Engineering supervision system and method based on BIM

Abstract

The invention provides a BIM-based project supervision system and method. The BIM-based project supervision system comprises a data acquisition module, an integration module, an analysis module and an early warning module; the data acquisition module collects design parameters, construction progress and resource data and transmits the data to the integration module; the integration module unifies data formats, associates and integrates the data formats, and transmits the data formats to the analysis module; the analysis module uses a preset algorithm to calculate and analyze indexes, and sends a result to the early warning module; the early warning module judges whether an early warning signal is triggered or not according to the analysis result; according to the invention, real-time monitoring and risk early warning of the engineering project can be realized, and the efficiency and safety of engineering management are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of construction engineering, and more specifically, to a BIM-based engineering supervision system and method. Background Art

[0002] In the construction industry, project supervision is a key link to ensure that construction projects proceed smoothly in accordance with design requirements and construction standards. Traditional project supervision mainly relies on manual inspection and paper records, which is not only inefficient but also prone to errors, making it difficult to achieve real-time monitoring of construction progress and quality. With the development of building information modeling (BIM) technology, the application of BIM technology in project supervision has gradually increased, but its integration and automation are still limited, and it is unable to give full play to the advantages of BIM technology in data management and analysis. Most of the existing BIM applications focus on the design stage, while the application in the construction stage is relatively lagging behind, especially in terms of construction progress monitoring, resource management and safety warning, and lack of effective integrated solutions.

[0003] In the process of implementing the embodiments of the present invention, the inventors found that there are at least the following problems or defects in the prior art: the existing engineering supervision system lacks the ability to comprehensively collect and integrate design parameters, construction progress and construction resource data, resulting in the inability to achieve comprehensive monitoring of engineering projects; at the same time, the existing system also has deficiencies in data analysis and early warning, and is unable to timely and accurately identify and respond to potential risks and problems, thereby affecting the efficiency and effectiveness of engineering supervision. Summary of the invention

[0004] The present invention provides a BIM-based engineering supervision system and method.

[0005] In a first aspect of the present invention, a BIM-based engineering supervision system is provided, comprising:

[0006] Data collection module, data integration module, indicator analysis module, and early warning module;

[0007] The data acquisition module is used to collect design parameters, construction progress data, and construction resource data in the BIM project, and transmit them to the data integration module;

[0008] The data integration module unifies the format and integrates the received data, and then transmits it to the indicator analysis module;

[0009] The indicator analysis module calculates and analyzes the integrated data based on a preset algorithm, and transmits the analysis results to the early warning module;

[0010] The warning module determines whether to trigger a warning signal based on the analysis result.

[0011] Furthermore, the early warning module includes an early warning rule setting unit and an early warning signal sending unit. The early warning rule setting unit determines whether to trigger an early warning based on the results of the indicator analysis module and preset early warning rules. The early warning signal sending unit sends early warning information to relevant engineering management personnel when the early warning is triggered.

[0012] Furthermore, the indicator analysis module also includes an indicator model optimization unit. When new standard engineering data is supplemented, the indicator model optimization unit optimizes and updates the standard engineering indicator library and algorithm model based on the new data.

[0013] Furthermore, the data acquisition module also includes a data verification unit for verifying the accuracy of the collected data and excluding erroneous data before transmission.

[0014] Furthermore, the data integration module also includes a data backup unit for regularly backing up the integrated data so as to restore the data when it is lost or damaged.

[0015] In a second aspect of the present invention, a BIM-based engineering supervision method is provided, comprising:

[0016] Collect design parameters, construction progress data, and construction resource data in BIM projects;

[0017] Unify the format and integrate the received data;

[0018] Calculate and analyze indicators on the integrated data;

[0019] Determine whether to trigger an early warning signal based on the analysis results.

