A construction engineering data management method, system, terminal and storage medium

Abstract

The application relates to a construction engineering data management method and system, a terminal and a storage medium, and relates to the technical field of construction engineering. The method comprises the following steps: generating data coding for each building component according to building component information in a building design stage; establishing a corresponding relationship table according to the corresponding relationship between the data coding and the building component; determining a key node in each key task point of the construction engineering, and receiving evidence data sent by a terminal device at the key node; uploading the evidence data to a database for evidence storage; receiving actual change information in a construction stage; comparing the actual change information with preset model information to determine whether the actual change information is consistent with the model information; if the actual change information is not consistent with the model information, updating the model information according to the actual change information to obtain new model information, and associating the actual change information to the data coding to generate an updated corresponding relationship table. The application has the effect of improving the convenience of construction engineering data management.

Description

Technical Field

[0001] This application relates to the field of building engineering, and in particular to a method, system, terminal and storage medium for building engineering data management. Background Technology

[0002] Digital transformation of the construction industry has become an inevitable trend, but the circulation and management of construction project data remains a core bottleneck restricting the industry's development. Although Building Information Modeling (BIM) technology is relatively mature in the design phase, data gaps and trust deficiencies persist during its transfer to the construction and operation phases, causing various data management platforms to often become mere formalities and unable to achieve real-time mapping.

[0003] Currently, existing methods for managing construction project data have several shortcomings: First, data is fragmented and easily tampered with. Data from the design, construction, and operation stages is stored independently, lacking a unified identification code for linking them together. Furthermore, key node data lacks an immutable evidence storage mechanism, making quality traceability difficult. Second, the network environment at construction sites is often poor. Traditional fixed-frequency data collection methods result in massive amounts of redundant data, consuming bandwidth and making real-time synchronization difficult in weak network environments. Third, data processing lacks intelligence. Standardized review relies on manual labor, and anomaly diagnosis is limited to threshold alarms, failing to identify deep causal logic. Moreover, the cascading effects of engineering changes are difficult to quantify quickly.

[0004] In view of the aforementioned technologies, the inventors believe there is a need to provide a method, system, terminal, and storage medium for building engineering data management. Summary of the Invention

[0005] To improve the convenience of building engineering data management, this application provides a building engineering data management method, system, terminal, and storage medium.

[0006] In a first aspect, this application provides a method, system, terminal, and storage medium for managing construction engineering data, employing the following technical solution: A method for managing construction project data, comprising: Based on the building component information from the architectural design phase, generate data codes for each building component; Establish a correspondence table based on the relationship between data codes and building components; Key nodes are identified within each critical task point of the construction project, and evidence storage data sent by terminal devices at these key nodes is received. Upload the evidence storage data to the database for evidence storage; Receive actual change information during the construction phase; Compare the actual change information with the preset model information to determine if they are consistent; If not, the model information is updated based on the actual change information to obtain new model information, and the actual change information is associated with the data code to generate an updated correspondence table.

[0007] By adopting the above technical solutions, a unique data code is generated for building components and a corresponding relationship table is established throughout the entire project cycle. This enables a strong binding between physical entities and digital information, which is beneficial to the continuity and traceability of data in all stages such as design and construction. It alleviates the problems of fragmented building information and data redundancy in traditional construction, and improves the real-time performance and consistency of the entire process from building design and construction to delivery.

[0008] Optionally, the rate of change of the signal can be monitored based on the evidence storage data sent by the terminal device to obtain the rate of change value; Compare the rate of change value with the set change threshold to determine whether the rate of change value is greater than the set change threshold. If not, reduce the sampling frequency of the terminal device based on the rate of change value; Compare the evidence storage data sent by adjacent terminal devices and calculate the difference in changes in the evidence storage data uploaded by adjacent terminal devices; Compare the change difference with the dynamic fluctuation threshold to determine whether the change difference is greater than the dynamic fluctuation threshold; If not, the high-precision data acquisition mode will be triggered, and all parameters in the evidence data will be uploaded. If so, then according to the differential synchronization mechanism, only the key parameters in the evidence storage data will be uploaded.

[0009] By adopting the above technical solution, an adaptive acquisition mechanism based on signal change rate and dynamic fluctuation threshold can adjust the sampling frequency and upload method of the terminal device. This reduces a significant amount of redundant data, enabling the transmission of critical data with lower resource consumption in construction sites where network bandwidth is limited. This helps alleviate the problem of data transmission delays or interruptions in weak network environments.

[0010] Optionally, interference parameters in the evidence storage data are eliminated according to preset interference elimination rules, and a first parameter set is generated; According to the preset signal correction rules, the parameters in the first parameter set are corrected, and the second parameter set is generated; According to the preset signal amplification rules, the parameters in the second parameter set are amplified, and a third parameter set is generated.

[0011] By adopting the above technical solution, the collected parameters are subjected to multi-level preprocessing, including interference elimination, correction, and amplification, which improves the signal-to-noise ratio and usability of the original data, purifies the data quality from the source, reduces misjudgments caused by sensor noise or environmental interference, and provides a data foundation for subsequent analysis and processing.

[0012] Optionally, the parameters in the third parameter set are converted into the first spectrogram according to the preset semantic transformation rules; Based on the preset building component conflict rules, calculate the logical conflict relationships between the building components in the first spectrum; Based on the analysis of the causes of logical conflicts between various building components, a conflict cause table is generated; Retrieve conflict solutions from the conflict cause table, and modify the first spectrum based on the conflict solutions to generate the second spectrum.

[0013] By adopting the above technical solution, the parameters in the third parameter set are converted into the first spectrogram, i.e., the knowledge graph, and logical conflict calculation is performed. This enables an upgrade from geometric collision checking to compliance logic review, which can automatically identify cross-disciplinary design conflicts, reduce oversights caused by individual checks, and lower the cost and oversight rate of manual drawing review.

[0014] Optionally, a cause-and-effect diagram containing multiple factors can be generated based on environmental variables, construction parameters, and material property data. Obtain abnormal data from the third parameter set; Calculate the range of abnormal data based on the conflict cause table and generate abnormal data indicators; Compare the abnormal data indicators with the driving factors in the cause-and-effect diagram to determine whether there is a real causal relationship; If not, then the abnormal data indicators are screened according to the correlation filtering rules to obtain candidate driving factors; Attribution analysis of abnormal data is performed based on candidate driving factors to obtain root cause determination results; The impact of intervention measures is quantified based on the root cause determination results, and counterfactual prediction signals are generated. The conflict cause table and the second spectrum are updated based on the counterfactual prediction signal.

[0015] By adopting the above technical solution and introducing a causal graph model to perform attribution analysis on abnormal data, it is possible to distinguish between correlated data and true causal driving factors, replacing the traditional threshold alarm. It can quantify the impact of environmental factors, such as temperature and humidity, on construction quality and provide counterfactual prediction. Based on the counterfactual prediction signal, the conflict cause table and the second spectrum are updated, which can provide a scientific basis for subsequent intervention and problem solving.

