consumption deviation similarity parameter threshold, performing a single correction on the initial allowable consumption deviation range of the building material based on the construction environment and construction process of the construction project through a parameter correction model to determine the allowable consumption deviation range of the building material; when the consumption deviation fluctuation parameter of the building material is less than the preset consumption deviation similarity parameter threshold, using the initial allowable consumption deviation range of the building material as the allowable consumption deviation range of the building material.

[0011] Furthermore, the material consumption analysis module is also used to: determine the consumption deviation association parameters between any two building materials; the material consumption analysis module uses a parameter correction model to perform a single correction on the initial allowable consumption deviation range of the building materials based on the construction environment and construction process of the construction project, and determines the allowable consumption deviation range of the building materials, including: using a parameter correction model to perform a single correction on the initial allowable consumption deviation range of the building materials based on the construction environment and construction process of the construction project, and determining the intermediate allowable consumption deviation range of each type of building material used in the construction project; based on the consumption deviation association parameters between any two building materials, jointly correcting the intermediate allowable consumption deviation range of each type of building material used in the construction project to determine the allowable consumption deviation range of each type of building material used in the construction project.

[0012] Furthermore, the personalized analysis module generates a material usage control plan for the construction project through the artificial intelligence language big model based on the allowable consumption deviation range and usage requirements of various types of construction materials used in the construction project, including: determining multiple construction nodes based on the pre-processed initial data of the construction project; determining the usage control indicators and usage recommendations of the construction materials for the multiple construction nodes based on the allowable consumption deviation range and usage requirements of various types of construction materials used in the construction project through the artificial intelligence language big model.

[0013] Furthermore, the system also includes an accounting profit and risk assessment module for automatically generating pre-profit reports, contract risk analysis, contract negotiation recommendations, contract review reports, break-even point analysis, and risk management strategies.

[0014] The present invention provides an intelligent planning method for construction projects, comprising: receiving initial data of a construction project; preprocessing the initial data of the construction project; determining various types of building materials used in the construction project based on the preprocessed initial data of the construction project; obtaining physical property parameters and usage requirements of various types of building materials used in the construction project; determining allowable consumption deviation ranges of various types of building materials used in the construction project based on the physical property parameters and usage requirements of various types of building materials used in the construction project; generating a material use control plan for the construction project based on the allowable consumption deviation ranges and usage requirements of various types of building materials used in the construction project through a large artificial intelligence language model; and transmitting the material use control plan for the construction project to a user terminal.

[0015] Compared with the existing technology, the intelligent planning system and method for construction projects provided in this specification have at least the following beneficial effects:

[0016] A data-driven approach reduces reliance on project managers' personal experience and judgment, making decision-making more objective and scientific. Leveraging big data and AI models, material demand and consumption trends can be more accurately predicted, reducing errors caused by human error. Based on the material's physical properties and usage requirements, a customized tolerance range for consumption is established for each material, ensuring rational and efficient use. This system can quickly respond to changes in the construction environment and adjust material usage plans in real time, avoiding over-purchasing or material shortages and thus reducing waste. In the face of complex and volatile construction environments, data can be rapidly analyzed and processed to generate appropriate material usage control plans to ensure smooth project progress. Precise material usage control reduces rework and quality issues caused by material problems, improving overall project quality. Optimizing material usage plans reduces unnecessary waste and additional costs, effectively controlling total project costs. Automated data processing and solution generation reduces manual intervention and improves work efficiency. Accumulating project experience and data into a knowledge base provides reference and inspiration for future projects, promoting the accumulation and transfer of project management knowledge. Automated execution of project planning, management, and business processes improves efficiency, mitigates risk, and ensures contract compliance. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] This specification will be further described in the form of exemplary embodiments, which will be described in detail with reference to the accompanying drawings. These embodiments are not limiting, and in these embodiments, like numbers represent like structures, wherein:

[0018] Figure 1 This is a module diagram of an intelligent planning system for a construction project shown in one embodiment of the present application;

[0019] Figure 2 is a schematic diagram of a knowledge graph shown in an embodiment of the present application;

[0020] Figure 3 This is a schematic diagram illustrating a joint correction of the allowable intermediate consumption deviation range of each type of building material used in a construction project according to an embodiment of the present application;

[0021] Figure 4 This is a flow chart of an intelligent planning method for a construction project shown in one embodiment of the present application. DETAILED DESCRIPTION

[0022] In order to more clearly illustrate the technical solutions of the embodiments of this specification, the following briefly introduces the drawings required for describing the embodiments.