[0020] According to the above-mentioned embodiments of the present invention, at least the following beneficial effects are achieved: the BIM-based engineering supervision system provided by the present invention can improve the efficiency and accuracy of engineering supervision through automated data collection, integration, analysis and early warning processes. The data collection module of the system can comprehensively collect design parameters, construction progress and resource data to ensure the real-time update and accuracy of all key information. The data integration module further unifies the format of these data and integrates them in association, providing a standardized data basis for subsequent analysis. The indicator analysis module uses a preset algorithm to conduct an in-depth analysis of the integrated data and calculates the deviation coefficient of key indicators, so that possible problems and abnormal situations in the project can be identified in a timely manner. The early warning module determines whether to trigger the early warning signal based on the analysis results, and sends early warning information to relevant engineering management personnel, so that potential risks can be discovered and handled early.

[0021] In addition, this system further improves the accuracy of the analysis results and the adaptability of the system by introducing weight adjustment and indicator model optimization units. The weights of different types of indicators can be adjusted according to their importance in the project, making the analysis results more in line with the actual situation. At the same time, with the continuous addition of new standard engineering data, the system can automatically optimize and update the standard engineering indicator library and algorithm model to ensure the long-term effectiveness and accuracy of the system. This continuous self-optimization capability enables the system to adapt to the ever-changing engineering environment and provide continuous support for engineering supervision. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The above and other objects, features and advantages of the exemplary embodiments of the present invention will become readily understood by reading the detailed description below with reference to the accompanying drawings. In the accompanying drawings, several embodiments of the present invention are shown in an exemplary and non-limiting manner, in which:

[0023] Figure 1 A schematic diagram of the structure of a BIM-based engineering supervision system provided by an embodiment of the present invention;

[0024] Figure 2 A schematic diagram of a process flow of a BIM-based engineering supervision method provided by an embodiment of the present invention;

[0025] Figure 3 The schematic diagram schematically shows the structure of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0026] It should be noted that any number of elements in the drawings is for illustration rather than limitation, and any naming is only for distinction and does not have any limiting meaning.

[0027] Reference below Figure 1 , Figure 1 The structure diagram of the BIM-based engineering supervision system provided by one embodiment of the present invention is as follows. Figure 1 As shown, a BIM-based engineering supervision system 100 includes:

[0028] Data collection module 101, data integration module 102, indicator analysis module 103, early warning module 104;

[0029] The data acquisition module is used to collect design parameters, construction progress data, and construction resource data in the BIM project, and transmit them to the data integration module;

[0030] The data integration module unifies the format and integrates the received data, and then transmits it to the indicator analysis module;

[0031] The indicator analysis module calculates and analyzes the integrated data based on a preset algorithm, and transmits the analysis results to the early warning module;

[0032] The warning module determines whether to trigger a warning signal based on the analysis result.

[0033] It should be noted that this system includes data acquisition module, data integration module, indicator analysis module and early warning module, which work together to realize BIM-based project supervision. Here, BIM refers to building information modeling, which is a digital expression of construction projects that can provide shared knowledge resources about the physical characteristics of construction projects and facility management processes.

[0034] Specifically, the data acquisition module is further divided into the design data acquisition unit, the progress data acquisition unit and the resource data acquisition unit. The design data acquisition unit is responsible for obtaining the structural dimensions and material specification data in the BIM model; the progress data acquisition unit is used to collect the actual construction progress data of the construction site; and the resource data acquisition unit collects the human, material and equipment resource usage data during the construction process. These units can accurately collect the required data from the BIM model and the construction site through specific parameter settings and data interfaces.

[0035] Preferably, the data integration module includes a data format unification unit and a data association unit. The data format unification unit converts data from different sources into a unified data format for subsequent processing. The data association unit associates the design, progress, and resource data according to the component ID in the BIM model.

[0036] In detail, the data integration module unifies the format and integrates the received data, and then transmits it to the indicator analysis module.

[0037] In some embodiments, the data acquisition module includes a design data acquisition unit, a progress data acquisition unit, and a resource data acquisition unit. The design data acquisition unit is used to obtain structural dimensions and material specification data in the BIM model, the progress data acquisition unit is used to collect actual construction progress data at the construction site, and the resource data acquisition unit is used to collect human, material, and equipment resource usage data during the construction process.

