Data credible sharing processing method and system for construction enterprise

Abstract

The invention provides a data credible sharing processing method and system for a construction enterprise, and relates to the technical field of industrial data process.The method comprises the steps that a design change file of a construction project is obtained, component parameters are extracted for standardization processing, and a standard component parameter set is generated; binding the unique hash value to form a trusted transmission packet, and synchronously transmitting the trusted transmission packet to the construction party and the purchaser; the construction party receives the trusted transmission packet, calculates first transmission accuracy, and calculates a process adaptation compatibility degree through a process adaptation model after verification is passed; the purchaser receives the trusted transmission packet, calculates second transmission accuracy, and calculates a material resource matching degree through a material matching model after verification is passed; and judging whether the process adaptation compatibility and the material resource matching degree meet a preset file issuing condition or not, and if yes, issuing the design change file. The technical problems that the credibility is insufficient and the transmission accuracy is difficult to guarantee in the construction enterprise data sharing process in the prior art are solved.

Description

Technical Field

[0001] This invention relates to the field of industrial data processing, and in particular to a data trust-sharing processing method and system for construction companies. Background Technology

[0002] Data sharing in the construction industry involves multiple stakeholders, including contractors, purchasers, and designers, and covers various types of data such as design parameters, process standards, material information, and cost plans.

[0003] However, traditional data sharing processes generally suffer from problems such as difficulty in ensuring the accuracy of data transmission and lack of effective verification mechanisms for data credibility. This can easily lead to data distortion, causing risks such as construction rework, project delays, and cost overruns, thus affecting the smooth progress of construction projects.

[0004] Therefore, there is an urgent need for a reliable data sharing processing method for construction companies to address the aforementioned industry pain points. Summary of the Invention

[0005] This invention addresses the technical problems of insufficient reliability and difficulty in ensuring transmission accuracy in the data sharing process of construction enterprises in the prior art, and provides a data reliable sharing processing method and system for construction enterprises.

[0006] The technical solution of the present invention to solve the above-mentioned technical problems is as follows: In a first aspect, the present invention provides a data trusted sharing processing method for construction companies, comprising: Obtain design change documents for construction projects, extract component parameters and standardize them to generate a standard component parameter set; A unique hash value is bound to the standard component parameter set to form a trusted transmission packet containing the standard component parameter set and the hash value, which is then synchronously transmitted to the construction party and the procurement party. The construction party receives the trusted transmission packet, calculates the first transmission accuracy, and after verification, calculates the process adaptation compatibility through the pre-trained process adaptation model. The purchasing party receives the trusted transmission packet, calculates the second transmission accuracy, and after verification, calculates the material resource matching degree through a pre-trained material matching model; Determine whether the process adaptation compatibility and the material resource matching degree meet the preset document issuance conditions. If they do, issue the design change document. If they do not, dynamically determine the adjustment strategy for the design change document until the preset document issuance conditions are met.

[0007] Secondly, the present invention provides a data trusted sharing and processing system for construction enterprises, comprising: The data acquisition module is used to acquire design change documents for construction projects, extract component parameters, perform standardization processing, and generate a standard component parameter set. The trusted transmission module is used to bind a unique hash value to the standard component parameter set, forming a trusted transmission packet containing the standard component parameter set and the hash value, and synchronously transmitting it to the construction party and the procurement party. The construction verification module is used by the construction party to receive the trusted transmission packet, calculate the first transmission accuracy, and after the verification is passed, calculate the process adaptation compatibility through a pre-trained process adaptation model. The procurement verification module is used by the procuring party to receive the trusted transmission packet, calculate the second transmission accuracy, and calculate the material resource matching degree through a pre-trained material matching model after the verification is passed. The output execution module is used to determine whether the process adaptation compatibility and the material resource matching degree meet the preset document issuance conditions. If they meet, the design change document is issued. If they do not meet, the adjustment strategy of the design change document is dynamically determined until the preset document issuance conditions are met.

[0008] The beneficial effects of this invention are: Compared to existing technologies, this application first obtains the design change documents of the construction project, extracts component parameters, and standardizes them to generate a standard component parameter set. This eliminates differences in the names, units, and formats of component parameters, ensuring the completeness and consistency of parameter extraction and providing unified basic data support for subsequent reliable parameter transmission and compatibility assessments by construction and procurement parties. Secondly, a unique hash value is bound to the standard component parameter set to form a reliable transmission packet containing the standard component parameter set and the hash value. This packet is simultaneously transmitted to the construction and procurement parties, effectively ensuring the integrity and authenticity of the standard component parameter set transmission and enabling synchronous reception of reliable data by both parties. This provides a secure and efficient data distribution foundation for subsequent compatibility assessments. Thirdly, the construction party receives the reliable transmission packet, calculates the initial transmission accuracy, and after verification, calculates the process compatibility degree using a pre-trained process adaptation model. This ensures the reliability of the standard component parameter data received by the construction party and accurately quantifies the process compatibility between the construction party and the design changes, providing a scientific basis for subsequent construction feasibility decisions. Furthermore, upon receiving the trusted transmission packet, the procuring party calculates the second transmission accuracy. After verification, a pre-trained material matching model is used to calculate the material resource matching degree, ensuring the reliability of the standard component parameter transmission received by the procuring party and accurately quantifying the matching degree between material resources and component requirements, providing a scientific basis for procurement decisions. Finally, it is determined whether the process adaptability and compatibility and material resource matching degree meet the preset document issuance conditions. If they do, a design change document is issued; otherwise, the adjustment strategy for the design change document is dynamically determined until the preset document issuance conditions are met. This ensures that the final issued design change document fully adapts to the construction party's process capabilities and the procuring party's material supply capabilities, avoiding blind modifications and improving the success rate of design change implementation.

[0009] Through the aforementioned technical solutions, this application standardizes the component parameters in design change documents and binds them with hash values ​​to construct trusted transmission packets, effectively ensuring the accuracy and reliability of data transmission. It verifies the accuracy of data transmission from both the construction and procurement parties and quantifies process compatibility and material resource matching through pre-trained models, achieving precise and efficient multi-party data collaboration. Furthermore, it determines whether design change documents can be issued based on preset document issuance conditions, ensuring the practical feasibility of design changes. This improves the reliability and accuracy of data sharing among construction companies, reduces the risk of rework, delays, and cost overruns caused by data issues, and strongly supports the smooth progress of construction projects. Attached Figure Description

[0010] Figure 1 This invention provides a flowchart illustrating a data trust sharing processing method for construction companies. Figure 2This invention provides a structural diagram of a trusted data sharing processing system for construction companies.

[0011] In the attached diagram, the components represented by each number are as follows: Data acquisition module 11, trusted transmission module 12, construction verification module 13, procurement verification module 14, and output execution module 15. Detailed Implementation

[0012] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0013] In the description of this invention, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the stated features. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0014] In the description of this invention, the term "for example" is used to mean "used as an example, illustration, or description." Any embodiment described as "for example" in this invention is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is provided to enable any person skilled in the art to make and use the invention. Details are set forth in the following description for purposes of explanation. It should be understood that those skilled in the art will recognize that the invention can be made without using these specific details. In other instances, well-known structures and processes will not be described in detail to avoid obscuring the description of the invention with unnecessary detail. Therefore, the invention is not intended to be limited to the embodiments shown, but is consistent with the broadest scope of the principles and features disclosed herein.

[0015] Example 1, as Figure 1 As shown, this embodiment of the invention provides a data trusted sharing processing method for construction companies, including: S10: Obtain the design change documents for the construction project, extract the component parameters and perform standardization processing to generate a standard component parameter set.

[0016] During the actual progress of construction projects, design changes are common for construction companies due to various factors such as dynamic changes in the construction environment, adjustments to owner requirements, optimization of technical parameters, and limitations of on-site construction conditions.

[0017] In existing technologies, design change documents often exist in various forms such as CAD drawings, BIM models, and text descriptions. Designers, contractors, and procurement parties all extract component parameters. However, the lack of unified standards for the naming, unit formats, and numerical specifications of component parameters among different stakeholders leads to discrepancies in their understanding of these parameters, creating potential problems for subsequent design changes that are disconnected from construction capabilities and procurement resources.

[0018] To address the aforementioned issues, this application obtains the design change documents of the construction project, extracts the component parameters, and performs standardization processing to generate a standard component parameter set.

[0019] Specifically, step S10 in the method includes: Extract component parameters from the design change document, wherein the component parameters include geometric parameters, mechanical parameters, process parameters, and material parameters; The component parameters are standardized in terms of name, unit, and format, and integrated into structured data according to fixed dimensions of geometric parameters, mechanical parameters, process parameters, and material parameters to generate a standard component parameter set.

[0020] In this embodiment, component parameters are first extracted from the design change documents. These parameters include geometric parameters, mechanical parameters, technological parameters, and material parameters. Specifically, design change documents typically include various formats such as CAD drawings, BIM model files, and change specification documents. The extraction of component parameters must cover the core information required for the entire process of component design, construction, and procurement.

[0021] Among them, geometric parameters refer to parameters that describe the external dimensions and spatial positional relationship of the component; mechanical parameters refer to parameters that reflect the load-bearing capacity and stress characteristics of the component; process parameters refer to parameters that guide the operational requirements of the component construction process; and material parameters refer to parameters that describe the material properties required for the component manufacturing process.