[0023] Figure 1 This is a module diagram of an intelligent planning system for a construction project shown in one embodiment of the present application. Figure 1As shown, an intelligent planning system for a construction project may include a data receiving module, a data processing module, a material statistics module, a material feature acquisition module, a material consumption analysis module, a personalized analysis module, a solution output module, and a benefit and risk assessment module.

[0024] The data receiving module can be used to receive initial data of a construction project.

[0025] Specifically, the initial data of the construction project may include a bill of quantities, a bill of materials, etc. of the construction project.

[0026] The initial data of a construction project may also include other materials, such as construction plans, project bidding and contract documents, etc.

[0027] The data processing module can be used to pre-process the initial data of the construction project.

[0028] In some embodiments, the method specifically includes:

[0029] Perform data cleaning on the initial data of the construction project;

[0030] Format the initial data after data cleaning.

[0031] Specifically, the initial data of the construction project can be cleaned based on the following steps:

[0032] Conduct a preliminary analysis of the bill of quantities and bill of materials to identify potential problems in the bill of quantities and bill of materials, such as missing values, outliers, and duplicate data. Develop targeted data cleaning rules based on the data analysis results. For example, for missing values, decide whether to fill (such as using the mean, median, or mode) or delete them; for outliers, decide whether to correct or eliminate them. Modify column names to make them more descriptive and standardized. Delete blank rows and columns to reduce invalid data. Convert data formats, such as converting text numbers to numeric types to facilitate mathematical operations. Fill or delete missing values ​​according to cleaning rules. Check and delete duplicate records in the bill of quantities and bill of materials. Correct obvious errors in the bill of quantities and bill of materials, such as incorrect coding, inconsistent units, etc. Group or classify data to facilitate subsequent data analysis and processing.

[0033] The initial data after data cleaning can be formatted based on the following steps:

[0034] Standardize project names and descriptions, automatically check their accuracy and consistency, make corrections or standardization, and unify measurement units and calculation methods: Automatically generate the table structure of bills of quantities and bills of materials according to standard format requirements, including column names, column widths, row heights, etc. Fill the pre-processed and standardized data into the table structure.

[0035] The material statistics module can be used to determine the various types of building materials used in a construction project based on the pre-processed initial data of the construction project.

[0036] Among them, various types of building materials used in construction projects may include structural materials (for example, steel bars, concrete, stone, wood, prefabricated components, etc.), decorative materials (for example, tiles, paint, wallpaper, glass, etc.), auxiliary materials (for example, cement bricks, insulation materials, waterproof materials, hardware accessories, etc.), etc.

[0037] Specifically, the material statistics module can determine the various types of building materials used in the construction project based on the following steps:

[0038] Create a database containing various building materials, including the name, specifications, model, brand, supplier information, etc. The database should support dynamic updates so that new materials can be added or the information of existing materials can be updated at any time;

[0039] Match and identify the project names, specifications and models in the bill of quantities and bill of materials, map the items in the bill of quantities and bill of materials with the entries in the materials database, and determine the various types of building materials used in the construction project.

[0040] The material feature acquisition module can be used to obtain the physical property parameters and usage requirements of various building materials used in construction projects.

[0041] In some embodiments, the material feature acquisition module is further configured to:

[0042] Obtain the physical properties of each sample building material, such as density, bulk density, porosity, void ratio, water absorption, moisture content, hardness, strength, specific heat capacity, thermal conductivity, and elastic-plastic properties;

[0043] Obtaining usage requirements for each sample building material in different sample construction scenarios, wherein the sample construction scenarios include sample construction environments and sample construction processes, and the usage requirements may include construction requirements for reducing loss of the sample building materials in different sample construction scenarios;

[0044] A knowledge graph is established based on the physical property parameters of each sample building material and its usage requirements in different construction scenarios.

[0045] The usage requirements of each sample building material in different sample construction scenarios can be determined manually or through relevant literature.