[0038] It should be noted that the data acquisition module of this system includes design data acquisition unit, progress data acquisition unit and resource data acquisition unit, which are responsible for collecting different types of data to support project supervision. Here, the design data acquisition unit refers to a component specifically used to extract design-related information such as structural dimensions and material specifications from the BIM model.

[0039] Specifically, the design data acquisition unit can automatically extract the structural dimensions and material specification data in the model by connecting to the BIM software interface. The progress data acquisition unit collects the actual construction progress data of the construction site through on-site data acquisition equipment, such as time-stamp cameras or progress tracking software. The resource data acquisition unit collects the human, material, and equipment resource usage data during the construction process through the resource management system. The parameter settings of these units should be adjusted according to the specific project requirements and the level of detail of the BIM model to ensure the accuracy and completeness of the data.

[0040] Preferably, the design data collection unit can be configured to automatically identify specific components and attributes in the BIM model to reduce manual intervention. The progress data collection unit can integrate GPS and time tracking functions to ensure that the collected data has accurate geographic location and time information. The resource data collection unit can use barcode or RFID technology to track the use of resources, thereby improving the automation and accuracy of data collection. In addition, these units can also be configured with an alarm mechanism. When the collected data does not meet the preset threshold, the system will automatically prompt so that timely measures can be taken.

[0041] In some embodiments, the data integration module includes a data format unification unit and a data association unit, and the data format unification unit converts data from different sources into a unified data format.

[0042] It should be noted that the data integration module of this system includes a data format unification unit and a data association unit, which work together to ensure that data collected from different sources can be effectively integrated and utilized. Here, the data format unification unit refers to a component that converts data from different data sources into a unified format.

[0043] Specifically, the data format unification unit is responsible for converting the data collected from the design data collection unit, the progress data collection unit and the resource data collection unit into a unified data format, such as JSON or XML, for subsequent processing.

[0044] Preferably, the data format unification unit can be configured to support conversion of multiple data formats, including but not limited to CSV, Excel, database export, etc., to adapt to different data sources.

[0045] Furthermore, the data integration module unifies the format and integrates the received data and transmits it to the indicator analysis module in the following steps:

[0046] Identify the data carrier content corresponding to the received data;

[0047] Extracting features from the data carrier content to obtain carrier content features;

[0048] The characteristic Gini coefficient corresponding to the carrier content feature is calculated by the following formula:

[0049]

[0050] Where F represents the characteristic Gini coefficient corresponding to the carrier content feature, H(a) represents the proportion of the ath feature in the carrier content feature, a represents the sequence number corresponding to the carrier content feature, and t represents the number of carrier content features;

[0051] According to the characteristic Gini coefficient, filtering the carrier content characteristics to obtain target carrier content;

[0052] Performing semantic analysis on the target carrier content to obtain the carrier content connotation;

[0053] Calculating the correlation coefficient between the connotations of the carrier content to obtain the connotation correlation coefficient;

[0054] The data correlation degree between the received data is obtained by combining the connotation correlation coefficient.

[0055] Among them, the carrier content feature is the representation corresponding to the data carrier content, the feature Gini coefficient represents the importance corresponding to the carrier content feature, the carrier content connotation is the meaning expressed by the target data content, and the connotation association coefficient represents the association relationship between the carrier content connotations.

[0056] Optionally, the recognition of the data carrier content corresponding to the received data can be achieved through OCR recognition technology; the feature extraction of the data carrier content can be achieved through a bag-of-words model; the filtering processing of the carrier content features can be performed according to the numerical size of the feature Gini coefficient; the semantic analysis of the target carrier content can be achieved through semantic analysis; the connotation correlation coefficients are summed to obtain the data correlation between the received data.