[0016] Optionally, receive natural language query requests sent by the terminal device; Compare the natural language query request with the semantic nodes in the second spectrogram to determine if they match; If not, the natural language query request is reconstructed to obtain an optimized query instruction; The second spectrum was searched according to the optimized query instructions, and search results including data analysis and root cause inference were obtained. The decision recommendation level is determined based on the search results, and a response feedback signal is generated. The question-and-answer log table and decision support record table are generated and updated based on the response feedback signals.

[0017] By adopting the above technical solution and combining the natural language interaction mechanism of the second spectrogram and the large language model, the threshold for engineering data query is lowered, enabling users to obtain corresponding data analysis and decision suggestions through more conversational commands. This reduces the operational difficulty of professional software, and the generated question-and-answer log table and decision support record table can record historical data, improving response speed and management efficiency.

[0018] Optionally, a dependency graph can be constructed based on the logical dependencies of building components in the new model information; Receive design change requests sent by terminal devices; Compare the design change requests with the building component nodes in the dependency graph to determine whether they will affect subsequent processes. If so, calculate the impact data to obtain the quantitative calculation results; Based on the quantitative calculation results, cost and schedule data are evaluated to obtain the impact assessment value; The risk level of the change is determined based on the impact assessment value, and a document signal is generated; Generate and update the impact record table and change tracking table based on document signals.

[0019] By adopting the above technical solution and constructing a dependency graph, design change requests during the construction process can be quantified and their impact assessed. After determining the risk level of the change, an impact record table and a change tracking table are generated and updated. This allows for the calculation of the impact of design changes on costs, schedules, and downstream processes, reducing deviations caused by manual estimations and lowering the risk of claims and disputes caused by omissions in changes.

[0020] Secondly, this application provides a construction engineering data management system, which adopts the following technical solution: A construction project data management system, comprising: The acquisition module is used to acquire building component information during the architectural design phase, evidence data sent by terminal devices at key nodes, and actual change information during the construction phase. The calculation module generates data codes for each building component and compares actual change information with preset model information; The output module is used to create a correspondence table, upload the evidence storage data to the database, the new model information, and the updated correspondence table.

[0021] By adopting the above technical solution, the acquisition module is used to acquire building component information during the architectural design phase. The terminal device sends evidence-based data and actual change information during the construction phase at key nodes. The calculation module generates data codes for each building component and compares the actual change information with the preset model information, thus realizing the systematic operation of the building engineering data management method. The processor calls the data processing program stored in the calculation module and executes the corresponding algorithm logic, ensuring accurate connection between each processing link while improving the automation level of the building engineering data management process. The output module is used to establish and update corresponding relationship tables, upload evidence-based data to the database for evidence storage, generate new model information, and update the daily data change statistical record table and anomaly record table for easy recording, retrieval, and viewing.

[0022] Thirdly, this application provides a smart terminal, which adopts the following technical solution: A smart terminal includes a memory and a processor, wherein the memory stores a computer program that can be loaded by the processor and executed as described above.

[0023] Fourthly, this application provides a computer storage medium capable of storing corresponding programs, which facilitates more convenient management of construction project data, and adopts the following technical solution: A computer-readable storage medium storing a computer program that can be loaded by a processor and executed by any of the above-described construction engineering data management methods.

[0024] In summary, this application includes at least one of the following beneficial technical effects: A construction engineering data management method generates data codes for building components, establishes a correspondence table between data codes and building components, receives and uploads evidence data sent by terminal devices at key nodes, receives actual change information during the construction phase, compares the actual change information with the preset model information, updates the model information according to the actual change information if there is a discrepancy, and associates the actual change information with the data codes to generate an updated correspondence table. This method is beneficial to the continuity and traceability of data at all stages of design and construction, effectively alleviates the problems of fragmented building information and data redundancy in traditional construction, and improves the real-time performance and consistency of the entire process from building design and construction to delivery.

[0025] The signal change rate is monitored based on the evidence data sent by the terminal device, and the change rate is compared with the set change threshold. The change difference of the evidence data sent by adjacent terminal devices is compared with the change difference and the dynamic fluctuation threshold. Through an adaptive acquisition mechanism based on the signal change rate and dynamic fluctuation threshold, the sampling frequency and upload method of the terminal device can be dynamically adjusted, reducing the generation and transmission of redundant data. In construction sites with limited network bandwidth, the backhaul of key data can be achieved with low resource consumption, alleviating the problem of data transmission delay or interruption in weak network environments.

[0026] The system receives natural language query requests from terminal devices and compares these requests with semantic nodes in the second spectrogram. It then searches the second spectrogram based on optimized query instructions, obtaining search results that include data analysis and root cause inference. Based on the search results, it determines the level of decision recommendations and generates a response feedback signal. By combining the second spectrogram with the natural language interaction mechanism, the system lowers the barrier to entry for engineering data queries. Users can obtain data analysis and decision recommendations simply through more conversational instructions, reducing the operational difficulty of specialized software. Simultaneously, by generating question-and-answer log tables and decision support record tables, it achieves the accumulation and recording of historical data, improving the overall system response rate and management efficiency. Attached Figure Description

[0027] Figure 1 This is a flowchart illustrating a construction engineering data management method provided in an embodiment of this application.

[0028] Figure 2 This is a flowchart illustrating an adaptive acquisition and data transmission method provided in an embodiment of this application.

[0029] Figure 3 This is a flowchart illustrating a data preprocessing method provided in an embodiment of this application.

[0030] Figure 4 This is a flowchart illustrating a spectral conversion and logic conflict detection method provided in an embodiment of this application.

[0031] Figure 5 This is a flowchart illustrating an anomaly attribution and counterfactual prediction method provided in an embodiment of this application.

[0032] Figure 6 This is a flowchart illustrating a natural language query and decision support method provided in an embodiment of this application.

[0033] Figure 7 This is a flowchart illustrating a design change impact assessment method provided in an embodiment of this application.

[0034] Figure 8This is a schematic diagram of the structure of a building engineering data management system provided in an embodiment of this application. Detailed Implementation

[0035] To make the purpose, technical solution, and advantages of this application clearer, the following description is provided in conjunction with the appendix. Figure 1-8 The present application will be further described in detail below with reference to embodiments. It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the scope of the application.

[0036] This application discloses a method for managing construction project data. (Refer to...) Figure 1 The construction project data management method includes: Step S101: Generate data codes for each building component based on the building component information from the architectural design phase.

[0037] Data coding refers to assigning a unique identifier to each individual building component in a building model, such as a beam, a wall, or an air conditioning unit. It typically employs a hierarchical coding system, such as project number – building – floor – building component type – serial number. This code is linked to information such as the building component's spatial location, material properties, and construction sequence.

[0038] For example, in a commercial complex project, the concrete frame column at position 5 on the 3rd floor is assigned the code XXX-03F-3-5-C01, where XXX is the project abbreviation, 03F is the floor number, and C01 is the column building component serial number.

[0039] Step S102: Establish a correspondence table based on the correspondence between data codes and building components.

[0040] A mapping table is a structured data table that records the mapping relationship between data codes and various attributes of building components. This mapping table primarily uses data codes to facilitate rapid retrieval and association of building component information throughout the entire project lifecycle.

[0041] For example, the code XXX-03F-3-5-C01 generated in step S101 is stored in a corresponding relationship table along with fields such as the concrete grade, steel reinforcement ratio, three-dimensional coordinates, and design drawing links of the building component, forming a complete record.