[0022] For example, the geometric parameters of a beam component extracted from the BIM model file are: beam length 6000mm, beam cross-section size 300mm×500mm, and allowable deviation of each parameter ±2mm; mechanical parameters are: concrete strength grade C35, beam design load value 25kN / m; process parameters are: formwork support process, steel bar welding process, concrete pouring process, and process accuracy requirement ±3mm; material parameters are: HRB400 grade Φ25mm steel bars, P.O42.5 grade cement, etc.

[0023] Secondly, the component parameters undergo naming standardization, unit unification, and format standardization. Following fixed dimensions such as geometric parameters, mechanical parameters, technological parameters, and material parameters, they are integrated into structured data to generate a standard component parameter set. Optionally, naming standardization can follow national building standard design terminology, such as the "Standard for Terminology of Building Structures" GB / T 50083, to unify parameter names and avoid misunderstandings caused by different expressions of the same parameter. Unit unification standardizes the units of parameters expressed differently to industry-standard units; for example, unifying length units to millimeters, strength units to megapascals, time units to days, and mass units to kilograms. Format standardization refers to unifying the presentation format of parameter values; for example, retaining two decimal places and clearly defining tolerance ranges.

[0024] For example, the standardized parameters are organized in key-value pairs according to a fixed order of geometric parameters, mechanical parameters, process parameters, and material parameters to form structured data that is easy for computers to recognize and process, thus generating a standard component parameter set. For example, the standard component parameter set of a certain beam component in the above example can be represented as: {Geometric parameters: [Beam length: 6000.00±2.00mm, Beam cross-sectional dimensions: 300.00×500.00±2.00mm], Mechanical parameters: [Concrete strength grade: C35, Design load value: 25.00kN / m], Process parameters: [Process type: formwork support, rebar welding, concrete pouring, Process accuracy: ±3.00mm], Material parameters: [Rebar type: HRB400 Φ25mm, Cement type: P.O42.5 grade]}.

[0025] In summary, compared to existing technologies, this application obtains design change documents from construction projects, extracts component parameters, and standardizes them to generate a standard component parameter set. This eliminates discrepancies in the names, units, and formats of component parameters, ensuring the completeness and consistency of parameter extraction. It provides unified basic data support for subsequent reliable parameter transmission and compatibility assessments in construction and procurement.

[0026] S20: Bind a unique hash value to the standard component parameter set to form a trusted transmission packet containing the standard component parameter set and the hash value, and transmit it synchronously to the construction party and the procurement party.

[0027] In existing technologies, when standard component parameter sets for design changes are transmitted to construction and procurement parties, there are problems such as data being easily tampered with, difficulty in verifying the authenticity of the source, separation of parameters and verification certificates leading to cumbersome verification, and asynchronous data reception by both parties, which slows down the subsequent compatibility assessment process.

[0028] To address the aforementioned issues, this application binds a unique hash value to the standard component parameter set, forming a trusted transmission packet containing the standard component parameter set and the hash value, which is then synchronously transmitted to the construction party and the procurement party. Specifically, a hash algorithm is used to jointly calculate a unique hash value by combining the structured standard component parameter set with the corresponding unique BIM model ID. The inclusion of the unique BIM model ID ensures that the standard component parameter set corresponds one-to-one with the specific entity component in the project, avoiding confusion between parameters of different components.

[0029] The hash value refers to a fixed-length unique identifier generated using an encrypted hash algorithm based on the complete content of the standard component parameter set and the unique BIM model ID of the corresponding component. Its characteristic is that even a slight change in the input content will result in a significant difference in the hash value, which can be used to accurately verify whether the data transmission process has been tampered with.

[0030] For example, if the standard component parameter set is the structured data of a beam component, and the unique ID of the bound BIM model is BM-001-L-003, the designer calculates a unique hash value in the form of a 64-bit string using the SHA-256 hash algorithm, for example, e3b0...

[0031] Furthermore, the standard component parameter set and hash value are integrated and encapsulated to form a trusted transmission packet, ensuring that the standard component parameter set and hash value are transmitted synchronously. Then, through a project-specific encrypted transmission channel, such as the SSL / TLS 1.3 protocol, the trusted transmission packet is simultaneously pushed to the access control systems of the construction party and the procurement party.

[0032] For example, the designer sends a trusted transmission packet simultaneously to the construction company's project management system and the procurement company's supply chain management system via an encrypted channel.

[0033] It should be noted that hash algorithms are well-known technologies in this field, and those skilled in the art can select the appropriate algorithm type and complete the corresponding calculations according to the actual application scenario. This application will not elaborate further on this.

[0034] In summary, compared to existing technologies, this application binds a unique hash value to the standard component parameter set, forming a trusted transmission packet containing the standard component parameter set and the hash value, which is then synchronously transmitted to the construction party and the procurement party. This effectively ensures the integrity and authenticity of the transmitted standard component parameter set, enabling the construction party and the procurement party to synchronously receive trusted data, and providing a secure and efficient data distribution foundation for subsequent compatibility assessment.

[0035] S30: The construction party receives the trusted transmission packet, calculates the first transmission accuracy, and after verification, calculates the process adaptation compatibility through the pre-trained process adaptation model.

[0036] In the existing technology, when the construction party receives standard component parameters related to design changes, there is a lack of effective means to verify the accuracy of data transmission, and it is difficult to quickly and accurately assess the degree of compatibility between its own process capabilities and the changed parameters. This can easily lead to problems such as rework and project delays in subsequent construction due to unreliable data or insufficient compatibility.

[0037] To address the aforementioned issues, the construction party in this application receives the trusted transmission packet, calculates the first transmission accuracy, and after verification, calculates the process adaptation compatibility using a pre-trained process adaptation model.

[0038] Specifically, step S30 in the method includes: The construction party receives the trusted transmission packet and calculates the first transmission accuracy by comparing the received hash value with the original hash value published by the designer. If the first transmission accuracy is greater than or equal to the preset accuracy threshold, the process adaptation compatibility is calculated through the pre-trained process adaptation model; If the first transmission accuracy is less than the preset accuracy threshold, the parameter transmission error information will be fed back to the designer, who will then regenerate and transmit the trusted transmission packet until the first transmission accuracy verification is passed.

[0039] In this embodiment, the construction party first receives a trusted transmission packet and calculates the first transmission accuracy by comparing the received hash value with the original hash value published by the designer. Specifically, after receiving the trusted transmission packet, the construction party first extracts the original hash value published by the designer from the trusted transmission packet, and then uses the same hash algorithm as the designer to calculate the received standard component parameter set and the corresponding BIM model unique ID to obtain a local hash value. Then, the local hash value is compared with the extracted original hash value. If the two are consistent, it means that the standard component parameter set has not been tampered with and is complete and error-free during transmission. If the two are inconsistent, the first transmission accuracy is calculated by comparing the received standard component parameter set with the standard component parameter set published by the designer item by item and counting the number of unbiased parameter items.

[0040] For example, the first transmission accuracy is calculated as the ratio of the number of unbiased parameter items to the total number of parameter items. For instance, if the total number of parameter items in a standard component parameter set is 20, and the hash values ​​are inconsistent, after checking each item, it is found that the number of unbiased parameter items is 18. Then the first transmission accuracy = (18 / 20) × 100% = 90%, indicating that there is a slight deviation.

[0041] Secondly, if the accuracy of the first transmission is greater than or equal to the preset accuracy threshold, the process adaptation compatibility is calculated using a pre-trained process adaptation model. The preset accuracy threshold can be dynamically set according to the project type and parameter transmission accuracy requirements. If the accuracy of the first transmission is lower than the preset accuracy threshold, it indicates a significant deviation in the standard component parameter set received by the construction party, requiring feedback to the design party for regeneration. Conversely, if the accuracy of the first transmission is greater than or equal to the preset accuracy threshold, it indicates a minor deviation in the parameter set, allowing for correction of abnormal parameters before continuing the subsequent process.

[0042] For example, for design changes to core structural components (such as beams and columns), the preset accuracy threshold can be increased to 98%; for design changes to general decorative components (such as decorative panels), the preset accuracy threshold can be reduced to 90%. Those skilled in the art can flexibly adjust this threshold according to the actual project requirements.

[0043] Finally, if the accuracy of the initial transmission is less than the preset accuracy threshold, the parameter transmission anomaly information is fed back to the designer, who then regenerates and transmits the trusted transmission packet until the accuracy of the initial transmission is verified. The parameter transmission anomaly information can be labeled with the anomaly type, such as parameter tampering, missing parameters, or mistransmission of parameters. It can also include a comparison between the locally calculated hash value and the original hash value, facilitating the designer's rapid problem identification.

[0044] For example, if the first transmission accuracy is less than a preset accuracy threshold, and the beam cross-section dimension parameters in the standard component parameter set received by the construction party are tampered with, the feedback parameter transmission anomaly information can be described as: {Anomaly type: parameter tampering, parameters involved: beam cross-section dimension, original hash value: XXX, local hash value: YYY, please retransmit the trusted transmission packet}. After receiving the feedback, the designer re-extracts the component parameters, generates the standard component parameter set and hash value, constructs a new trusted transmission packet, and sends it to the construction party until the first transmission accuracy meets the preset accuracy threshold.