[0057] Optionally, as an optional embodiment of the present invention, the step of calculating the correlation coefficient between the connotations of the carrier content to obtain the connotation correlation coefficient is:

[0058] Performing vector processing on the content connotation of the carrier to obtain a connotation vector;

[0059] Calculating the connotation covariance between the connotations of the carrier contents according to the connotation vectors, and calculating the connotation standard deviation of the connotation of the carrier contents;

[0060] Combining the connotation covariance and the connotation standard deviation, the correlation coefficient between the connotations of the carrier content is calculated by the following formula to obtain the connotation correlation coefficient:

[0061]

[0062] Among them, G represents the connotation correlation coefficient, Cov(M b ,M b+1 ) represents the connotation covariance corresponding to the b-th and b+1-th content connotations in the carrier content connotation, N b and N b+1 They respectively represent the content standard deviations corresponding to the b-th and b+1-th content contents in the carrier content, and b and b+1 respectively represent the serial numbers corresponding to the carrier content contents.

[0063] Furthermore, the data integration module can also include data cleaning and preprocessing functions to remove duplicate data, fill in missing values ​​or correct obvious errors to improve data quality. In the data association process, a version control mechanism can be introduced to track and manage different versions of BIM model data to ensure data consistency and traceability.

[0064] In some embodiments, the preset algorithm of the indicator analysis module includes the following steps:

[0065] Step 1: Establish a standard engineering index library and extract key index data from normal BIM projects as the standard index set I = {i 1 ,i 2 ,…,i m}, where i j represents the jth standard indicator, m is the total number of standard indicators; at the same time, the indicator vector i to be analyzed is extracted from the current engineering data test .

[0066] Step 2: Calculate the index vector i to be analyzed test With each indicator i in the standard indicator set j The deviation coefficient C(i test ,i j ), the calculation formula is:

[0067]

[0068] Among them, i test,j is the jth index component of the index vector to be analyzed, is the mean of the indicators in the standard indicator set, where C(i test ,i j ) is the deviation coefficient between the indicator vector to be analyzed and the standard indicator, which is used to measure the degree of deviation of the current indicator relative to the standard indicator.

[0069] Step 3: Determine the engineering status according to the deviation coefficient and set the deviation threshold S. test ,i j)>S, the project is judged to have abnormal conditions in this indicator. The key indicators include structural safety index, which is calculated through stress and strain of structural components in BIM model; construction progress deviation index, which is obtained by comparing planned progress with actual progress; resource utilization index, which is calculated by the ratio of actual resource usage to planned usage.

[0070] It should be noted that the core of this system lies in the indicator analysis module, which calculates and analyzes the indicators of the integrated data through a specific algorithm and transmits the analysis results to the early warning module. Here, the indicator analysis module refers to the component in the system responsible for processing and analyzing data to identify the engineering status, and the preset algorithm refers to the mathematical model or calculation method used to perform these calculations.

[0071] Specifically, the indicator analysis module first establishes a standard engineering indicator library, which contains the key indicator data in normal BIM projects as a standard indicator set. At the same time, the system extracts the indicator vector to be analyzed from the current engineering data. These steps involve in-depth understanding and analysis of engineering data, including but not limited to structural safety indicators, construction progress deviation indicators, and resource utilization indicators. Parameter settings will be determined based on the specific requirements of the project and historical data to ensure the accuracy and practicality of the analysis results.

[0072] Preferably, the preset algorithm of the indicator analysis module includes calculating the deviation coefficient between the indicator vector to be analyzed and each indicator in the standard indicator set. This deviation coefficient is calculated by a specific formula, which measures the degree of deviation of the current indicator relative to the standard indicator. In actual operation, statistical methods can be used to determine the mean and standard deviation in the standard indicator set, as well as the various indicator components of the indicator vector to be analyzed.

[0073] Furthermore, the system can also assign different weights to different types of indicators according to the specific conditions of the project to adjust their importance in the overall analysis. This can be achieved by introducing a weighted average method, so that key indicators have a greater impact on the final analysis results. In the calculation of the deviation coefficient, it is also possible to consider introducing the concept of a dynamic threshold, which can be adjusted according to changes in the project progress and external conditions to improve the flexibility and adaptability of the system.