[0042] Step S103: Identify key nodes within each critical task point of the construction project, and receive the evidence storage data sent by the terminal device at the key nodes.

[0043] Critical milestones refer to significant moments with legal or managerial importance throughout the design, construction, and operation and maintenance phases. Examples include design handover, acceptance of concealed works, completion of concrete pouring, and equipment installation and commissioning. Evidential data includes on-site photos, videos, sensor readings, and signed records, collected and transmitted by terminal equipment.

[0044] For example, after the basement waterproofing layer is completed, the supervisor uses a handheld terminal device to take photos of the waterproofing layer being laid and enters the acceptance results. The system then confirms this node as the acceptance node for the concealed waterproofing works and receives the aforementioned evidence data.

[0045] Step S104: Upload the evidence storage data to the database.

[0046] A database refers to uploading evidence data to a central database or distributed storage system in an immutable form, while recording data such as upload timestamps, operator identities, and geographical locations, which is beneficial to the authenticity and traceability of the evidence data.

[0047] For example, the waterproof layer acceptance photos and signature records received in step S103 are compressed and encrypted and then uploaded to the cloud database, generating a unique value. After the evidence is stored, the system returns the evidence certificate number for subsequent retrieval.

[0048] Step S105: Receive actual change information during the construction phase.

[0049] Actual change information refers to modifications made at the construction site that deviate from the original model or drawings due to design adjustments, material replacements, construction errors, owner requirements, or other reasons. Change information can be submitted through BIM collaboration platforms, mobile devices, or automatically triggered by IoT sensors.

[0050] For example, it was found on site that the diameter of the steel bars in the 3rd floor, 5th axis frame column had been changed from 25mm to 28mm due to supply reasons. The construction worker filled in the change application in the system and uploaded the material certificate of conformity and change instruction sheet. The system received the change information.

[0051] Step S106: Compare the actual change information with the preset model information to determine whether they are consistent.

[0052] The preset model information refers to standard parameters extracted from the BIM model established during the design phase, such as the dimensions, location, material, and performance indicators of building components. The system locates the model information of the corresponding building component through data encoding and compares the parameters in the change information with the preset values ​​item by item.

[0053] For example, the system retrieves the preset rebar diameter of code XXX-03F-3-5-C01 as 25mm, while the actual diameter entered in the change information is 28mm. The comparison result is inconsistent, triggering the subsequent update process; if the change information is completely consistent with the preset value, it is determined to be consistent and no processing is required.

[0054] Step S107: If not, update the model information according to the actual change information to obtain new model information, and associate the actual change information with the data code to generate an updated correspondence table.

[0055] Model information updates refer to replacing or supplementing the corresponding parameters in the original BIM model with actual change information to form a real-time model that reflects the actual site conditions. Simultaneously, information such as the reason for the change, the time of the change, and the personnel involved are linked to the data code, refreshing the associated records in the corresponding relationship tables.

[0056] For example, change the diameter field of the rebar code XXX-03F-3-5-C01 from 25mm to 28mm, and add a new record to the change record sub-table of the corresponding relationship table: Change date 2025-03-15, Reason: Change in supply specifications, Responsible person: Zhang San. Save the updated relationship table as a new version for use in subsequent construction and operation and maintenance phases.

[0057] By adopting the above technical solutions, a unique data code is generated for building components and a corresponding relationship table is established throughout the entire project cycle. This enables a strong binding between physical entities and digital information, which is beneficial to the continuity and traceability of data in all stages such as design and construction. It alleviates the problems of fragmented building information and data redundancy in traditional construction, and improves the real-time performance and consistency of the entire process from building design and construction to delivery.

[0058] This application discloses a method for managing construction project data. (Refer to...) Figure 2 The method includes: Step S201: Monitor the rate of change of the signal based on the evidence storage data sent by the terminal device, and obtain the rate of change value.

[0059] The rate of change of a signal refers to the amplitude of change in stored data per unit time, such as parameters like stress, displacement, temperature, and vibration frequency collected by sensors. It is used to characterize the degree of fluctuation in the state of various building components or the environment. The rate of change value can be calculated by dividing the difference between the current data and the data at the previous moment by the time interval.

[0060] For example, a foundation pit displacement monitoring terminal uploads horizontal displacement data every 5 seconds. Two consecutive data acquisitions show values ​​of 12.3 mm and 12.5 mm, respectively. The rate of change is (12.5 - 12.3) / 5 = 0.04 mm / s. The system continuously monitors this rate of change using corresponding sensors.

[0061] Step S202: Compare the rate of change value with the set change threshold to determine whether the rate of change value is greater than the set change threshold.

[0062] Setting a change threshold refers to a preset limit value based on engineering specifications or experience, used to distinguish whether a signal is in a stable state or a rapidly changing state. If the rate of change is greater than the set change threshold, it indicates that the building components or environment are undergoing drastic and significant changes, and the sampling frequency needs to be maintained or increased; if the rate of change is less than or equal to the set change threshold, it indicates that the state of the building components is tending to be stable.

[0063] For example, for the aforementioned foundation pit displacement monitoring, a change threshold of 0.10 mm / s is set. The current rate of change, 0.04 mm / s, is not greater than 0.10 mm / s and is therefore determined to be in a stable state.

[0064] Step S203: If not, reduce the sampling frequency of the terminal device according to the rate of change value.

[0065] Reducing the sampling frequency refers to lowering the sampling rate of the terminal device, thereby reducing the amount of data generated and the signal transmission load. The magnitude of the reduction is inversely proportional to the rate of change; the smoother the change, the greater the frequency reduction.

[0066] For example, since the current rate of change is only 0.04 mm / s, which is much smaller than the set threshold of 0.10 mm / s, the system adjusts the device terminal's acquisition frequency from 5 seconds / time to 20 seconds / time, and at the same time extends the upload interval to save device power and network bandwidth.

[0067] Step S204: Compare the evidence storage data sent by adjacent terminal devices and calculate the difference in changes of the evidence storage data uploaded by adjacent terminal devices.

[0068] Adjacent terminal devices refer to two or more terminal devices that are spatially adjacent. Spatially adjacent means that at least two sensors are located in the same construction section or the same structural unit and are of the same type.

[0069] The variation difference refers to the numerical difference in data collected by these terminal devices within the same time period, and is used to determine the consistency of data within a local area.

[0070] For example, in a concrete curing area, three temperature sensors are arranged adjacent to each other, measuring temperatures of 22.1℃, 22.3℃, and 22.0℃ respectively. The system calculates that the maximum difference in temperature between adjacent sensors is 0.3℃.

[0071] Step S205: Compare the change difference with the dynamic fluctuation threshold to determine whether the change difference is greater than the dynamic fluctuation threshold.

[0072] Dynamic fluctuation thresholds refer to comparison limits that are adjusted in real time based on current environmental conditions, construction stage, or historical data distribution. Unlike fixed thresholds, they can adapt to normal fluctuation ranges under different operating conditions. If the difference in the judgment value is greater than the dynamic fluctuation threshold, it indicates an abnormal difference between adjacent terminal devices, which may be caused by local faults or sudden events. If the difference in the judgment value is less than or equal to the dynamic fluctuation threshold, it indicates a high degree of data consistency.