[0046] Specifically, Figure 2 is a schematic diagram of a knowledge graph shown in an embodiment of the present application, such as Figure 2 As shown, Sample Building Material 1 corresponds to Usage Requirement 1 in Sample Construction Scenario 1, Usage Requirement 2 in Sample Construction Scenario 2, and Usage Requirement 3 in Sample Construction Scenario 3. For illustrative purposes only, in dry environments, the requirements for rebar use are relatively low, primarily focusing on whether its mechanical properties meet design requirements. In this case, it is important to ensure that the specifications, models, and strength grades of the rebar comply with the construction drawings and relevant standards. Furthermore, during processing and installation, strict compliance with specifications should be followed to ensure the quality of rebar connections, such as binding and welding. In humid environments, rebar is susceptible to corrosion due to the combined effects of moisture and oxygen in the air. Therefore, additional anti-corrosion measures are necessary when using rebar in such environments. For example, hot-dip galvanized rebar or epoxy-coated rebar, which offer superior corrosion resistance, can be used. Furthermore, during construction, care should be taken to keep the rebar surface dry and clean to avoid the adhesion of moisture and impurities. In corrosive environments, such as those at seaside or in chemical plants, rebar corrodes more rapidly, posing a serious threat to the durability and safety of the structure. Therefore, more stringent anti-corrosion measures are necessary when using rebar in such environments. In addition to selecting rebar with excellent corrosion resistance, you can also apply anti-corrosion paint or wrap the rebar surface with an anti-corrosion coating. Furthermore, structural anti-corrosion measures should be considered during the design and construction process, such as installing an anti-corrosion isolation layer and increasing the thickness of the protective layer. High temperatures can alter the mechanical properties of rebar, such as reducing strength and deteriorating plasticity. Therefore, when using rebar in such environments, it is necessary to select a type with good high-temperature resistance and strictly adhere to relevant standards and specifications during design and construction. Fire protection measures should also be considered to improve the structure's overall fire resistance.

[0047] In some embodiments, the material feature acquisition module can obtain the physical property parameters and usage requirements of various building materials used in construction projects based on the knowledge graph, specifically including:

[0048] For each type of building material used in a construction project, based on the basic information and knowledge graph of the building materials, target sample building materials are determined, and physical property parameters of the target sample building materials are obtained. The basic information of the building materials may include name, specification, model, brand, supplier information, etc. A first similarity determination model may be used to calculate the material matching degree between the building materials and the sample building materials based on the basic information of the building materials and the basic information of the sample building materials. The sample building material with the highest material matching degree greater than a preset material matching degree threshold is used as the target sample building material. The first similarity determination model may be a convolutional neural network (CNN) model.

[0049] For each type of building material used in a construction project, a target construction scene is determined based on the construction scene and knowledge graph of the building material. Based on the target construction scene, the usage requirements of the building material are obtained. Specifically, the construction scene of the building material can be extracted from the preprocessed initial data of the construction project through a scene extraction model. The scene similarity between the construction scene of the building material and each sample construction scene of the target sample building material corresponding to the building material is determined through a second similarity determination model. The sample construction scene with a scene similarity greater than a preset scene similarity threshold and the largest scene similarity is used as the target construction scene. The scene extraction model and the second similarity determination model can both be convolutional neural network (CNN) models.

[0050] The material consumption analysis module can be used to determine the allowable consumption deviation range of various building materials used in the construction project based on the physical property parameters and usage requirements of various building materials used in the construction project.

[0051] In some embodiments, the method specifically includes:

[0052] Establishing a consumption prediction model, wherein the consumption prediction model may be a convolutional neural network (CNN) model;

[0053] Through the consumption prediction model, based on the physical property parameters and usage requirements of various building materials used in construction projects, the allowable consumption deviation range of various building materials used in construction projects is determined.

[0054] The allowable consumption deviation range of construction materials may be a ratio range of the difference between the planned usage and the actual usage of construction materials allowed in a construction project.