[0074] In some embodiments, in step 2, different weights w are assigned to different types of indicators. j , the modified deviation coefficient calculation formula is:

[0075]

[0076] Among them, w j is the weight of the jth indicator, which is used to adjust the importance of different indicators in the overall analysis.

[0077] It should be noted that this system introduces the concept of weight in the indicator analysis module to adjust the importance of different indicators in the overall analysis. Here, weight refers to the value assigned to each indicator to reflect the influence or importance of the indicator in the analysis results.

[0078] Specifically, by assigning different weights to different types of indicators, the system can more accurately reflect the degree of influence of each indicator on the project status. For example, the structural safety indicator may be assigned a higher weight because it is directly related to the safety and reliability of the project. The weight setting can be determined based on historical data analysis, expert opinions, or project management needs. The setting of specific parameters includes but is not limited to the relative importance of each indicator and their contribution to the final analysis results.

[0079] Preferably, the weight distribution can be achieved by a weighted deviation coefficient calculation formula, which takes into account the weight of each indicator and their degree of deviation from the standard indicator. In actual operation, linear weighting, nonlinear weighting or dynamic weight adjustment methods based on machine learning can be used.

[0080] Furthermore, the system can also provide a user interface that allows project managers to manually adjust weights according to the specific conditions of the project, or automatically optimize weight distribution based on historical data analysis. This flexibility enables the system to adapt to different project environments and changing project requirements.

[0081] In some embodiments, the early warning module includes an early warning rule setting unit and an early warning signal sending unit. The early warning rule setting unit determines whether to trigger an early warning based on the results of the indicator analysis module and preset early warning rules. The early warning signal sending unit sends early warning information to relevant engineering management personnel when the early warning is triggered.

[0082] It should be noted that the early warning module of this system includes an early warning rule setting unit and an early warning signal sending unit, which work together to achieve timely response to engineering abnormalities. Here, the early warning rule setting unit refers to a component used to determine whether to trigger an early warning based on the indicator analysis results and preset rules, while the early warning signal sending unit refers to a component that sends early warning information to relevant engineering management personnel when an early warning is triggered.

[0083] Specifically, the warning rule setting unit determines whether a warning needs to be issued based on the results of the indicator analysis module and the preset warning rules. These warning rules can be based on the deviation coefficient exceeding a specific threshold, or abnormal changes in key indicators. The warning signal sending unit is responsible for conveying the warning information to the project management personnel through email, SMS, application push, etc. Parameter settings include the specific conditions of the warning rules, the format of the warning information, and the sending method, which can be customized according to the scale, complexity and management requirements of the project.

[0084] Preferably, the warning rule setting unit can use machine learning methods to automatically learn and optimize warning rules based on historical data to improve the accuracy and timeliness of warnings. The warning signal sending unit can integrate multiple communication protocols to support different communication methods, such as wireless networks, satellite communications, etc., to ensure that warning information can be sent in a timely manner in various environments.

[0085] Furthermore, the system can also provide priority settings for warning information, allowing managers to take different response measures based on the severity of the warning. For example, for abnormalities in structural safety indicators, the system can be set as a high-priority warning, while slight deviations in resource utilization can be set as a low-priority warning. Such refinement can help managers allocate resources and attention more effectively to deal with different engineering risks.

[0086] In some embodiments, the indicator analysis module also includes an indicator model optimization unit. When new standard engineering data is supplemented, the indicator model optimization unit optimizes and updates the standard engineering indicator library and algorithm model based on the new data.

[0087] It should be noted that this system includes an indicator model optimization unit in the indicator analysis module. The function of this unit is to optimize and update the standard engineering indicator library and algorithm model based on new standard engineering data when new standard engineering data is added. Here, the indicator model optimization unit refers to the component in the system that is responsible for continuously improving and adjusting the analysis model to adapt to new data.