[0045] Specifically, the phrase "calculating process adaptation compatibility through a pre-trained process adaptation model" includes: Call the pre-trained process adaptation model; Input the standard component parameter set and the actual construction conditions data of the construction party, wherein the actual construction conditions data includes at least equipment capacity, process reserves, and construction schedule; Output process feasibility parameters, schedule adaptability parameters, and cost controllability parameters; The process feasibility parameters, schedule adaptability parameters, and cost controllability parameters are weighted according to preset weights to obtain the initial process adaptability and compatibility. The ratio of the actual performance of similar projects in the past by the construction party to the benchmark requirements is obtained and used as the first correction factor; The final process adaptation compatibility is obtained by multiplying the initial process adaptation compatibility, the first correction coefficient, and the verified first transfer accuracy.

[0046] In this embodiment, a pre-trained process adaptation model is first invoked. This process adaptation model is a deep learning model containing a shared input layer and three branch networks, used to evaluate the contractor's ability to adapt to design change parameters.

[0047] Secondly, input the standard component parameter set and the actual construction conditions data of the construction party. The actual construction conditions data should include at least the following: equipment capacity, technological reserves, and construction schedule. Equipment capacity refers to the performance parameters and operating status of the construction party's existing equipment, such as equipment model, precision level, service life, and recent maintenance records. Technological reserves refer to the types of construction technologies mastered by the construction party, their maturity, and the skill level of construction personnel, such as whether the construction party has mastered high-precision formwork support technology and whether welding workers possess first-class welding qualifications. The construction schedule refers to the current construction progress arrangement, the planned completion time of each process, and the relationship between processes.

[0048] Next, output the process feasibility parameters, schedule adaptability parameters, and cost controllability parameters. The process feasibility parameter is a quantitative indicator of whether the contractor's existing equipment and processes can meet the requirements of the design change process; its value ranges from 0 to 100 points, with higher values ​​indicating stronger process feasibility. The schedule adaptability parameter is a quantitative indicator of the impact of implementing the design change on the existing schedule plan; its value ranges from 0 to 100 points, with higher values ​​indicating better schedule adaptability. The cost controllability parameter is a quantitative indicator of whether the additional costs resulting from the design change are within the budget; its value ranges from 0 to 100 points, with higher values ​​indicating stronger cost controllability.

[0049] For example, if the required process accuracy in the standard component parameter set is ±3mm, and the accuracy of the construction equipment is ±2mm, and the process maturity is high, then the pre-trained process adaptation model may output a process feasibility parameter of 90 points; if the implementation of the change requires an additional 2 days of construction time and does not affect the connection of subsequent processes, then the pre-trained process adaptation model may output a construction period adaptability parameter of 85 points; if the additional cost of the change is 50,000 yuan, which is within the project budget, then the pre-trained process adaptation model may output a cost controllability parameter of 95 points.

[0050] Furthermore, the process feasibility parameters, schedule adaptability parameters, and cost controllability parameters are weighted according to preset weights to obtain the initial process compatibility. The preset weights are dynamically set based on the priority of the construction project's requirements for process feasibility, schedule adaptability, and cost controllability, and the sum of the preset weights for these parameters is 1. For example, in a typical project, the preset weights can be set as follows: process feasibility weight 60%, schedule adaptability weight 20%, and cost controllability weight 20%. Those skilled in the art can flexibly adjust these weights according to the actual needs of the project.

[0051] The initial process compatibility calculation formula is: Initial process compatibility = Process feasibility parameter × Process feasibility weight + Schedule compatibility parameter × Schedule compatibility weight + Cost controllability parameter × Cost controllability weight.

[0052] For example, if the process feasibility parameter is 90 points, the schedule adaptability parameter is 85 points, and the cost controllability parameter is 95 points, and the corresponding weights are 60%, 20%, and 20% respectively, then the initial process adaptability compatibility = 90×60%+85×20%+95×20%=54+17+19=90 points.

[0053] Furthermore, the ratio of the actual performance of similar historical projects undertaken by the construction contractor to the benchmark requirements is obtained and used as the first correction coefficient. Here, similar historical projects refer to completed projects of the same type as the current project and with similar design changes; actual performance refers to data such as the actual quality of process completion, actual changes in schedule, and actual cost expenditure in historical projects; benchmark requirements refer to the preset indicators such as process standards, schedule plans, and cost budgets corresponding to the design changes in those historical projects.

[0054] For example, the calculation of the first correction factor requires taking into account the ratio of the actual performance of the contractor in similar projects in the past to the benchmark requirements in terms of three dimensions: process, schedule and cost, and taking the arithmetic mean of the three as the final first correction factor.

[0055] For example, if the historical benchmark requirement for similar projects was that the formwork support accuracy error should be ≤3mm, and the actual construction results showed that the formwork support accuracy error was 3mm, fully meeting the benchmark requirement, then the ratio of the actual completed quality to the benchmark requirement is 3mm / 3mm = 1.0. If the historical benchmark requirement for similar projects was that the construction period of beam components involved in the design change required 10 days, but due to efficient organization, it was completed in only 9.5 days, then the ratio of the actual change in construction period to the benchmark requirement is 9.5 days / 10 days = 0.95. If the historical benchmark requirement for similar projects was that the additional construction cost due to the design change should be ≤500,000 yuan, but the actual additional cost after construction was 525,000 yuan, then the ratio of the actual cost expenditure to the benchmark requirement is 525,000 yuan / 500,000 yuan = 1.05. Based on the ratios of the above three dimensions, the first correction coefficient is obtained as (1.0 + 0.95 + 1.05) / 3 = 1.0. This indicates that the overall execution effect of the construction party in similar historical projects is completely in line with the benchmark requirements and can be directly used to correct the initial process adaptation and compatibility of the current project.

[0056] Finally, the initial process adaptation compatibility, the first correction coefficient, and the verified first transfer accuracy are multiplied to obtain the final process adaptation compatibility. Here, process adaptation compatibility = initial process adaptation compatibility × first correction coefficient × first transfer accuracy. This is because the initial process adaptation compatibility is a theoretical evaluation result calculated based on the process adaptation model, which may deviate from the actual execution capabilities of the construction party, such as the model not fully covering special scenarios of the current project. The first correction coefficient can calibrate the gap between the model's theoretical value and the actual construction capability through the actual execution results of similar historical projects, making the evaluation more realistic. Simultaneously, the first transfer accuracy reflects the reliability of parameter transmission; parameter transmission deviation directly affects the quality of the basic data for the compatibility assessment. Including this in the calculation process reflects the impact of data reliability on the final result, ensuring that the evaluation result simultaneously considers model prediction accuracy, historical execution experience, and data transmission quality.

[0057] For example, if the initial process adaptation compatibility score is 90 points, the first correction coefficient is 1.0, and the first transmission accuracy is 90%, then the final process adaptation compatibility score is 90 × 1.0 × 90% = 81 points. This indicates that the construction party's ability to adapt to the current design changes is at a high level. Although there is a slight deviation in the transmission of the standard component parameter set, the overall construction feasibility is still good, based on the model evaluation results and historical execution experience.

[0058] Furthermore, the construction process of the "process adaptation model" includes: Collect historical standard component parameter sets corresponding to historical design changes, as well as historical actual construction condition data of the corresponding construction parties, and integrate them to form a sample construction dataset; Obtain historical process feasibility parameters, historical construction period adaptability parameters, and historical cost controllability parameters corresponding to the sample construction data, and use them as the sample construction supervision label set; Based on the random forest model, a branch for process feasibility assessment is constructed. Based on long short-term memory networks, a branch for evaluating project duration adaptability is constructed. Based on the gradient boosting model, a cost controllability evaluation branch is constructed; The three branches are connected to a shared input layer to form a process adaptation model; Using the sample construction dataset and sample construction supervision label set, the process adaptation model is trained and validated in a supervised manner, and training is completed after convergence.

[0059] In this embodiment, the historical standard component parameter set corresponding to historical design changes and the corresponding historical actual construction condition data of the construction party are first collected and integrated to form a sample construction dataset. The historical standard component parameter set refers to the standard component parameter set corresponding to design changes in past projects, and the historical actual construction condition data refers to the actual data such as the construction party's equipment capabilities, process reserves, and schedule plans in past projects.

[0060] For example, a large number of historical standard component parameters covering different project types (such as building construction, municipal engineering, and water conservancy projects) and different component types (such as beams, columns, load-bearing walls, and decorative panels) are collected from historical design change data to construct a historical standard component parameter set; at the same time, historical actual construction condition data of the construction party, including dimensions such as equipment capacity, process reserves, and construction schedule, are obtained, and the two types of data are integrated according to the corresponding relationship to form a sample construction dataset.

[0061] Secondly, historical process feasibility parameters, historical construction period adaptability parameters, and historical cost controllability parameters corresponding to the sample construction data are obtained as the sample construction supervision label set. For example, historical process feasibility parameters, historical construction period adaptability parameters, and historical cost controllability parameters are extracted from the project completion archives to serve as the sample construction supervision label set, ensuring the accuracy of the supervision labels.

[0062] Furthermore, a process feasibility assessment branch is constructed based on the random forest model. The random forest model is an ensemble learning algorithm that improves assessment accuracy by constructing multiple decision trees and combining their predictions; it is suitable for handling classification and regression problems with multi-dimensional features.