[0073] For example, according to the specifications for this concrete curing stage, the dynamic fluctuation threshold is set to 0.5℃. A maximum variation difference of 0.3℃ between adjacent sensors is considered consistent with the data if it does not exceed 0.5℃.

[0074] Step S206: If not, trigger the high-precision data acquisition mode and upload all parameters in the evidence storage data.

[0075] High-precision data acquisition mode refers to terminal devices acquiring and uploading data at a high or maximum sampling rate and with a complete parameter set. This ensures that the most detailed data records can be obtained under low-fluctuation and stable conditions for subsequent accurate analysis and model calibration. All parameters include raw values, timestamps, geographical locations, and auxiliary environmental variables.

[0076] For example, since the difference in temperature between adjacent temperature sensors is 0.3℃, which is within the normal range, the system triggers high-precision mode. All three sensors upload all parameters, including temperature, humidity, acquisition time, and device power, once per second to construct a complete maintenance temperature control curve.

[0077] Step S207: If so, then according to the differential synchronization mechanism, only the key parameters in the evidence storage data are uploaded.

[0078] The difference synchronization mechanism refers to the practice of prioritizing the uploading of only the key parameters that best reflect the nature of the anomaly, such as the anomaly value, the time of occurrence, and the device identifier, when significant differences occur in data from adjacent devices, in order to avoid network congestion and data redundancy. This method ensures that anomaly information reaches the management end quickly, facilitating timely processing.

[0079] For example, suppose one adjacent temperature sensor measures 35.2℃, while the other two remain around 22.0℃, a difference of 13.2℃, far exceeding the dynamic fluctuation threshold of 0.5℃. The system triggers a difference synchronization mechanism, uploading only three key parameters of the abnormal sensor: its temperature value of 35.2℃, its location number, and the timestamp of the abnormality, along with a warning flag. Detailed data from the other normal devices are temporarily stored locally.

[0080] By adopting the above technical solution, an adaptive acquisition mechanism based on signal change rate and dynamic fluctuation threshold can adjust the sampling frequency and upload method of the terminal device. This reduces a significant amount of redundant data, enabling the transmission of critical data with lower resource consumption in construction sites where network bandwidth is limited. This helps alleviate the problem of data transmission delays or interruptions in weak network environments.

[0081] This application discloses a method for managing construction project data. (Refer to...) Figure 3 The method includes: Step S301: According to the preset interference elimination rules, eliminate the interference parameters in the evidence storage data and generate the first parameter set.

[0082] Interference parameters refer to abnormal data points caused by non-engineering factors such as environmental noise, electromagnetic interference, equipment vibration, or transmission errors, which can obscure the true state information of building components. Pre-defined interference elimination rules include, but are not limited to: median filtering to remove impulse noise, moving average filtering to smooth random fluctuations, and outlier detection based on statistical distributions. The remaining reliable data after elimination constitutes the first parameter set.

[0083] For example, during welding construction, a rebar stress sensor was subjected to strong electromagnetic interference, resulting in two abnormal jumps in two out of ten continuously collected stress values: 135 MPa and 142 MPa, while the normal values ​​before and after were stable at around 85 MPa. The system identified these two jump points as interference parameters and removed them. Then, it filled in the gaps with the average of the adjacent normal values ​​to generate the first parameter set after interference removal.

[0084] Step S302: Correct the parameters in the first parameter set according to the preset signal correction rules, and generate the second parameter set.

[0085] Signal correction rules refer to the use of mathematical models to calibrate and compensate for systematic biases in sensors, such as zero-point drift, temperature drift, nonlinear errors, or installation position offsets. Common correction methods include linear calibration and temperature compensation coefficient methods. The corrected data is closer to the true value, generating a second parameter set.

[0086] For example, a displacement gauge experiences zero-point drift due to long-term use, with the initial zero point shifting from 0 mm to -1.2 mm. The system, according to a preset correction rule, adds 1.2 mm to all displacement readings in the first parameter set. Simultaneously, the sensor exhibits a temperature drift of 0.02 mm / ℃ during high-temperature periods. The system further performs linear compensation based on real-time temperature data to obtain a corrected second parameter set.

[0087] Step S303: Amplify the parameters in the second parameter set according to the preset signal amplification rules, and generate the third parameter set.

[0088] Signal amplification refers to the practice of proportionally amplifying weak signals to a more suitable range for analysis and processing when the change in the target physical quantity is small, using gain coefficients or dynamic range expansion methods, while maintaining a stable signal-to-noise ratio. This amplification does not alter the original physical value, but rather makes it easier to identify minute changes during subsequent feature extraction or thresholding. Common methods include linear gain amplification, logarithmic transformation, and adaptive gain control.

[0089] By adopting the above technical solution, the collected parameters are subjected to multi-level preprocessing, including interference elimination, correction, and amplification, which improves the signal-to-noise ratio and usability of the original data, purifies the data quality from the source, reduces misjudgments caused by sensor noise or environmental interference, and provides a data foundation for subsequent analysis and processing.

[0090] This application discloses a method for managing construction project data. (Refer to...) Figure 4 The method includes: Step S401: Convert the parameters in the third parameter set into the first spectrum according to the preset semantic conversion rules.

[0091] The first spectrogram refers to a semantic network constructed in the form of a knowledge graph. Nodes represent building components, such as beams, slabs, columns, pipes, equipment, material properties, construction procedures, or environmental parameters. Edges represent semantic relationships between these entities, such as supports, passages, and dependencies. Predefined semantic transformation rules define how to map numerical parameters, type labels, and time-series data from the third parameter set to entities and their attributes in the graph, and how to extract implicit semantic associations.

[0092] For example, the third parameter set of a project includes the following information: frame column C35, concrete strength C40, main beam L12, clear height 3.0m, air duct DN400, and elevation 2.8m. Based on semantic conversion rules, the system converts frame column C35 into node column C35, main beam L12 into node beam L12, and air duct DN400 into node air duct DN400. It also automatically infers the relationship between beam L12 supporting column C35 and air duct DN400 passing beneath beam L12, thereby generating the first spectrogram.

[0093] Step S402: Calculate the logical conflict relationship between each building component in the first spectrum according to the preset building component conflict rules.

[0094] Building component conflict rules refer to a predefined set of logical constraints used to detect whether different professional building components violate specifications or design intent in the same space or logically. Conflict types include: geometric collisions, such as two entities occupying the same space; insufficient clearance, such as the distance between pipelines and the finished building surface not meeting requirements; damage to fire compartments, such as pipelines crossing firewalls without fireproof sealing; and lack of maintenance space, such as insufficient operating distance around equipment. The system calculates and identifies all pairs of building components that violate the constraints and their conflict types.

[0095] For example, the system applies a conflict rule to the first spectrum: the distance between the bottom elevation of the duct and the bottom elevation of the beam should be ≥100mm. By querying the elevation of the DN400 duct at node 2.8m and the bottom elevation of the L12 beam at node 2.85m, the system calculates that the height difference between the two is only 50mm, which is less than 100mm, thus detecting a conflict between the net height of the duct and the beam. Simultaneously, the system detects a lack of fire damper associated edges at the duct crossing the firewall node, marking it as a fire damper deficiency conflict.