[0088] Specifically, the indicator model optimization unit monitors newly collected standard engineering data and updates the existing standard engineering indicator library based on this data. This includes adding new indicators, modifying the parameters of existing indicators, or deleting outdated indicators. At the same time, the algorithm model will also be adjusted based on the new data to improve the accuracy and reliability of the analysis. Parameter settings may include learning rate, number of iterations, model complexity, etc. These parameters can be adjusted according to the performance of the model and the characteristics of the data.

[0089] Preferably, the indicator model optimization unit can adopt an incremental learning or online learning method, so that it can gradually absorb new data and update the model without retraining the entire model. In addition, a model validation step can be introduced to evaluate the performance of the updated model through cross-validation or using an independent test set to ensure the generalization ability of the model.

[0090] Furthermore, in actual operation, an automated model optimization process can be set up to trigger model optimization regularly or under specific conditions to maintain the advancement and adaptability of the system. Such refinement can help the system better adapt to changes in the engineering environment and improve the efficiency and effectiveness of engineering supervision.

[0091] In some embodiments, the data acquisition module further includes a data verification unit for verifying the accuracy of the collected data and eliminating erroneous data before transmission.

[0092] It should be noted that a data verification unit is added to the data acquisition module of this system. The purpose of this unit is to ensure that the collected data is accurate before transmission. Here, the data verification unit refers to the component in the system that is specifically responsible for verifying the accuracy of the data. It eliminates erroneous data through a series of inspection and verification steps.

[0093] Specifically, the data verification unit performs a series of accuracy checks on the data collected from the design data collection unit, the schedule data collection unit, and the resource data collection unit. This may include checks on the integrity, consistency, and logic of the data. For example, it verifies whether the data is within a reasonable range, whether there are missing values, and whether the relationship between the data is as expected. Parameter settings may involve defining the expected range of the data, setting thresholds, and determining rules for data association.

[0094] Preferably, the data verification unit can use a variety of verification techniques, such as statistical analysis, anomaly detection algorithms or machine learning models, to automatically identify and exclude abnormal data. For example, a statistical model based on historical data can be set to identify and mark data points that are significantly different from historical trends.

[0095] Furthermore, the data verification unit can also provide data correction suggestions. When erroneous data is detected, the system can prompt the user to make manual corrections or automatically apply preset correction rules. Such refinement helps to improve the quality and reliability of data, thereby ensuring the accuracy of subsequent analysis and early warning.

[0096] In some embodiments, the data integration module further includes a data backup unit for regularly backing up the integrated data so as to facilitate recovery when the data is lost or damaged.

[0097] It should be noted that the data integration module of this system includes a data backup unit, which is responsible for regularly backing up the integrated data to prevent data loss or damage and to enable recovery. Here, the data backup unit refers to the component in the system that is responsible for creating data copies to ensure data security.

[0098] Specifically, the data backup unit will back up the data processed by the data integration module at predetermined time intervals or when triggered by specific events. This includes the integration results of design, schedule and resource data, as well as any intermediate processing products. The backup can be a full backup or an incremental backup. The specific parameter settings include backup frequency, backup type (full or incremental), storage location and retention policy. These parameters can be adjusted according to the importance of the data, the frequency of changes and the storage cost.

[0099] Preferably, the data backup unit can use a variety of backup technologies, such as cloud storage backup, local disk backup or off-site backup, to ensure data security and recoverability. For example, an automatic backup plan can be set to perform data backup during off-peak hours every day, or to trigger backup immediately after a significant change in data.

[0100] Furthermore, the backup unit can also integrate data encryption and access control functions to protect the privacy and security of the backup data. In the backup strategy, a version control mechanism can also be included so that a specific version of data can be selected for recovery during data recovery. Such refinement helps to improve the flexibility and reliability of data backup and ensure that data can be quickly restored in any situation.