[0063] For example, the process feasibility assessment branch is constructed based on a multi-decision tree ensemble model using the random forest algorithm. It mainly consists of an input layer, a feature processing module, multiple decision trees, and an output layer. The input layer receives a standard component parameter set and the actual construction conditions data from the construction party. The feature processing module standardizes and filters the input data based on importance. The multi-decision tree ensemble layer contains 50-100 independent CART decision trees, the number of which can be dynamically adjusted according to the sample size. Each decision tree extracts different samples from the training set through bootstrap sampling and randomly selects some features for splitting, such as using only 2 / 3 of the total features in each tree to reduce the risk of overfitting. The output layer integrates the prediction results of all decision trees using a majority voting strategy, and finally outputs a process feasibility parameter of 0-100 points, reflecting the construction party's ability to meet the process requirements of design changes.

[0064] Furthermore, a project schedule adaptability assessment branch is constructed based on a Long Short-Term Memory (LSTM) network. LSTM is a special type of recurrent neural network that can effectively process time-series data, capture long-term dependencies within the data, and is suitable for assessment scenarios with time-series characteristics, such as project schedules.

[0065] For example, the schedule adaptability assessment branch is constructed based on a time series model of a Long Short-Term Memory (LSTM) network. It mainly consists of an input layer, a time series feature processing module, an LSTM network layer, a fully connected layer, and an output layer. The input layer receives a standard component parameter set and the actual construction conditions data from the construction party. The time series feature processing module preprocesses the input data, such as filling missing values, standardization, and sequence alignment. The LSTM network layer contains 2-3 stacked LSTM units, each with 64-128 hidden units. It captures long-term dependencies in the data through a gating mechanism, such as identifying the chain reaction of design changes and new procedures on the schedule of subsequent key nodes. A dropout layer is also introduced with a dropout rate of 0.2 to prevent overfitting. The fully connected layer converts the time series feature vector output by the LSTM network into a fixed-dimensional feature vector and performs nonlinear mapping through the ReLU activation function to enhance the feature extraction of key influencing factors. The output layer uses a linear activation function to output a schedule adaptability parameter of 0-100 points, reflecting the degree of impact of design changes on the existing schedule plan.

[0066] Furthermore, a cost controllability assessment branch is constructed based on the gradient boosting model. The gradient boosting model (such as XGBoost) is an iterative decision tree algorithm that corrects the errors of the previous model by continuously building new decision trees. It is suitable for handling nonlinear regression problems and can accurately capture the complex relationship between cost and various influencing factors.

[0067] For example, the cost controllability assessment branch is constructed using an ensemble model based on Gradient Boosting Tree (XGBoost), which mainly consists of an input layer, a feature engineering module, a gradient boosting tree ensemble layer, and an output layer. The input layer receives a set of standard component parameters and actual construction conditions data from the construction party. The feature engineering module performs fine-tuning on the input data, such as numerical normalization, feature crossing, and outlier correction. The gradient boosting tree ensemble layer contains 100-200 CART regression trees, with the number dynamically adjusted according to the sample size. Each new tree fits the prediction residuals of the preceding ensemble model, while controlling the tree depth, the number of leaf nodes, and the learning rate to prevent overfitting. The output layer maps the output of the ensemble model to a cost controllability parameter of 0-100 points through linear transformation, reflecting the degree of cost fluctuation caused by design changes.

[0068] Furthermore, the three branches are connected to the shared input layer to form a process adaptation model. Specifically, the shared input layer synchronously inputs the standard component parameter set and the actual construction conditions data of the construction party into the process feasibility assessment branch, the schedule adaptability assessment branch, and the cost controllability assessment branch. Each branch performs further feature extraction and evaluation calculations based on its corresponding specific characteristics, and outputs the corresponding evaluation parameters.

[0069] Finally, the process adaptation model is trained and validated in a supervised manner using a sample construction dataset and a sample construction supervision label set, completing the training upon convergence. For example, the process adaptation model can be trained using the following technical path: the sample construction dataset and the corresponding sample construction supervision label set are divided into a training set and a validation set in a 7:3 ratio. The sample construction data in the training set are used as input features. The mean squared error loss function is used to minimize the deviation between the model's predicted values ​​and the supervision labels. The model parameters are iteratively updated using gradient descent, and the generalization performance is monitored in real time using the validation set. The model training is considered converged when the rate of change of the validation set loss over five consecutive iterations is less than 1e-5 or the preset maximum number of iterations is reached, resulting in the trained process adaptation model.

[0070] In summary, compared to existing technologies, this application allows the construction party to receive the trusted transmission packet, calculate the first transmission accuracy, and after verification, calculate the process adaptation compatibility using a pre-trained process adaptation model. This ensures the reliability of the standard component parameter data received by the construction party and accurately quantifies the degree of process adaptation between the construction party and design changes, providing a scientific basis for subsequent construction feasibility decisions.

[0071] S40: The purchasing party receives the trusted transmission packet, calculates the second transmission accuracy, and after verification, calculates the material resource matching degree through the pre-trained material matching model.

[0072] In existing technologies, when the purchaser receives standard component parameters corresponding to design changes, there is a lack of effective means to verify the accuracy of data transmission. Furthermore, the material resource matching assessment relies heavily on manual experience, making it difficult to accurately match component demand with material supply capacity. This can easily lead to problems such as procurement errors, material waste, or project delays.

[0073] To address the aforementioned issues, the procuring party in this application receives the trusted transmission packet, calculates the second transmission accuracy, and after verification, calculates the material resource matching degree using a pre-trained material matching model.

[0074] Specifically, step S40 in the method includes: The purchaser receives the trusted transmission packet and calculates the second transmission accuracy by comparing the received hash value with the original hash value published by the designer. If the second transmission accuracy is greater than or equal to the preset accuracy threshold, the material resource matching degree is calculated using the pre-trained material matching model; If the second transmission accuracy is less than the preset accuracy threshold, the parameter transmission error information will be fed back to the designer, who will then regenerate and transmit the trusted transmission packet until the second transmission accuracy verification is passed.

[0075] In this embodiment, the procuring entity first receives a trusted transmission packet and calculates the second transmission accuracy by comparing the received hash value with the original hash value published by the designer. Exemplarily, the calculation method for the second transmission accuracy is the same as for the first transmission accuracy: After receiving the trusted transmission packet, the procuring entity first extracts the original hash value published by the designer from the trusted transmission packet. Then, using the same hash algorithm as the designer, it calculates the local hash value for the received standard component parameter set and the corresponding unique BIM model ID. Next, it compares the local hash value with the extracted original hash value. If they match, it indicates that the standard component parameter set has not been tampered with and is complete and error-free during transmission. If they do not match, the second transmission accuracy is calculated by comparing the received standard component parameter set with the standard component parameter set published by the designer item by item, counting the number of unbiased parameter items. For example, if the second transmission accuracy is 95% obtained using the same calculation method as the first transmission accuracy, it indicates a slight deviation.

[0076] Secondly, if the accuracy of the second transmission is greater than or equal to the preset accuracy threshold, the material resource matching degree is calculated using a pre-trained material matching model. The preset accuracy threshold is consistent with the preset accuracy threshold used by the construction party.

[0077] Finally, if the accuracy of the second transmission is less than the preset accuracy threshold, the parameter transmission anomaly information is fed back to the designer, who then regenerates and transmits the trusted transmission packet until the accuracy of the second transmission passes verification. For example, if the accuracy of the second transmission is less than the preset accuracy threshold, the parameter transmission anomaly information is fed back to the designer. Upon receiving the feedback, the designer re-extracts the component parameters, generates a standard component parameter set and hash value, constructs a new trusted transmission packet, and sends it to the purchaser until the accuracy of the second transmission meets the preset accuracy threshold.

[0078] Specifically, the phrase "calculating the material resource matching degree through a pre-trained material matching model" includes: Call the pre-trained material matching model; Input the standard component parameter set and the actual resource data of the purchaser, wherein the actual resource data includes at least supplier qualifications, supply chain timeliness records, and material cost benchmarks; Output supplier capability matching parameters, supply timeliness matching parameters, and cost adaptability parameters; The supplier capability matching parameter, supply timeliness matching parameter, and cost adaptability parameter are weighted according to preset weights to obtain the initial material resource matching degree. The ratio of the actual supply performance of similar projects in the past to the benchmark requirements is obtained and used as the second correction factor. The final material resource matching degree is obtained by multiplying the initial material resource matching degree, the second correction coefficient, and the verified second transfer accuracy.

[0079] In this embodiment, a pre-trained material matching model is first invoked. This material matching model is a deep learning model based on a shared input layer and a three-branch network, used to evaluate the purchasing party's ability to match material parameters to design changes.

[0080] Secondly, input the standard component parameter set and the actual resource data of the purchaser. The actual resource data includes at least supplier qualifications, supply chain timeliness records, and material cost benchmarks: Supplier qualifications refer to the qualification level, production capacity, and quality certification status of the purchaser's cooperating suppliers, such as whether the supplier has ISO9001 quality system certification and whether its annual production capacity reaches 100,000 tons; Supply chain timeliness records refer to the supplier's historical delivery cycle, transportation route, transportation method, and historical delays, such as the supplier's average delivery cycle being 7 days, transportation method being road transport, and historical delay rate being 5%; Material cost benchmarks refer to the purchaser's budgeted prices for various materials, historical purchase prices, and market price fluctuations, such as the budgeted price of Φ25mm steel bars being 4800 yuan / ton, the historical average purchase price being 4700 yuan / ton, and the recent market price fluctuation range being ±3%, etc.