[0055] In some embodiments, the material consumption analysis module determines the allowable consumption deviation range of various building materials used in the construction project based on the physical property parameters and usage requirements of various building materials used in the construction project using the consumption prediction model, including:

[0056] Through the consumption prediction model, based on the physical properties and usage requirements of various building materials used in the construction project, the initial allowable consumption deviation range of various building materials used in the construction project is determined;

[0057] Obtain consumption information for various types of building materials used in multiple historical building construction projects;

[0058] For each type of building material used in a construction project, the consumption deviation fluctuation parameter of the building material is calculated based on the consumption information of each type of building material used in multiple historical construction projects. When the consumption deviation fluctuation parameter of the building material is greater than or equal to the preset consumption deviation similarity parameter threshold, the initial allowable consumption deviation range of the building material is corrected individually based on the construction environment and construction process of the construction project through the parameter correction model to determine the allowable consumption deviation range of the building material. When the consumption deviation fluctuation parameter of the building material is less than the preset consumption deviation similarity parameter threshold, the initial allowable consumption deviation range of the building material is used as the allowable consumption deviation range of the building material.

[0059] Specifically, the consumption deviation fluctuation parameter can be calculated based on the following formula:

[0060] ;

[0061] in, For the Consumption deviation fluctuation parameters of building materials, For the The consumption deviation value of the building material of the type in the nth historical building construction project, For the construction project of historical buildings

[0062] For the The planned consumption of building materials of the type n in the construction project of the historic building, For the The actual consumption of building materials of this type in the nth historical building construction project.

[0063] In some embodiments, the material consumption analysis module is further configured to:

[0064] Determine the consumption deviation correlation parameters between any two building materials.

[0065] Specifically, the consumption deviation correlation parameter between two building materials can be calculated based on the following formula:

[0066] ;

[0067] in, For the Class I building materials and Consumption deviation correlation parameters between class sample building materials, For the The mean deviation of consumption of building materials, For the The mean deviation of consumption of building materials, For the The consumption deviation value of this type of building material in the nth historical building construction project.

[0068] In some embodiments, the material consumption analysis module uses a parameter correction model to perform a single correction on the initial allowable consumption deviation range of the construction materials based on the construction environment and construction process of the construction project, and determines the allowable consumption deviation range of the construction materials, including:

[0069] Based on the construction environment and construction process of the construction project, a parameter correction model is used to perform a single correction on the initial allowable consumption deviation range of the construction materials, thereby determining the intermediate allowable consumption deviation range of each type of construction material used in the construction project. The parameter correction model may be a recurrent neural network (RNN) model.

[0070] Based on the consumption deviation correlation parameters between any two building materials, the intermediate allowable consumption deviation range of each type of building material used in the construction project is jointly corrected to determine the allowable consumption deviation range of each type of building material used in the construction project.

[0071] Figure 3 FIG. 1 is a schematic diagram showing a joint correction of the allowable consumption deviation range of each type of building material used in a construction project in an embodiment of the present application. Figure 3 Specifically, the intermediate allowable consumption deviation range of each type of building materials used in the construction project can be jointly revised based on the following process:

[0072] S11. Using a joint correction model, based on consumption deviation correlation parameters between any two types of building materials used in the construction project, jointly correct an intermediate allowable consumption deviation range for each type of building material used in the construction project to determine a current iteration of an allowable consumption deviation range for each type of building material used in the construction project. The joint correction model may be a deep neural network (DNN).

[0073] S12. Calculate the cumulative difference of the current iteration based on the allowable consumption deviation range of the current iteration for each type of building material used in the construction project and the intermediate allowable consumption deviation range for each type of building material used in the construction project;

[0074] S13. Determine whether the cumulative difference of the current iteration is less than the preset cumulative difference threshold. If so, use the allowable consumption deviation range of the current iteration of each type of building material used in the construction project as the allowable consumption deviation range of each type of building material used in the construction project. If not, execute S14.

[0075] S14. Using a joint correction model, based on consumption deviation association parameters between any two types of building materials used in the construction project, jointly correct the intermediate allowable consumption deviation range of each type of building material used in the construction project to determine the current iteration allowable consumption deviation range of each type of building material used in the construction project.

[0076] S15. Based on the allowable consumption deviation range of the previous iteration of each type of building material used in the construction project and the allowable consumption deviation range of the current iteration of each type of building material used in the construction project, calculate the cumulative difference of the current iteration and execute S13.

[0077] Specifically, the cumulative difference of the current iteration can be calculated based on the following formula:

[0078] ;

[0079] in, is the cumulative difference of the current iteration, For construction projects The allowed consumption deviation range of the current iteration of the building material class, For construction projects The allowed consumption deviation range of the previous iteration of the building material class, The total amount of building materials used for a building construction project.