[0096] Step S403: Based on the analysis of the causes of logical conflicts between various building components, generate a conflict cause table.

[0097] The conflict cause table refers to the system's analysis of the root cause of each pair of logical conflicts detected in step S402, such as incorrect design parameter input, omissions in interdisciplinary collaboration, and mismatched specification versions. It records the conflict identifier, involved building components, conflict type, cause classification, and severity level in a structured table format. Root cause analysis can be achieved through rule-based reasoning, historical case matching, or constraint violation path tracing.

[0098] For example, regarding the conflict between the net height of the duct and the beam, the system analyzes the cause: the structural beam height is designed as 600mm based on 1 / 10 of the span, while the mechanical and electrical duct height is determined to be 400mm based on the standard thickness plus insulation layer. However, when the two disciplines were modeled independently, no pipeline collision check was performed, resulting in insufficient net height allocation. The conflict cause table generates a record: Conflict ID C001, building component pair (duct DN400, beam L12), conflict type insufficient net height, cause classification lack of cross-disciplinary collaboration, severity level high.

[0099] Step S404: Retrieve conflict solutions from the conflict cause table, and modify the first spectrum according to the conflict solutions to generate the second spectrum.

[0100] Conflict solutions refer to corrective measures for specific conflict types, matched from the built-in knowledge base, code provisions, engineering experience, or historical projects. The system retrieves corresponding solutions based on the conflict type and cause classification in the conflict cause table, such as adjusting pipeline routes, modifying beam cross-sectional dimensions, or adding local slab reductions. Then, it automatically or semi-automatically modifies the node attributes or relationships in the first spectrogram to create a second spectrogram that satisfies all constraints. The modification process simultaneously records change logs for easy traceability by designers.

[0101] For example, the system retrieves solutions to conflict C001: Solution 1 raises the duct elevation by 50mm; Solution 2 adds a partial opening to the beam; Solution 3 reduces the duct cross-section and adds branch pipes. Considering site conditions, such as the presence of sprinkler pipes above, the system automatically evaluates and selects Solution 1, modifying the elevation of the DN400 duct node from 2.8m to 2.85m. After modification, the recalculated net height difference is 100mm, conforming to the specifications. Simultaneously, for the fire protection deficiency conflict, the system retrieves a solution to add a fire damper and adds the fire damper node F001 and its relationship edges with the duct and firewall in the diagram. After completing all modifications, a second diagram is generated.

[0102] By adopting the above technical solution, the parameters in the third parameter set are converted into the first spectrogram, i.e., the knowledge graph, and logical conflict calculation is performed. This enables an upgrade from geometric collision checking to compliance logic review, which can automatically identify cross-disciplinary design conflicts, reduce oversights caused by individual checks, and lower the cost and oversight rate of manual drawing review.

[0103] This application discloses a method for managing construction project data. (Refer to...) Figure 5 The method includes: Step S501: Generate a cause-and-effect diagram containing multiple factors based on environmental variables, construction parameters, and material property data.

[0104] Cause-effect graphs are used to express causal dependencies between different variables. Environmental variables include external conditions such as temperature, humidity, and wind speed; construction parameters include process indicators such as pouring speed, vibration time, and curing methods; and material property data include concrete mix proportions, steel reinforcement types, and admixture dosages. The system uses a domain knowledge rule discovery algorithm to construct causal relationships from cause to effect, forming a cause-effect graph.

[0105] For example, to address the problem of concrete floor slab cracking, the system inputs environmental variables (such as temperature, relative humidity, etc.), construction parameters (such as the time for covering with plastic film after pouring, the time for the first water curing, etc.) and material properties (such as water-cement ratio, fly ash content, etc.), and combines them with preset causal logic (such as high temperature and low humidity accelerating water evaporation, causing increased plastic shrinkage, thus leading to an increased risk of cracking, etc.) to generate a causal graph: the nodes include temperature, humidity, curing delay, water-cement ratio, plastic shrinkage rate, and cracking.

[0106] Step S502: Obtain abnormal data from the third parameter set.

[0107] Outlier data refers to values ​​in the third parameter set that deviate from the expected range or statistical distribution, typically manifesting as abrupt changes, persistent exceedances, or abnormal trends. These outliers may indicate engineering quality problems or equipment malfunctions.

[0108] Step S503: Calculate the range of abnormal data based on the conflict cause table and generate abnormal data indicators.

[0109] The conflict cause table records the various logical conflicts previously detected, along with their corresponding sensitive parameters and threshold ranges.

[0110] For example, the system matches the relevant conflict type from the conflict cause table based on the building component or region to which the current abnormal data belongs, and then calculates the upper and lower limits or change range that should be paid attention to for the abnormal data, forming abnormal data indicators such as deviation, over-limit duration, and change rate.

[0111] Step S504: Compare the abnormal data indicators with the driving factors in the causal graph to determine whether there is a real causal relationship.

[0112] A true causal relationship means that the changes in abnormal data indicators are indeed caused by one or more driving factors in the causal diagram, rather than a random statistical correlation. Driving factors may include temperature, humidity, and maintenance methods. The system evaluates this through causal inference methods: if, under given driving factors, the conditional probability distribution of the abnormal data indicators differs significantly from the baseline distribution and satisfies the directional condition, then a true causal relationship is determined to exist.

[0113] For example, the system compares abnormal data indicators with driving factors in the cause-and-effect graph: temperature, humidity, and curing delay. Through analysis of the cause-and-effect graph, after excluding other confounding factors such as water-cement ratio, the calculated actual strain change rate falls within the high confidence interval of the distribution, indicating a genuine causal relationship: high temperature, low humidity, and delayed curing collectively caused the abnormal strain increase.

[0114] Step S505: If not, then the abnormal data indicators are screened according to the correlation filtering rules to obtain candidate driving factors.

[0115] When step S504 determines that the abnormal data indicators have no real causal relationship with the existing driving factors in the cause-effect diagram, it indicates that the possible causes were not included in the cause-effect diagram or are only spurious correlations. The system uses correlation filtering rules to screen out factors with strong statistical correlations with the abnormal data indicators from all environmental variables, construction parameters, and material properties as candidate driving factors for further analysis.

[0116] For example, assuming the causal relationship between the abnormal strain rate of change and temperature and humidity is not significant, the system calculates the correlation coefficients between the abnormal data indicators and all input variables, finding a high correlation with concrete temperature and wind speed. These two variables are added to the list of candidate driving factors, awaiting further attribution verification.

[0117] Step S506: Perform attribution analysis on the abnormal data based on the candidate driving factors to obtain the root cause determination results.

[0118] Attribution analysis refers to the use of causal inference algorithms to identify the true root cause from candidate driving factors and quantify the contribution of each factor to outlier data. Root cause determination results include the root cause factor, contribution weight, confidence level, and action path.

[0119] For example, for the candidate driving factors of concrete temperature and wind speed, the system constructs a structural causal reasoning model algorithm: higher concrete temperature → accelerated heat of hydration → increased plastic shrinkage; increased wind speed → faster surface moisture evaporation rate → increased plastic shrinkage. Through reasoning: after reducing the concrete temperature to normal, the strain change rate A is calculated. After setting the wind speed to normal, the strain change rate B is calculated. Comparing the two, A is greater than B, indicating that concrete temperature contributes more significantly; therefore, the root cause determination is that the concrete temperature is too high.