[0081] Next, output the supplier capability matching parameters, supply timeliness matching parameters, and cost adaptability parameters. The supplier capability matching parameter refers to a quantitative indicator of whether the supplier can provide materials that meet the design change requirements, with a value range of 0-100. A higher parameter indicates a stronger matching degree. The supply timeliness matching parameter refers to a quantitative indicator of whether the supplier's delivery cycle can meet the construction schedule requirements, with a value range of 0-100. A higher parameter indicates a better matching degree. The cost adaptability parameter refers to a quantitative indicator of whether the material procurement cost can be controlled within the budget, with a value range of 0-100. A higher parameter indicates a stronger cost adaptability.

[0082] For example, if the standard component parameter set requires a steel bar yield strength ≥ 400MPa, and the supplier can provide a steel bar yield strength of 410MPa, the material matching model may output a supplier capability matching parameter of 95 points; if the standard component parameter set requires materials to arrive within 10 days, and the supplier's average delivery cycle is 7 days, the material matching model may output a supply timeliness matching parameter of 90 points; if the standard component parameter set has a material budget price of 4800 yuan / ton and a supplier's quotation of 4750 yuan / ton, the material matching model may output a cost suitability parameter of 98 points.

[0083] Furthermore, the supplier capability matching parameter, supply timeliness matching parameter, and cost adaptability parameter are weighted according to preset weights to obtain the initial material resource matching degree. The preset weights are dynamically set based on the priority of the construction project's needs for supplier capability, supply timeliness, and cost, and the sum of the preset weights for the supplier capability matching parameter, supply timeliness matching parameter, and cost adaptability parameter is 1. For example, in a typical project, the preset weights can be set as follows: supplier capability matching parameter 50%, supply timeliness matching parameter 30%, and cost adaptability parameter 20%. Those skilled in the art can flexibly adjust these weights according to the actual needs of the project.

[0084] For example, if the supplier capability matching parameter is 95 points, the supply timeliness matching parameter is 90 points, and the cost adaptability parameter is 98 points, the initial material resource matching degree is calculated according to the preset weights as 95×50%+90×30%+98×20%=94.1 points.

[0085] Furthermore, the ratio of the actual supply performance of similar historical projects to the benchmark requirements is obtained as a second correction factor. Here, similar historical projects refer to completed projects with the same material types and similar procurement scale as the current project; actual supply performance refers to data such as the actual quality, delivery time, and procurement cost of materials in historical projects; and benchmark requirements refer to the material design requirements, procurement budget, and timeline plans corresponding to historical projects.

[0086] For example, the calculation of the second correction factor requires considering the ratios of the actual supply effect to the benchmark requirements across three dimensions: supplier capability, supply timeliness, and cost. The average value is then taken as the final second correction factor. For instance, in a similar historical project, the ratio of the actual material quality to the benchmark requirements was 1.0, the ratio of the delivery time to the benchmark requirements was 0.9 (early delivery), and the ratio of the procurement cost to the benchmark requirements was 1.02 (slight overspending). Therefore, the second correction factor = (1.0 + 0.9 + 1.02) / 3 = 0.973.

[0087] Finally, the initial material resource matching degree, the second correction coefficient, and the verified second transfer accuracy are multiplied to obtain the final material resource matching degree. The formula for calculating the material resource matching degree is: Material Resource Matching Degree = Initial Material Resource Matching Degree × Second Correction Coefficient × Second Transfer Accuracy. This is because the initial material resource matching degree is a preliminary assessment value output by the material matching model and may contain deviations such as not considering actual working conditions and material losses. The second correction coefficient can specifically compensate for these deviations and calibrate the impact of material performance fluctuations and environmental changes on the matching degree. The second transfer accuracy is used to ensure the authenticity and completeness of material resource information during the transfer process, avoiding information distortion that leads to inaccurate matching degree assessments. Multiplying these three factors together achieves accurate quantification of the material resource matching degree.

[0088] For example, if the initial material resource matching degree is 94.1 points, the second correction coefficient is 0.973, and the second transmission accuracy is 95%, then the final material resource matching degree = 94.1 × 0.973 × 0.95 = 86.98 points.

[0089] Furthermore, the construction process of the "material matching model" includes: Collect historical standard component parameter sets from historical design changes, as well as historical resource data from the corresponding purchasers, and integrate them to form a sample procurement dataset; Obtain the historical supplier capability matching parameters, historical supply timeliness matching parameters, and historical cost adaptability parameters corresponding to the sample procurement dataset, and use them as the sample procurement supervision label set; Based on the knowledge graph model, construct a supplier capability matching branch; Based on long short-term memory networks, construct supply timeliness matching branches; Based on the gradient boosting model, a cost adaptability evaluation branch is constructed. The three branches are connected to a shared input layer to form a material matching model; Using the sample procurement dataset and sample procurement supervision label set, the material matching model is trained and validated in a supervised manner, and training is completed after convergence.

[0090] In this embodiment, the historical standard component parameter set of historical design changes and the corresponding historical resource data of the purchaser are first collected and integrated to form a sample procurement dataset. For example, a large number of historical standard component parameters covering different project types (such as building construction, municipal engineering, and water conservancy projects) and different component types (such as beams, columns, load-bearing walls, and decorative panels) are collected from the historical design change data to construct a historical standard component parameter set; at the same time, historical resource data of the purchaser, including supplier qualifications, supply chain timeliness records, and material cost benchmarks, are obtained, and the two types of data are integrated according to their corresponding relationships to form a sample procurement dataset.

[0091] Secondly, obtain the historical supplier capability matching parameters, historical supply timeliness matching parameters, and historical cost adaptability parameters corresponding to the sample procurement dataset, and use them as the sample procurement supervision label set. For example, extract historical supplier capability matching parameters, historical supply timeliness matching parameters, and historical cost adaptability parameters from the project completion archives, and use them as the sample construction supervision label set to ensure the accuracy of the supervision labels.

[0092] Secondly, a supplier capability matching branch is constructed based on a knowledge graph model. The knowledge graph model is a semantic network based on a graph structure, which can clearly represent the relationships between entities and is suitable for handling complex relationship assessments between suppliers and material requirements.

[0093] For example, the supplier capability matching branch is built based on a knowledge graph model, mainly consisting of an entity extraction layer, a relation definition layer, a knowledge graph storage layer, an inference and computation layer, and an output layer. The entity extraction layer receives a standard component parameter set and the actual resource data of the purchaser. The relation definition layer constructs a semantic relationship network between entities, standardizes relation definitions through an ontology rule base, and forms a structured knowledge graph. The knowledge graph storage layer uses a graph database (such as Neo4j) to store entity and relation data, stored in a structured form of triples, such as (Supplier A, supplies, prefabricated panels), (Supplier A, possesses, capacity 500 pieces / month), etc., and associates numerical features of entity attributes, supporting efficient graph traversal and related queries. The inference and computation layer performs matching degree inference based on the association paths of the knowledge graph, including path weight calculation and similarity matching, and calculates a comprehensive matching score. The output layer maps the inference results to supplier capability matching parameters ranging from 0 to 100 points. The higher the supplier capability matching parameter, the stronger the adaptability of the supplier's capacity, qualifications, and performance capabilities to the current component requirements.

[0094] Furthermore, a supply timeliness matching branch is constructed based on the Long Short-Term Memory (LSTM) network. The LSTM network can effectively process time-series data such as supply chain timeliness, capturing the temporal correlation between supply cycles and construction schedule requirements.

[0095] For example, the supply timeliness matching branch is built based on a Long Short-Term Memory (LSTM) network and mainly consists of an input layer, a temporal feature preprocessing module, an LSTM network layer, a fully connected layer, and an output layer. The input layer receives a standard component parameter set and the actual resource data from the purchaser. The temporal feature preprocessing module standardizes the input data, repairs missing values, and aligns the timelines to ensure consistency and integrity. The LSTM network layer contains 2-3 stacked LSTM units, each with 64-128 hidden units. It accurately captures long-term temporal dependencies in the supply chain through a gating mechanism and introduces a dropout layer with a dropout rate of 0.2 to reduce the risk of overfitting. The fully connected layer maps the high-dimensional temporal features output by the LSTM network to fixed-dimensional feature vectors and enhances feature extraction of key influencing factors through the ReLU activation function. The output layer uses a Sigmoid activation function to map the model output to a supply timeliness matching parameter ranging from 0 to 100. A higher supply timeliness matching parameter indicates a stronger fit between the supplier's supply capacity and the purchaser's schedule, better meeting the component arrival timeliness requirements.

[0096] Furthermore, a cost suitability assessment branch is constructed based on the gradient boosting model. The gradient boosting model (such as XGBoost) can accurately capture the nonlinear relationship between material cost and various influencing factors, making it suitable for cost suitability assessment.