[0080] The personalized analysis module can be used to generate a material usage control plan for a construction project based on the allowable consumption deviation range and usage requirements of various types of construction materials used in the construction project through a large artificial intelligence language model.

[0081] In some embodiments, the personalized analysis module generates a material usage control plan for the construction project using an artificial intelligence language model based on the allowable consumption deviation range and usage requirements of various types of construction materials used in the construction project, including:

[0082] Determine multiple construction nodes based on pre-processed initial data of the building construction project;

[0083] Through the artificial intelligence language large model, based on the allowable consumption deviation range and usage requirements of various building materials used in construction projects, the usage control indicators and usage recommendations of building materials at multiple construction nodes are determined.

[0084] Specifically, a node determination model may be used to determine a plurality of construction nodes based on pre-processed initial data of the construction project, wherein the node determination model may be a convolutional neural network (CNN) model.

[0085] The AI ​​language model's input can be constructed based on the allowable consumption tolerances and usage requirements for various building materials used in construction projects. The AI ​​language model's goal is to determine building material usage control indicators and usage recommendations for multiple construction nodes. The compiled data is fed into the AI ​​language model, which analyzes the data, combines historical project experience with current construction conditions, and performs intelligent reasoning and prediction. The AI ​​language model outputs building material usage control indicators and usage recommendations for multiple construction nodes. These indicators and recommendations should detail the type and quantity of building materials required for each construction node, along with the allowable consumption tolerances and specific usage recommendations.

[0086] The plan output module can be used to transmit the material usage control plan of the construction project to the user terminal.

[0087] Specifically, the user terminal can provide a friendly human-computer interaction interface, where users can submit engineering data, view analysis results, obtain usage suggestions, etc.

[0088] The profit and risk assessment module can be used to automatically generate pre-profit reports, contract risk analysis, contract negotiation recommendations, contract review reports, break-even analysis, and risk management strategies.

[0089] Specifically, the profit and risk assessment module can automatically generate preliminary profit reports, contract risk analysis, contract negotiation suggestions, contract review reports, break-even analysis and risk management strategies based on artificial intelligence models.

[0090] An intelligent planning system for construction projects can have the following beneficial effects:

[0091] 1. Improve project management efficiency: By automating the processing of project planning-related data and the execution of project management tasks, the present invention significantly improves the efficiency of the workflow, reduces the time required for manual input and analysis, and makes project planning and execution faster.

[0092] 2. Reduce error rate: The application of artificial intelligence language large models reduces errors in human judgment and provides objective decisions based on data and advanced algorithms, thereby improving the accuracy of project management decisions.

[0093] 3. Optimize risk management: The system can automatically identify and assess potential risks, propose risk management strategies, and provide timely warnings to help project teams better prepare for and deal with uncertainties.

[0094] 4. Enhance contract negotiation and management capabilities: By automatically generating suggestions and review reports on contract terms, the system supports users in making more favorable and compliant decisions in contract negotiation and management.

[0095] 5. Improve data processing capabilities: The system's pre-processing module can effectively process large amounts of complex project data, ensure data quality and consistency, and provide a reliable basis for subsequent analysis and decision-making.

[0096] 6. Provide real-time decision support: The planning schemes and management reports generated by the system can be provided to project managers for reference in real time, providing immediate decision support, thereby improving the project's ability to respond to changes.

[0097] 7. Increase project transparency: Through automatically generated documents and reports, all aspects of the project and the decision-making process are more transparent, facilitating supervision and auditing.

[0098] 8. Improved user experience: The user interface is designed with a focus on intuitiveness and ease of use, reducing training requirements and enabling project teams to seamlessly adopt the new system.

[0099] 9. Strengthen the comprehensiveness of project planning: By comprehensively utilizing project data, the system can provide more comprehensive and detailed planning schemes, taking into account all aspects of project management.

[0100] The technical solution of the present invention not only improves the automation level of construction project management, but also enhances the overall quality and efficiency of project execution, reduces costs and risks, and increases the possibility of project success.

[0101] Figure 4 This is a flow chart of an intelligent planning method for a construction project shown in one embodiment of the present application. Figure 4 As shown, an intelligent planning method for a construction project may include the following steps.