[0120] Step S507: Quantify the impact of the intervention measures based on the root cause determination results and generate counterfactual prediction signals.

[0121] Counterfactual predictive signals refer to simulations based on the current causal model, demonstrating how anomalous data indicators will change after implementing specific interventions (such as reducing concrete temperature or increasing moisture retention through covering), and outputting predictive results. The system quantifies the expected effects of each intervention through intervention calculation simulation, providing a quantitative basis for decision-making.

[0122] For example, the system proposes intervention measures based on the root cause—excessively high concrete temperature: adding ice chips during mixing or extending the shading period of the mixer truck for cooling. The expected rate of change of strain after the intervention is calculated using a cause-effect graph: lowering the concrete temperature predicts a decrease in plastic shrinkage, corresponding to a decrease in the rate of change of strain. A counterfactual prediction signal is generated: Intervention: Temperature reduction of XX℃; Effect: Strain rate of change decreases by XX; Cracking probability decreases by XX%; Recommendation priority: High.

[0123] Step S508: Update the conflict cause table and the second spectrum based on the counterfactual prediction signal.

[0124] Updating the conflict cause table involves adding newly discovered root causes, corresponding interventions, and quantified effects to the original table, forming a continuously accumulating knowledge base. Updating the second spectrum involves adding newly discovered causal relationships, such as high concrete temperature → early shrinkage → cracking, and intervention feedback, in graphical form to the second spectrum, enriching the solutions.

[0125] By adopting the above technical solution and introducing a causal graph model to perform attribution analysis on abnormal data, it is possible to distinguish between correlated data and true causal driving factors, replacing the traditional threshold alarm. It can quantify the impact of environmental factors, such as temperature and humidity, on construction quality and provide counterfactual prediction. Based on the counterfactual prediction signal, the conflict cause table and the second spectrum are updated, which can provide a scientific basis for subsequent intervention and problem solving.

[0126] This application discloses a method for managing construction project data. (Refer to...) Figure 6 The method includes: Step S601: Receive a natural language query request sent by the terminal device.

[0127] Natural language query requests refer to engineering data queries made by users through mobile terminals, computers, or voice input devices in the form of everyday spoken or written language. For example, "Has the concrete curing temperature of Building No. 3 met the standard?" or "Which area experienced the greatest change in steel reinforcement stress last week?" The system receives the request, parses the text, and identifies the building components, location, time, attributes, and intent within it.

[0128] For example, the site foreman uses his mobile phone to voice input: "Can you check if the settlement of the basement roof slab has exceeded the warning value in the last three days?" The system transcribes the voice into text and receives it as a natural language query request.

[0129] Step S602: Compare the natural language query request with the semantic nodes in the second spectrogram to determine whether they match.

[0130] Semantic nodes refer to points in the second spectrogram that represent entity concepts such as building components, material properties, construction parameters, spatial locations, and temporal information. The system performs similarity matching between keywords, phrases, and semantic roles in the natural language query request and node labels, attributes, and relational edges in the second spectrogram. If all key semantic elements in the query request can be found in corresponding nodes in the spectrogram, and the query logic is clear and unambiguous, it is considered a match; otherwise, it is considered a mismatch.

[0131] For example, the query request keywords include the three-day warning value for basement roof slab settlement. The system retrieves the following nodes from the second spectrum: basement roof slab, attribute node: settlement, time node: three days, and threshold node: warning value. All nodes exist and have clear relationship paths. For example, the basement roof slab has a settlement value. Comparing the settlement value and the warning value, it is determined to be a match. If the user inquires about which building component has excessive carbonization depth, and the second spectrum does not contain a node related to carbonization depth, it is determined to be a mismatch.

[0132] Step S603: If not, the natural language query request is reconstructed to obtain an optimized query instruction.

[0133] Refactoring refers to the process by which the system, using a large language model or a pre-defined terminology mapping table, transforms the user's spoken expression into standardized nodes and relationships that actually exist in the second spectrogram when the original query request contains semantic ambiguity, missing technical terms, or inconsistent node names. The optimized query command is a structured query language that can be directly used for spectrogram retrieval. The refactoring process preserves the user's original intent, adjusting only the terminology and granularity of the expression.

[0134] For example, if a user inputs that a building component is slightly crooked, the request does not match any standard semantic nodes. The system uses a large language model for semantic understanding, inferring that the user's intent is to query whether the vertical deviation of a building component exceeds the standard, and maps "slightly crooked" to the standard attribute "vertical deviation," and maps "which building component" to a building component type enumeration.

[0135] Step S604: Search the second spectrum according to the optimized query command to obtain search results that include data analysis and root cause inference.

[0136] The retrieval process refers to the system inputting optimized query commands into the second spectrogram, and obtaining nodes, edges, and their associated data that meet the criteria through graph traversal, path search, attribute filtering, and aggregation calculation. Simultaneously, based on the conflict cause table, causal relationships, and historical records stored in the second spectrogram, the system performs secondary analysis on the retrieved raw data, such as statistical trends, calculating the proportion of data exceeding limits, and inferring root causes, such as tracing back the driving factors that triggered the anomalies. Finally, it generates a comprehensive retrieval result containing both data results and analytical conclusions.

[0137] For example, based on the optimization instructions, the second spectrum is retrieved, locating all building component nodes whose verticality deviation attributes exceed the standard threshold. A total of three columns (Z-12, Z-15, and Z-22) are identified, with deviation values ​​of 18mm, 22mm, and 16mm respectively, and a threshold of 10mm. The system further analyzes the root cause: through the information stored in the second spectrum regarding the influence of concrete pouring speed on formwork lateral pressure and thus verticality deviation, it is found that the pouring speed of these three columns was 3.2m / h, exceeding the upper limit of 2.5m / h. The root cause is inferred to be excessively fast pouring speed, and the retrieval results containing information on each cause and effect are output.

[0138] Step S605: Determine the decision suggestion level based on the search results and generate a response feedback signal.

[0139] The decision recommendation level refers to categorizing recommendations into different priorities based on the severity, urgency, and impact on project safety or schedule of the search results. The system combines a pre-set risk matrix or a rule-based inference engine to quantify and score abnormal data and root cause inferences in the search results, thereby determining the level. The response feedback signal encapsulates the search results and decision recommendation levels into readable information for users, which may include text, charts, warning colors, recommended actions, etc.

[0140] For example, the verticality deviations of the three columns mentioned above are 18mm, 22mm, and 16mm, respectively. The 22mm deviation exceeds the allowable value of 10mm by 120%, and is located in the core tube area. The system determines the decision recommendation level to be high and generates a response feedback signal {Level: High, Message: Three columns have been found to have severely excessive verticality. It is recommended to immediately stop subsequent construction in this area and correct or demolish and rebuild Z-15. Recommended actions: [Recheck measurements, adjust formwork supports, control pouring rate ≤ 2.5m / h]}.

[0141] Step S606: Generate and update the question-and-answer log table and decision support record table based on the response feedback signal.