[0097] For example, the cost suitability assessment branch is built based on a gradient boosting tree ensemble model, mainly consisting of an input layer, a cost feature engineering module, a gradient boosting tree ensemble layer, a feature fusion layer, and an output layer. The input layer receives a standard component parameter set and the actual resource data of the purchaser. The cost feature engineering module performs refined processing on the input features, such as numerical normalization, feature crossing, and outlier correction. The gradient boosting tree ensemble layer uses 150-200 CART regression trees to build an ensemble model, trained using a residual iterative fitting strategy. Each new tree optimizes the prediction error of the preceding model, while regularization constraints are applied by controlling the tree depth, the number of leaf nodes, and the learning rate. The feature fusion layer weights and fuses the basic cost features output by the ensemble model with the purchaser's cost constraint features, assigning higher weights to key constraint features through an attention mechanism to enhance the model's sensitivity to cost controllability boundaries. The output layer maps the fusion result to a cost suitability parameter of 0-100 points through a linear transformation. The higher the cost suitability parameter, the higher the matching degree between the current component demand and the supplier's quotation and the purchaser's cost constraints, and the stronger the cost controllability.

[0098] Furthermore, the three branches are connected to a shared input layer to form a material matching model. The shared input layer is used to extract common material features from the historical standard component parameter set, such as material type and performance level. The extracted common features are simultaneously input into the three evaluation branches, and each branch further processes them in conjunction with specific features, ultimately outputting the corresponding evaluation parameters.

[0099] Finally, the material matching model is trained and validated in a supervised manner using a sample procurement dataset and a sample procurement supervised label set, completing the training upon convergence. For example, the training process of the material matching model is consistent with that of the process adaptation model, and can be found in step S30.

[0100] In summary, compared to existing technologies, this application allows the procuring party to receive the trusted transmission packet, calculate the second transmission accuracy, and, after verification, calculate the material resource matching degree using a pre-trained material matching model. This ensures the reliability of the standard component parameter transmission received by the procuring party and accurately quantifies the matching degree between material resources and component requirements, providing a scientific basis for procurement decisions.

[0101] S50: Determine whether the process adaptation compatibility and the material resource matching degree meet the preset document issuance conditions. If they meet, issue the design change document. If they do not meet, dynamically determine the adjustment strategy for the design change document until the preset document issuance conditions are met.

[0102] The aforementioned steps yielded the process compatibility degree, which reflects the contractor's technological capabilities and the adaptation of design changes, as well as the material resource matching degree, which reflects the matching of the purchaser's material supply and component demand. Based on this, the actual feasibility of implementing design changes can be comprehensively assessed, and then it can be determined whether to issue design change documents to the relevant implementing parties.

[0103] To address the aforementioned issues, this application determines whether the process compatibility and material resource matching meet the preset document issuance conditions. If they do, a design change document is issued; otherwise, the designer modifies the design change document until the preset document issuance conditions are met.

[0104] Specifically, step S50 in the method includes: Determine whether the process adaptation and compatibility meets a preset process threshold, and whether the material resource matching degree meets a preset material threshold; If all conditions are met, issue a design change document; If the process adaptation compatibility does not meet the preset process threshold, or the material resource matching degree does not meet the preset material threshold, calculate the dual-dimensional joint adaptation coefficient and identify the associated constraint relationship; The adjustment strategy for the design change document is determined based on the dual-dimensional joint adaptation coefficient and the associated constraint relationship until the preset document issuance conditions are met.

[0105] In this embodiment, it is first determined whether the process compatibility meets a preset process threshold and whether the material resource matching meets a preset material threshold. If both are met, a design change document is issued. The preset process threshold and preset material threshold are dynamically set according to the project type and the importance level of the components. For example, the preset process threshold and preset material threshold for core structural components can be set to 80 points, while the preset process threshold and preset material threshold for general decorative components can be set to 70 points.

[0106] For example, if the preset process threshold and the preset material threshold are both 80 points, the process adaptation compatibility is 81 points, and the material resource matching degree is 86.98 points, then the preset process threshold and the preset material threshold are met. This indicates that the design change can meet the actual conditions of construction and procurement. The design change documents and the corresponding standard component parameter set can be issued to the construction party and the procurement party simultaneously to guide the subsequent construction and procurement work.

[0107] Conversely, if the process compatibility does not meet the preset process threshold, or the material resource matching does not meet the preset material threshold, a two-dimensional joint adaptation coefficient is calculated, and the associated constraints are identified. The formula for calculating the two-dimensional joint adaptation coefficient is: Two-dimensional joint adaptation coefficient = [(Process compatibility / Preset process threshold + Material resource matching / Preset material threshold) / 2 × 100%]. The two-dimensional joint adaptation coefficient can intuitively reflect the overall compliance level of process compatibility and material matching, avoiding extreme cases where a slight failure in a single dimension necessitates a complete overhaul.

[0108] For example, if both the preset process threshold and the preset material threshold are 80 points, when the process compatibility is 79 points and the material resource matching is 83 points, the dual-dimensional joint compatibility coefficient = [(79 / 80+83 / 80) / 2]×100%=101.25%, indicating that although the process compatibility is slightly lower than the preset process threshold, the material resource matching exceeds the standard, and the overall compatibility level of both is higher than the benchmark requirement.

[0109] The identification of related constraints refers to the following: If only the process compatibility does not meet the preset process threshold, but the material resource matching degree meets the preset material threshold, it is determined whether process parameters such as processing accuracy, process requirements, and equipment compatibility standards exceed the characteristic support range of the current compliant materials, or whether the process requirements excessively restrict the application scenario of the materials. In this case, the related constraint relationship is determined to be a one-way constraint from the process side to the material side. If only the material resource matching degree does not meet the preset material threshold, but the process compatibility meets the preset process threshold, it is determined whether material parameters such as specifications, material standards, and supply timeliness conflict with the process implementation path, or whether the material demand exceeds the supply range that the existing process can adapt to. In this case, the related constraint relationship is determined to be a one-way constraint from the material side to the process side. If both the process compatibility does not meet the preset process threshold and the material resource matching degree does not meet the preset material threshold, the related constraint relationship is determined to be a two-way constraint between the process side and the material side.

[0110] For example, if both the preset process threshold and the preset material threshold are 80 points, when the process compatibility score is 79 points and the material resource matching score is 83 points, only the process compatibility score does not meet the preset process threshold, but the material resource matching score meets the preset material threshold. It is determined whether the process parameters such as processing accuracy, process requirements, and equipment compatibility standards exceed the characteristic support range of the current compliant material, or whether the process requirements excessively restrict the application scenario of the material. After judgment, the requirement of processing accuracy ±0.3mm in the process parameters exceeds the characteristic support range of the maximum processing accuracy ±0.5mm of the current compliant material, and the associated constraint relationship is determined to be a one-way constraint on the material side by the process side.

[0111] Secondly, the adjustment strategy for design change documents is determined based on the dual-dimensional joint adaptation coefficient and the associated constraint relationship, until the preset document issuance conditions are met. The preset document issuance conditions refer to whether the process adaptation compatibility meets a preset process threshold and whether the material resource matching degree meets a preset material threshold.

[0112] Ideally, a mapping relationship between the two-dimensional joint adaptation coefficient, the associated constraints, and the modification rules can be pre-constructed. For example, it can be divided into the following reasonable ranges: if the two-dimensional joint adaptation coefficient is between 90% and 120%, and the associated constraints are either a one-way constraint from the process side to the material side or a one-way constraint from the material side to the process side, then the design parameters corresponding to the non-compliant side should be fine-tuned first, without the need for comprehensive modification; if the two-dimensional joint adaptation coefficient is <90%, or the associated constraints are a two-way constraint from the process side to the material side, then the designer should modify the conflicting parameters specifically, and optimize the process and material-related requirements simultaneously if necessary.

[0113] For example, if the preset process threshold and material threshold are both 80 points, the process compatibility score is 79 points, and the material resource matching score is 83 points, the calculated dual-dimensional joint adaptation coefficient is 101.25%, which is in the 90%-120% range. The correlation constraint relationship is that the process side unilaterally constrains the material side, and it is because the processing accuracy requirement exceeds the material characteristic support range. Then, based on the mapping rule, the adjustment strategy is determined as follows: only fine-tune the processing accuracy requirement in the process parameters, such as relaxing it from ±0.3mm to ±0.5mm, without modifying the material-related parameters.

[0114] For example, after the modification is completed, the process adaptation compatibility and material resource matching degree need to be recalculated according to the methods of S10, S20, S30 and S40, and compared with the preset process threshold and preset material threshold until the process adaptation compatibility and material resource matching degree both meet the preset process threshold and preset material threshold, and then the design change document is issued.

[0115] In summary, compared to existing technologies, this application determines whether the process compatibility and material resource matching meet the preset document issuance conditions. If they do, a design change document is issued; otherwise, an adjustment strategy for the design change document is dynamically determined until the preset document issuance conditions are met. Thus, by verifying whether the process compatibility and material resource matching meet the standards, and dynamically formulating differentiated adjustment strategies, this ensures that the final issued design change document fully adapts to the construction party's process capabilities and the procurement party's material supply capabilities, avoiding blind modifications and improving the success rate of design change implementation.

[0116] In summary, the embodiments of this application have at least the following technical effects: Compared to existing technologies, this application first obtains the design change documents of the construction project, extracts component parameters, and standardizes them to generate a standard component parameter set. This eliminates differences in the names, units, and formats of component parameters, ensuring the completeness and consistency of parameter extraction, and providing unified basic data support for subsequent reliable parameter transmission and compatibility assessments at the construction and procurement ends.

[0117] Secondly, this application binds a unique hash value to the standard component parameter set, forming a trusted transmission packet containing the standard component parameter set and the hash value, which is simultaneously transmitted to the construction party and the procurement party. This effectively ensures the integrity and authenticity of the transmitted standard component parameter set, enabling the construction party and the procurement party to receive trusted data synchronously, and providing a secure and efficient data distribution foundation for subsequent compatibility assessment.