[0102] Step 410, receiving initial data of a construction project;

[0103] Step 420, pre-processing the initial data of the construction project;

[0104] Step 430 , determining various types of building materials used in the construction project based on the pre-processed initial data of the construction project;

[0105] Step 440, obtaining physical property parameters and usage requirements of various building materials used in the construction project;

[0106] Step 450: Determine the allowable consumption deviation range of various building materials used in the construction project based on the physical property parameters and usage requirements of the various building materials used in the construction project;

[0107] Step 460: Generate a material usage control plan for the construction project based on the allowable consumption deviation range and usage requirements of various types of construction materials used in the construction project using the artificial intelligence language model.

[0108] Step 470: Transmit the material usage control plan for the construction project to the user terminal.

[0109] An intelligent planning method for a construction project can be executed by an intelligent planning system for a construction project. For more descriptions of the intelligent planning method for a construction project, please refer to the relevant descriptions of the intelligent planning system for a construction project, which will not be repeated here.

[0110] Finally, it should be understood that the embodiments described in this specification are intended only to illustrate the principles of the embodiments of this specification. Other variations may also fall within the scope of this specification. Therefore, by way of example and not limitation, alternative configurations of the embodiments of this specification may be considered consistent with the teachings of this specification. Accordingly, the embodiments of this specification are not limited to the embodiments explicitly described and illustrated in this specification.

Claims

1. An intelligent planning system for construction projects, characterized in that: include: A data receiving module, used for receiving initial data of a building construction project; A data processing module, used for pre-processing the initial data of the building construction project; A material statistics module, for determining various types of building materials used in the building construction project based on pre-processed initial data of the building construction project; A material characteristic acquisition module is used to obtain the physical characteristic parameters and usage requirements of various building materials used in the construction project; The material consumption analysis module is used to determine the allowable consumption deviation range of various building materials used in the construction project based on the physical property parameters and usage requirements of various building materials used in the construction project, specifically including: S11. Using a joint correction model, based on consumption deviation association parameters between any two types of building materials used in the construction project, jointly correct the intermediate allowable consumption deviation range of each type of building material used in the construction project to determine the current iteration allowable consumption deviation range of each type of building material used in the construction project. S12. Calculate the cumulative difference of the current iteration based on the allowable consumption deviation range of the current iteration for each type of building material used in the construction project and the intermediate allowable consumption deviation range for each type of building material used in the construction project; S13. Determine whether the cumulative difference of the current iteration is less than a preset cumulative difference threshold. If so, use the allowable consumption deviation range of the current iteration for each type of building material used in the construction project as the allowable consumption deviation range for each type of building material used in the construction project. If not, execute S14. S14. Using a joint correction model, based on consumption deviation association parameters between any two types of building materials used in the construction project, jointly correct the intermediate allowable consumption deviation range of each type of building material used in the construction project to determine the current iteration allowable consumption deviation range of each type of building material used in the construction project. S15. Calculate the cumulative difference of the current iteration based on the allowable consumption deviation range of each type of building material used in the construction project in the previous iteration and the allowable consumption deviation range of each type of building material used in the construction project in the current iteration, and execute S13. The material feature acquisition module is further used for: Obtain the physical property parameters of each sample building material; Obtaining usage requirements for each sample building material in different sample construction scenarios, wherein the sample construction scenarios include a sample construction environment and a sample construction process; Establish a knowledge graph based on the physical properties of each sample building material and its usage requirements in different construction scenarios; The material characteristic acquisition module acquires the physical characteristic parameters and usage requirements of various building materials used in the construction project, including: Based on the knowledge graph, obtain the physical property parameters and usage requirements of various building materials used in the construction project; A personalized analysis module is used to generate a material usage control plan for the construction project based on the allowable consumption deviation range and usage requirements of various types of construction materials used in the construction project through an artificial intelligence language large model; The plan output module is used to transmit the material use control plan of the construction project to the user terminal.

2. The intelligent planning system for construction projects according to claim 1, characterized in that: The data processing module pre-processes the initial data of the building construction project, including: Performing data cleaning on the initial data of the building construction project; Format the initial data after data cleaning.