[0142] The question-and-answer log table records the original request, reconstructed instructions, timestamp, user identity, retrieval time, and response summary for each natural language query, facilitating subsequent auditing, model optimization, and user behavior analysis. The decision support record table specifically records decision suggestions, adoption status, actual processing results, and effect feedback corresponding to high or urgency-level response feedback signals, forming a closed-loop management historical knowledge base.

[0143] For example, the system stores the query indicating which building component is slightly crooked, along with the corresponding optimization instructions, search results, and decision suggestions, in the question-and-answer log table, creating a new record with log ID QXXXXXXX-001. Simultaneously, for the high-level suggestion to stop construction and correct the deviation, an initial record is generated in the decision support record table: Decision ID DXXXXXXX-001, Related Query ID QXXXXXXX-001, Suggestion Content: Correction Z-15, Adoption Status: Pending Confirmation. After the foreman confirms and executes the correction, the record shows the result as corrected, with a remeasured deviation of 4mm, and the record's status is updated to "Adopted, Effective."

[0144] By adopting the above technical solution and combining the natural language interaction mechanism of the second spectrogram and the large language model, the threshold for engineering data query is lowered, enabling users to obtain corresponding data analysis and decision suggestions through more conversational commands. This reduces the operational difficulty of professional software, and the generated question-and-answer log table and decision support record table can record historical data, improving response speed and management efficiency.

[0145] This application discloses a method for managing construction project data. (Refer to...) Figure 7 The method includes: Step S701: Construct a dependency graph based on the logical dependencies of building components in the new model information.

[0146] Logical dependency refers to the mutual constraints between building components in terms of construction sequence, spatial location, force transmission, and resource sharing.

[0147] Common dependency types include: construction sequence dependency (e.g., masonry walls can only begin after concrete columns have been poured and cured), spatial dependency (e.g., ductwork in the ceiling must be in place before the ceiling joists are installed), and functional dependency (e.g., distribution boxes rely on cable trays for wiring).

[0148] A dependency graph uses building components as nodes and dependencies as connecting edges to reflect the pre- and post-construction constraints in project implementation. The system automatically extracts the spatial intersections, system connections, and schedule relationships between building components from the new model information to generate this dependency graph.

[0149] For example, the new model information for a standard layer includes building components such as cast-in-place floor slabs, pre-embedded electrical wiring, water supply manholes, and wall masonry. The system extracts dependencies: pre-embedded electrical wiring must be completed before the cast-in-place floor slab concrete is poured; wall masonry must be carried out after the cast-in-place floor slab has cured; water supply manholes can only be installed with risers after the wall masonry is completed. This generates a dependency graph.

[0150] Step S702: Receive a design change request sent by the terminal device.

[0151] A design change request refers to a proposal to modify the original design scheme submitted by the construction party, designer, or owner via mobile terminal or computer. It typically includes the changed building components, the content of the change, the reason for the change, and the expected change time. Upon receiving the request, the system performs format validation and integrity checks, and records the request's unique identifier, the sender, and the sending time.

[0152] For example, the site construction worker discovered a conflict between the air duct and the main fire sprinkler pipe and submitted a design change request via a tablet computer on site: Change building component: air duct DN400; Change content: horizontal position shifted 300mm eastward; Reason for change: collision with the main sprinkler pipe; Urgency level: medium.

[0153] Step S703: Compare the design change request with the building component nodes in the dependency graph to determine whether it will affect subsequent processes.

[0154] The system locates the target building component node in the dependency graph based on the target building component code specified in the design change request. Then, it uses breadth-first or depth-first search to find all subsequent building component nodes reachable from that node. If the downstream node set is not empty, the system determines that the change will affect subsequent processes; if the downstream node set is empty, the system determines that there is no impact and the change can be executed directly.

[0155] For example, a change request targets a DN400 duct. In the dependency graph, this duct has downstream nodes: duct insulation layer, air outlet installation, duct pressure test, and ceiling enclosure. Since there are four subsequent processes, the system determines that the change will affect these processes. If the change target is a temporary guardrail, which has no downstream nodes in the graph, then it is determined that there is no impact.

[0156] Step S704: If so, calculate the impact data and obtain the quantitative calculation results.

[0157] The impact data includes: the number of subsequent processes affected, the planned start and end times of each process, the changes in required resources, the scope of spatial conflicts, and the area requiring rework. The system uses a dependency graph combined with the construction schedule to simulate the chain reaction, calculate the number of days of delay, additional work, and resource gaps caused by the changes, and generate quantitative calculation results.

[0158] For example, if a DN400 duct is horizontally offset by 300mm, the existing duct supports and hangers need to be removed and redone, affecting downstream processes: insulation layer installation will be delayed by 2 days; air outlet installation by 2 days; pressure testing by 2 days; and ceiling sealing by 2 days. Additional workload: Removing and reinstalling 4 sets of supports and hangers will add 4 working days. The quantitative calculation result is: a total project delay of 2 days, increased labor costs of XXXX yuan, and no additional material purchases.

[0159] Step S705: Evaluate the cost and schedule data based on the quantitative calculation results to obtain the impact assessment value.

[0160] Impact assessment value is a comprehensive indicator that combines factors such as project delays, cost increases, and quality risks into a weighted score or monetization method, making it easier for decision-makers to quickly determine the overall impact of changes.

[0161] For example, the assessment formula can be preset as: Impact Assessment Value = (Number of Days Affected by Project Duration × Workload Weighting Coefficient) + (Increased Cost Amount × Cost Weighting Coefficient) + (Quality Risk Coefficient × Risk Weight). The system adjusts the weights according to the actual situation of the project, such as contract penalties.

[0162] Step S706: Determine the risk level of the change based on the impact assessment value and generate a document signal.

[0163] Changing the risk level involves classifying the impact assessment value into four levels: low, medium, high, and urgent, based on preset thresholds.

[0164] For example, an impact assessment value < 5000 is considered low risk, 5000-20000 is medium risk, 20000-50000 is high risk, and > 50000 is urgent. The document signal refers to the structured change assessment report automatically generated by the system, which includes a summary of the change request, detailed impact analysis, quantitative calculation results, assessment value, and risk level, along with an attached electronic form for the approval process, to be sent to the approver.

[0165] Step S707: Generate and update the impact record table and change tracking table based on the document signals.

[0166] The Impact Record Table is used for long-term storage of assessment details for each design change, including change ID, building component code, before-and-after comparison, quantified impact, assessment value, risk level, approval status, and actual implementation results. The Change Tracking Table tracks the complete status of changes from proposal, assessment, approval to implementation, with each change corresponding to one status record. For example, the Change Tracking Table displays the status from submission of a design change request as follows: Pending Assessment → Under Assessment → Pending Approval → Approved → Implementation → Completed → Verified, thus forming a complete status record. The system writes key fields from document signals into the Impact Record Table and the Change Tracking Table respectively to establish a correlation.

[0167] By adopting the above technical solution and constructing a dependency graph, design change requests during the construction process can be quantified and their impact assessed. After determining the risk level of the change, an impact record table and a change tracking table are generated and updated. This allows for the calculation of the impact of design changes on costs, schedules, and downstream processes, reducing deviations caused by manual estimations and lowering the risk of claims and disputes caused by omissions in changes.