[0118] Furthermore, upon receiving the trusted transmission packet, the construction party calculates the first transmission accuracy. After verification, the process adaptation compatibility is calculated using a pre-trained process adaptation model. This ensures the reliability of the standard component parameter data received by the construction party and accurately quantifies the degree of process adaptation between the construction party and the design changes, providing a scientific basis for subsequent construction feasibility decisions.

[0119] Furthermore, upon receiving the trusted transmission packet, the procuring entity calculates the second transmission accuracy. After successful verification, it calculates the material resource matching degree using a pre-trained material matching model. This ensures the reliability of the standard component parameter transmission received by the procuring entity and accurately quantifies the matching degree between material resources and component requirements, providing a scientific basis for procurement decisions.

[0120] Finally, this application determines whether the process compatibility and material resource matching meet the preset document issuance conditions. If they do, a design change document is issued; otherwise, an adjustment strategy for the design change document is dynamically determined until the preset document issuance conditions are met. In this way, by verifying whether the process compatibility and material resource matching meet the standards, and dynamically formulating differentiated adjustment strategies, this ensures that the final issued design change document fully adapts to the construction party's process capabilities and the procurement party's material supply capabilities, avoiding blind modifications and improving the success rate of design change implementation.

[0121] Through the aforementioned technical solutions, this application standardizes the component parameters in design change documents and binds them with hash values ​​to construct trusted transmission packets, effectively ensuring the accuracy and reliability of data transmission. It verifies the accuracy of data transmission from both the construction and procurement parties and quantifies process compatibility and material resource matching through pre-trained models, achieving precise and efficient multi-party data collaboration. Furthermore, it determines whether design change documents can be issued based on preset document issuance conditions, ensuring the practical feasibility of design changes. This improves the reliability and accuracy of data sharing among construction companies, reduces the risk of rework, delays, and cost overruns caused by data issues, and strongly supports the smooth progress of construction projects.

[0122] Example 2, as Figure 2 As shown, based on the same inventive concept as the data trusted sharing processing method for construction companies provided in Embodiment 1, this embodiment of the invention also provides a data trusted sharing processing system for construction companies, including: Data acquisition module 11 is used to acquire design change documents of construction projects, extract component parameters and perform standardization processing to generate a standard component parameter set; Trusted transmission module 12 is used to bind a unique hash value to the standard component parameter set to form a trusted transmission packet containing the standard component parameter set and the hash value, and synchronously transmit it to the construction party and the procurement party. Construction verification module 13 is used by the construction party to receive the trusted transmission packet, calculate the first transmission accuracy, and after verification, calculate the process adaptation compatibility through a pre-trained process adaptation model. The procurement verification module 14 is used by the procuring party to receive the trusted transmission packet, calculate the second transmission accuracy, and calculate the material resource matching degree through a pre-trained material matching model after the verification is passed. The output execution module 15 is used to determine whether the process adaptation compatibility and the material resource matching degree meet the preset document issuance conditions. If they meet, the design change document is issued. If they do not meet, the adjustment strategy of the design change document is dynamically determined until the preset document issuance conditions are met.

[0123] Specifically, the data acquisition module 11 is used for: Extract component parameters from the design change document, wherein the component parameters include geometric parameters, mechanical parameters, process parameters, and material parameters; The component parameters are standardized in terms of name, unit, and format, and integrated into structured data according to fixed dimensions of geometric parameters, mechanical parameters, process parameters, and material parameters to generate a standard component parameter set.

[0124] The trusted transmission module 12 is specifically used for: A unique hash value is bound to the standard component parameter set to form a trusted transmission packet containing the standard component parameter set and the hash value, which is then synchronously transmitted to the construction party and the procurement party.

[0125] The construction verification module 13 is specifically used for: The construction party receives the trusted transmission packet and calculates the first transmission accuracy by comparing the received hash value with the original hash value published by the designer. If the first transmission accuracy is greater than or equal to the preset accuracy threshold, the process adaptation compatibility is calculated through the pre-trained process adaptation model; If the first transmission accuracy is less than the preset accuracy threshold, the parameter transmission error information will be fed back to the designer, who will then regenerate and transmit the trusted transmission packet until the first transmission accuracy verification is passed.

[0126] Specifically, the phrase "calculating process adaptation compatibility through a pre-trained process adaptation model" includes: Call the pre-trained process adaptation model; Input the standard component parameter set and the actual construction conditions data of the construction party, wherein the actual construction conditions data includes at least equipment capacity, process reserves, and construction schedule; Output process feasibility parameters, schedule adaptability parameters, and cost controllability parameters; The process feasibility parameters, schedule adaptability parameters, and cost controllability parameters are weighted according to preset weights to obtain the initial process adaptability and compatibility. The ratio of the actual performance of similar projects in the past by the construction party to the benchmark requirements is obtained and used as the first correction factor; The final process adaptation compatibility is obtained by multiplying the initial process adaptation compatibility, the first correction coefficient, and the verified first transfer accuracy.

[0127] Furthermore, the construction process of the "process adaptation model" includes: Collect historical standard component parameter sets corresponding to historical design changes, as well as historical actual construction condition data of the corresponding construction parties, and integrate them to form a sample construction dataset; Obtain historical process feasibility parameters, historical construction period adaptability parameters, and historical cost controllability parameters corresponding to the sample construction data, and use them as the sample construction supervision label set; Based on the random forest model, a branch for process feasibility assessment is constructed. Based on long short-term memory networks, a branch for evaluating project duration adaptability is constructed. Based on the gradient boosting model, a cost controllability evaluation branch is constructed; The three branches are connected to a shared input layer to form a process adaptation model; Using the sample construction dataset and sample construction supervision label set, the process adaptation model is trained and validated in a supervised manner, and training is completed after convergence.

[0128] The procurement verification module 14 is specifically used for: The purchaser receives the trusted transmission packet and calculates the second transmission accuracy by comparing the received hash value with the original hash value published by the designer. If the second transmission accuracy is greater than or equal to the preset accuracy threshold, the material resource matching degree is calculated using the pre-trained material matching model; If the second transmission accuracy is less than the preset accuracy threshold, the parameter transmission error information will be fed back to the designer, who will then regenerate and transmit the trusted transmission packet until the second transmission accuracy verification is passed.

[0129] Specifically, the phrase "calculating the material resource matching degree through a pre-trained material matching model" includes: Call the pre-trained material matching model; Input the standard component parameter set and the actual resource data of the purchaser, wherein the actual resource data includes at least supplier qualifications, supply chain timeliness records, and material cost benchmarks; Output supplier capability matching parameters, supply timeliness matching parameters, and cost adaptability parameters; The supplier capability matching parameter, supply timeliness matching parameter, and cost adaptability parameter are weighted according to preset weights to obtain the initial material resource matching degree. The ratio of the actual supply performance of similar projects in the past to the benchmark requirements is obtained and used as the second correction factor. The final material resource matching degree is obtained by multiplying the initial material resource matching degree, the second correction coefficient, and the verified second transfer accuracy.

[0130] Furthermore, the construction process of the "material matching model" includes: Collect historical standard component parameter sets from historical design changes, as well as historical resource data from the corresponding purchasers, and integrate them to form a sample procurement dataset; Obtain the historical supplier capability matching parameters, historical supply timeliness matching parameters, and historical cost adaptability parameters corresponding to the sample procurement dataset, and use them as the sample procurement supervision label set; Based on the knowledge graph model, construct a supplier capability matching branch; Based on long short-term memory networks, construct supply timeliness matching branches; Based on the gradient boosting model, a cost adaptability evaluation branch is constructed. The three branches are connected to a shared input layer to form a material matching model; Using the sample procurement dataset and sample procurement supervision label set, the material matching model is trained and validated in a supervised manner, and training is completed after convergence.

[0131] Specifically, the output execution module 15 is used for: Determine whether the process adaptation and compatibility meets a preset process threshold, and whether the material resource matching degree meets a preset material threshold; If all conditions are met, issue a design change document; If the process compatibility does not meet the preset process threshold, or the material resource matching does not meet the preset material threshold, the designer shall modify the design change document and repeatedly calculate the process compatibility and material resource matching until both meet the preset process threshold and preset material threshold before issuing the design change document.