3. The intelligent planning system for construction projects according to claim 1, characterized in that: The material feature acquisition module obtains the physical property parameters and usage requirements of various building materials used in the construction project based on the knowledge graph, including: For each type of building material used in the construction project, determining target sample building materials based on basic information of the building materials and the knowledge graph, and obtaining physical property parameters of the target sample building materials; For each type of building material used in the construction project, a target construction scenario is determined based on the construction scenario of the building material and the knowledge graph, and based on the target construction scenario, the usage requirements of the building material are obtained.

4. The intelligent planning system for construction projects according to claim 3, characterized in that: The material consumption analysis module determines the allowable consumption deviation range of various building materials used in the construction project based on the physical property parameters and usage requirements of various building materials used in the construction project, including: Establish consumption forecasting models; The consumption prediction model is used to determine the allowable consumption deviation range of various building materials used in the construction project based on the physical property parameters and usage requirements of various building materials used in the construction project.

5. The intelligent planning system for construction projects according to claim 4, characterized in that: The material consumption analysis module determines the allowable consumption deviation range of various building materials used in the construction project based on the physical property parameters and usage requirements of various building materials used in the construction project through the consumption prediction model, including: Determining, by means of a consumption prediction model, an initial allowable consumption deviation range of various building materials used in the construction project based on the physical property parameters and usage requirements of various building materials used in the construction project; Obtain consumption information for various types of building materials used in multiple historical building construction projects; For each type of building material used in the construction project, based on the consumption information of each type of building material used in the multiple historical construction projects, the consumption deviation fluctuation parameter of the building material is calculated; when the consumption deviation fluctuation parameter of the building material is greater than or equal to the preset consumption deviation similarity parameter threshold, the initial allowable consumption deviation range of the building material is single-corrected based on the construction environment and construction process of the construction project through a parameter correction model to determine the allowable consumption deviation range of the building material; when the consumption deviation fluctuation parameter of the building material is less than the preset consumption deviation similarity parameter threshold, the initial allowable consumption deviation range of the building material is used as the allowable consumption deviation range of the building material.

6. The intelligent planning system for construction projects according to claim 5, characterized in that: The material consumption analysis module is also used to: Determine the consumption deviation correlation parameters between any two building materials; The material consumption analysis module uses a parameter correction model to perform a single correction on the initial allowable consumption deviation range of the building materials based on the construction environment and construction process of the building construction project, and determines the allowable consumption deviation range of the building materials, including: Based on the construction environment and construction process of the construction project, a parameter correction model is used to perform a single correction on the initial allowable consumption deviation range of the construction materials to determine the intermediate allowable consumption deviation range of each type of construction material used in the construction project; Based on the consumption deviation association parameter between any two building materials, the intermediate allowable consumption deviation range of each type of building material used in the construction project is jointly corrected to determine the allowable consumption deviation range of each type of building material used in the construction project.

7. An intelligent planning system for construction projects according to any one of claims 1 to 6, characterized in that: The personalized analysis module generates a material usage control plan for the construction project based on the allowable consumption deviation range and usage requirements of various types of construction materials used in the construction project through the artificial intelligence language large model, including: Determine multiple construction nodes based on pre-processed initial data of the building construction project; Through the artificial intelligence language large model, based on the allowable consumption deviation range and usage requirements of various types of building materials used in the construction project, the usage control indicators and usage recommendations of the building materials for the multiple construction nodes are determined.

8. An intelligent planning system for construction projects according to any one of claims 1 to 6, characterized in that: It also includes a profit and risk assessment module for automatically generating pre-profit reports, contract risk analysis, contract negotiation recommendations, contract review reports, break-even analysis, and risk management strategies.

9. An intelligent planning method for a construction project, characterized in that: An intelligent planning system for a construction project according to any one of claims 1 to 8, comprising: Receive initial data for building construction projects; Preprocessing initial data of the building construction project; Determining various types of building materials used in the building construction project based on the pre-processed initial data of the building construction project; Obtaining the physical properties and usage requirements of various building materials used in the construction project; Determine the allowable consumption deviation range of various building materials used in the construction project based on the physical property parameters and usage requirements of various building materials used in the construction project; Generate a material usage control plan for the construction project based on the allowable consumption deviation range and usage requirements of various types of construction materials used in the construction project through an artificial intelligence language large model; The material usage control plan of the construction project is transmitted to the user terminal.

Images