[0168] Based on the same inventive concept, embodiments of this application provide a construction engineering data management system, referring to... Figure 8 The system includes: The acquisition module 801 is used to acquire building component information during the architectural design phase, evidence data sent by terminal devices at key nodes, and actual change information during the construction phase.

[0169] The calculation module 802 generates data codes for each building component and compares the actual change information with the preset model information.

[0170] Output module 803 is used to establish a correspondence table, upload the evidence storage data to the database, new model information, and the updated correspondence table.

[0171] By adopting the above technical solution, the acquisition module is used to acquire building component information during the architectural design phase. The terminal device sends evidence-based data and actual change information during the construction phase at key nodes. The calculation module generates data codes for each building component and compares the actual change information with the preset model information, thus realizing the systematic operation of the building engineering data management method. The processor calls the data processing program stored in the calculation module and executes the corresponding algorithm logic, ensuring accurate connection between each processing link while improving the automation level of the building engineering data management process. The output module is used to establish and update corresponding relationship tables, upload evidence-based data to the database for evidence storage, generate new model information, and update the daily data change statistical record table and anomaly record table for easy recording, retrieval, and viewing.

[0172] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional modules is used as an example. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. The specific working process of the system, device, and unit described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0173] This application provides a computer-readable storage medium storing a computer program that can be loaded by a processor and executed as a construction project data management method.

[0174] Computer storage media include, for example, USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, optical disks, and other media that can store program code.

[0175] Based on the same inventive concept, embodiments of this application provide a smart terminal, including a memory and a processor, wherein the memory stores a computer program that can be loaded by the processor and executed as a construction engineering data management method.

[0176] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional modules is used as an example. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. The specific working process of the system, device, and unit described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0177] The above are all preferred embodiments of this application and are not intended to limit the scope of protection of this application. Any feature disclosed in this specification (including the abstract and drawings) may be replaced by other equivalent or similar features unless specifically stated otherwise. That is, unless specifically stated otherwise, each feature is only one example of a series of equivalent or similar features.

Claims

1. A method for managing construction project data, characterized in that, include: Based on the building component information from the architectural design phase, generate data codes for each building component; Establish a correspondence table based on the relationship between data codes and building components; Key nodes are identified within each critical task point of the construction project, and evidence storage data sent by terminal devices at these key nodes is received. Upload the evidence storage data to the database for evidence storage; Receive actual change information during the construction phase; Compare the actual change information with the preset model information to determine if they are consistent; If not, the model information is updated based on the actual change information to obtain new model information, and the actual change information is associated with the data code to generate an updated correspondence table.

2. The construction project data management method according to claim 1, characterized in that, Before receiving actual change information during the construction phase, the process also includes: The rate of change of the signal is monitored based on the evidence data sent by the terminal device to obtain the rate of change value; Compare the rate of change value with the set change threshold to determine whether the rate of change value is greater than the set change threshold. If not, reduce the sampling frequency of the terminal device based on the rate of change value; Compare the evidence storage data sent by adjacent terminal devices and calculate the difference in changes in the evidence storage data uploaded by adjacent terminal devices; Compare the change difference with the dynamic fluctuation threshold to determine whether the change difference is greater than the dynamic fluctuation threshold; If not, the high-precision data acquisition mode will be triggered, and all parameters in the evidence data will be uploaded. If so, then according to the differential synchronization mechanism, only the key parameters in the evidence storage data will be uploaded.

3. The construction project data management method according to claim 2, characterized in that, After triggering the high-precision data acquisition mode and uploading all parameters from the evidence storage data, the process also includes: According to the preset interference elimination rules, the interference parameters in the evidence storage data are eliminated, and the first parameter set is generated; According to the preset signal correction rules, the parameters in the first parameter set are corrected, and the second parameter set is generated; According to the preset signal amplification rules, the parameters in the second parameter set are amplified, and a third parameter set is generated.

4. The construction project data management method according to claim 3, characterized in that, After generating the third parameter set, the process also includes: According to the preset semantic transformation rules, the parameters in the third parameter set are converted into the first spectrogram; Based on the preset building component conflict rules, calculate the logical conflict relationships between the building components in the first spectrum; Based on the analysis of the causes of logical conflicts between various building components, a conflict cause table is generated; Retrieve conflict solutions from the conflict cause table, and modify the first spectrum based on the conflict solutions to generate the second spectrum.

5. The construction project data management method according to claim 4, characterized in that, After retrieving conflict solutions from the conflict cause table, modifying the first spectrum based on the conflict solutions, and generating the second spectrum, the process further includes: Based on environmental variables, construction parameters, and material property data, a cause-and-effect diagram containing multiple factors is generated. Obtain abnormal data from the third parameter set; Calculate the range of abnormal data based on the conflict cause table and generate abnormal data indicators; Compare the abnormal data indicators with the driving factors in the cause-and-effect diagram to determine whether there is a real causal relationship; If not, then the abnormal data indicators are screened according to the correlation filtering rules to obtain candidate driving factors; Attribution analysis of abnormal data is performed based on candidate driving factors to obtain root cause determination results; The impact of intervention measures is quantified based on the root cause determination results, and counterfactual prediction signals are generated. The conflict cause table and the second spectrum are updated based on the counterfactual prediction signal.

6. The construction project data management method according to claim 5, characterized in that, After quantifying the impact of intervention measures based on the root cause determination results and generating counterfactual prediction signals, the method further includes: Receive natural language query requests sent by terminal devices; Compare the natural language query request with the semantic nodes in the second spectrogram to determine if they match; If not, the natural language query request is reconstructed to obtain an optimized query instruction; The second spectrum was searched according to the optimized query instructions, and search results including data analysis and root cause inference were obtained. The decision recommendation level is determined based on the search results, and a response feedback signal is generated. The question-and-answer log table and decision support record table are generated and updated based on the response feedback signals.

7. A method for managing construction project data according to claim 6, characterized in that, After determining the decision suggestion level based on the search results and generating a response feedback signal, the process further includes: Based on the logical dependencies of building components in the new model information, construct a dependency graph; Receive design change requests sent by terminal devices; Compare the design change requests with the building component nodes in the dependency graph to determine whether they will affect subsequent processes. If so, calculate the impact data to obtain the quantitative calculation results; Based on the quantitative calculation results, cost and schedule data are evaluated to obtain the impact assessment value; The risk level of the change is determined based on the impact assessment value, and a document signal is generated; The impact record table and change tracking table are generated and updated based on the document signals.

8. A construction project data management system, characterized in that, The system is used to execute a construction project data management method as described in any one of claims 1 to 7, comprising: The acquisition module is used to acquire building component information during the architectural design phase, evidence data sent by terminal devices at key nodes, and actual change information during the construction phase. The calculation module generates data codes for each building component and compares actual change information with preset model information; The output module is used to create a correspondence table, upload the evidence storage data to the database, the new model information, and the updated correspondence table.

9. A smart terminal, characterized in that, It includes a memory and a processor, wherein the memory stores a computer program that can be loaded by the processor and executed as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer program is stored that can be loaded by a processor and execute the method as described in any one of claims 1 to 7.

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