[0132] In summary, the embodiments of this application have at least the following technical effects: Compared to existing technologies, this application first uses a data acquisition module to obtain design change documents for construction projects, extract component parameters, and standardize them to generate a standard component parameter set. This eliminates differences in the names, units, and formats of component parameters, ensuring the completeness and consistency of parameter extraction and providing unified basic data support for subsequent reliable parameter transmission and compatibility assessments by construction and procurement parties. Secondly, through a reliable transmission module, a unique hash value is bound to the standard component parameter set, forming a reliable transmission packet containing the standard component parameter set and the hash value. This packet is simultaneously transmitted to the construction and procurement parties, effectively ensuring the integrity and authenticity of the standard component parameter set transmission and enabling synchronous reception of reliable data by both parties. This provides a secure and efficient data distribution foundation for subsequent compatibility assessments. Thirdly, through a construction verification module, the construction party receives the reliable transmission packet, calculates the initial transmission accuracy, and after successful verification, calculates the process compatibility degree using a pre-trained process adaptation model. This ensures the reliability of the standard component parameter data received by the construction party and accurately quantifies the process compatibility between the construction party and the design changes, providing a scientific basis for subsequent construction feasibility decisions. Furthermore, through the procurement verification module, the procuring party receives trusted transmission packets, calculates the second transmission accuracy, and after successful verification, calculates the material resource matching degree using a pre-trained material matching model. This ensures the reliability of the standard component parameter transmission received by the procuring party and accurately quantifies the matching degree between material resources and component requirements, providing a scientific basis for procurement decisions. Finally, it determines whether the process adaptability and compatibility and material resource matching degree meet the preset document issuance conditions. If they do, a design change document is issued; otherwise, the adjustment strategy for the design change document is dynamically determined until the preset document issuance conditions are met. This ensures that the final issued design change document fully adapts to the construction party's process capabilities and the procuring party's material supply capabilities, avoiding blind modifications and improving the success rate of design change implementation. In this way, the reliability and transmission accuracy of data sharing among construction companies are improved, reducing the risks of construction rework, schedule delays, and cost overruns caused by data problems, effectively supporting the smooth progress of construction projects.

[0133] It should be noted that the descriptions of each embodiment in the above embodiments have different focuses. For parts that are not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0134] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0135] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0136] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0137] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0138] Although preferred embodiments of the invention have been described, those skilled in the art, once they have learned the basic inventive concept, can make other changes and modifications to these embodiments.

[0139] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of this invention and its equivalents, this invention also intends to include these modifications and variations.

Claims

1. A data trust-sharing processing method for construction companies, characterized in that, The method includes: Obtain design change documents for construction projects, extract component parameters and standardize them to generate a standard component parameter set; A unique hash value is bound to the standard component parameter set to form a trusted transmission packet containing the standard component parameter set and the hash value, which is then synchronously transmitted to the construction party and the procurement party. The construction party receives the trusted transmission packet, calculates the first transmission accuracy, and after verification, calculates the process adaptation compatibility through the pre-trained process adaptation model. The purchasing party receives the trusted transmission packet, calculates the second transmission accuracy, and after verification, calculates the material resource matching degree through a pre-trained material matching model; Determine whether the process adaptation compatibility and the material resource matching degree meet the preset document issuance conditions. If they do, issue the design change document. If they do not, dynamically determine the adjustment strategy for the design change document until the preset document issuance conditions are met.

2. The data trusted sharing processing method for construction enterprises according to claim 1, characterized in that, Obtain design change documents for the construction project, extract component parameters and standardize them to generate a standard component parameter set, including: Extract component parameters from the design change document, wherein the component parameters include geometric parameters, mechanical parameters, process parameters, and material parameters; The component parameters are standardized in terms of name, unit, and format, and integrated into structured data according to fixed dimensions of geometric parameters, mechanical parameters, process parameters, and material parameters to generate a standard component parameter set.

3. The data trusted sharing processing method for construction enterprises according to claim 1, characterized in that, The construction party receives the trusted transmission packet, calculates the first transmission accuracy, and after successful verification, calculates the process adaptation compatibility using a pre-trained process adaptation model, including: The construction party receives the trusted transmission packet and calculates the first transmission accuracy by comparing the received hash value with the original hash value published by the designer. If the first transmission accuracy is greater than or equal to the preset accuracy threshold, the process adaptation compatibility is calculated through the pre-trained process adaptation model; If the first transmission accuracy is less than the preset accuracy threshold, the parameter transmission error information will be fed back to the designer, who will then regenerate and transmit the trusted transmission packet until the first transmission accuracy verification is passed.

4. The data trust sharing processing method for construction enterprises according to claim 1, characterized in that, The process compatibility is calculated using a pre-trained process adaptation model, including: Call the pre-trained process adaptation model; Input the standard component parameter set and the actual construction conditions data of the construction party, wherein the actual construction conditions data includes at least equipment capacity, process reserves, and construction schedule; Output process feasibility parameters, schedule adaptability parameters, and cost controllability parameters; The process feasibility parameters, schedule adaptability parameters, and cost controllability parameters are weighted according to preset weights to obtain the initial process adaptability and compatibility. The ratio of the actual performance of similar projects in the past by the construction party to the benchmark requirements is obtained and used as the first correction factor; The final process adaptation compatibility is obtained by multiplying the initial process adaptation compatibility, the first correction coefficient, and the verified first transfer accuracy.

5. The data trust sharing processing method for construction enterprises according to claim 1, characterized in that, The process of building a process adaptation model includes: Collect historical standard component parameter sets corresponding to historical design changes, as well as historical actual construction condition data of the corresponding construction parties, and integrate them to form a sample construction dataset; Obtain historical process feasibility parameters, historical construction period adaptability parameters, and historical cost controllability parameters corresponding to the sample construction data, and use them as the sample construction supervision label set; Based on the random forest model, a branch for process feasibility assessment is constructed. Based on long short-term memory networks, a branch for evaluating project duration adaptability is constructed. Based on the gradient boosting model, a cost controllability evaluation branch is constructed; The three branches are connected to a shared input layer to form a process adaptation model; Using the sample construction dataset and sample construction supervision label set, the process adaptation model is trained and validated in a supervised manner, and training is completed after convergence.

6. The data trusted sharing processing method for construction enterprises according to claim 1, characterized in that, Upon receiving the trusted transmission packet, the procuring party calculates the second transmission accuracy. After successful verification, it calculates the material resource matching degree using a pre-trained material matching model, including: The purchaser receives the trusted transmission packet and calculates the second transmission accuracy by comparing the received hash value with the original hash value published by the designer. If the second transmission accuracy is greater than or equal to the preset accuracy threshold, the material resource matching degree is calculated using the pre-trained material matching model; If the second transmission accuracy is less than the preset accuracy threshold, the parameter transmission error information will be fed back to the designer, who will then regenerate and transmit the trusted transmission packet until the second transmission accuracy verification is passed.

7. The data trusted sharing processing method for construction enterprises according to claim 1, characterized in that, The material resource matching degree is calculated using a pre-trained material matching model, including: Call the pre-trained material matching model; Input the standard component parameter set and the actual resource data of the purchaser, wherein the actual resource data includes at least supplier qualifications, supply chain timeliness records, and material cost benchmarks; Output supplier capability matching parameters, supply timeliness matching parameters, and cost adaptability parameters; The supplier capability matching parameter, supply timeliness matching parameter, and cost adaptability parameter are weighted according to preset weights to obtain the initial material resource matching degree. The ratio of the actual supply performance of similar projects in the past to the benchmark requirements is obtained and used as the second correction factor. The final material resource matching degree is obtained by multiplying the initial material resource matching degree, the second correction coefficient, and the verified second transfer accuracy.

8. The data trusted sharing processing method for construction enterprises according to claim 1, characterized in that, The process of constructing a material matching model includes: Collect historical standard component parameter sets from historical design changes, as well as historical resource data from the corresponding purchasers, and integrate them to form a sample procurement dataset; Obtain the historical supplier capability matching parameters, historical supply timeliness matching parameters, and historical cost adaptability parameters corresponding to the sample procurement dataset, and use them as the sample procurement supervision label set; Based on the knowledge graph model, construct a supplier capability matching branch; Based on long short-term memory networks, construct supply timeliness matching branches; Based on the gradient boosting model, a cost adaptability evaluation branch is constructed. The three branches are connected to a shared input layer to form a material matching model; Using the sample procurement dataset and sample procurement supervision label set, the material matching model is trained and validated in a supervised manner, and training is completed after convergence.

9. The data trust sharing processing method for construction enterprises according to claim 1, characterized in that, Determine whether the process compatibility and material resource matching meet the preset document issuance conditions. If they do, issue a design change document. If not, dynamically determine the adjustment strategy for the design change document until the preset document issuance conditions are met, including: Determine whether the process adaptation and compatibility meets a preset process threshold, and whether the material resource matching degree meets a preset material threshold; If all conditions are met, a design change document will be issued. If the process adaptation compatibility does not meet the preset process threshold, or the material resource matching degree does not meet the preset material threshold, calculate the dual-dimensional joint adaptation coefficient and identify the associated constraint relationship; The adjustment strategy for the design change document is determined based on the dual-dimensional joint adaptation coefficient and the associated constraint relationship until the preset document issuance conditions are met.

10. A trusted data sharing and processing system for construction companies, characterized in that, For performing the method according to any one of claims 1-9, comprising: The data acquisition module is used to acquire design change documents for construction projects, extract component parameters, perform standardization processing, and generate a standard component parameter set. The trusted transmission module is used to bind a unique hash value to the standard component parameter set, forming a trusted transmission packet containing the standard component parameter set and the hash value, and synchronously transmitting it to the construction party and the procurement party. The construction verification module is used by the construction party to receive the trusted transmission packet, calculate the first transmission accuracy, and after the verification is passed, calculate the process adaptation compatibility through a pre-trained process adaptation model. The procurement verification module is used by the procuring party to receive the trusted transmission packet, calculate the second transmission accuracy, and calculate the material resource matching degree through a pre-trained material matching model after the verification is passed. The output execution module is used to determine whether the process adaptation compatibility and the material resource matching degree meet the preset document issuance conditions. If they meet, the design change document is issued. If they do not meet, the adjustment strategy of the design change document is dynamically determined until the preset document issuance conditions are met.

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