[0101] The above-mentioned embodiments of the present invention have the following beneficial effects: The BIM-based engineering supervision system described in the present invention can provide a comprehensive engineering monitoring solution, and can realize real-time monitoring and management of engineering projects through integrated data acquisition, integration, analysis and early warning modules. The data acquisition module can accurately collect design parameters, construction progress and resource data, and ensure the accuracy of the data through the data verification unit to avoid the impact of erroneous data on subsequent analysis. The data integration module can not only unify the data format, but also realize the correlation correspondence of data through the data association unit, providing an accurate data basis for indicator analysis. The indicator analysis module uses a preset algorithm to calculate and analyze indicators, and can improve the accuracy of the analysis results and the adaptability of the system through weight adjustment and model optimization units. The early warning module triggers the early warning signal in time according to the analysis results, and sends early warning information to the engineering management personnel, so that potential risks can be discovered and handled early.

[0102] In addition, the system regularly backs up the integrated data through the data backup unit to ensure that the data can be quickly restored when it is lost or damaged, which can ensure the security and integrity of the project data. The design of the system allows different weights to be assigned to different types of indicators, making the analysis results more in line with the actual situation, which can improve the pertinence and effectiveness of project supervision. Through the collaborative work of these modules, the system can improve the automation level of project supervision, reduce manual intervention, reduce costs, and improve project quality and construction safety.

[0103] like Figure 2 As shown, in some embodiments, a BIM-based engineering supervision method 200 includes:

[0104] Step 201, collecting design parameters, construction progress data, and construction resource data in the BIM project;

[0105] Step 202, unifying the format and associating the received data;

[0106] Step 203, calculating and analyzing the indicators of the integrated data;

[0107] Step 204: determine whether to trigger an early warning signal based on the analysis result.

[0108] It is understandable that the steps described in the BIM-based engineering supervision method 200 are similar to those described in the reference Figure 1 Therefore, the modules, features and beneficial effects described above for the BIM-based engineering supervision system are also applicable to the BIM-based engineering supervision method 200 and the operations contained therein, and will not be repeated here.

[0109] Reference below Figure 3 , which shows a schematic diagram of a structure 300 of an electronic device suitable for implementing some embodiments of the present invention. The electronic devices in some embodiments of the present invention may include, but are not limited to, mobile terminals such as mobile phones, notebook computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 3 The terminal device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0110] like Figure 3As shown, the electronic device 300 may include a processing device (e.g., a central processing unit, a graphics processing unit, etc.) 301, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 302 or a program loaded from a storage device 308 into a random access memory (RAM) 303. In the RAM 303, various programs and data required for the operation of the electronic device 300 are also stored. The processing device 301, the ROM 302, and the RAM 303 are connected to each other via a bus 304. An input / output (I / O) interface 305 is also connected to the bus 304.

[0111] Typically, the following devices may be connected to the I / O interface 305: input devices 306 including, for example, a touch screen, a touch pad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; output devices 307 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; storage devices 308 including, for example, a magnetic tape, a hard disk, etc.; and communication devices 309. The communication devices 309 may allow the electronic device 300 to communicate with other devices wirelessly or by wire to exchange data. Although Figure 3 The electronic device 300 is shown with various devices, but it should be understood that it is not required to implement or possess all the devices shown. More or fewer devices may be implemented or possessed instead. Figure 3 Each block shown in the figure may represent one device, or may represent multiple devices as required.

[0112] Furthermore, the storage medium of the embodiment of the present application stores program instructions that can implement all the above methods, wherein the program instructions can be stored in the above storage medium in the form of a software product, including several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) or a processor to execute all or part of the steps of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, or terminal devices such as a computer, a server, a mobile phone, and a tablet.

[0113] The above descriptions are only some preferred embodiments of the present invention and an explanation of the technical principles used. Those skilled in the art should understand that the scope of the invention involved in the embodiments of the present invention is not limited to the technical solutions formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above inventive concept. For example, the above features are replaced with (but not limited to) technical features with similar functions disclosed in the embodiments of the present invention to form a technical solution.

Claims

1. A BIM-based engineering supervision system, characterized in that: It includes data collection module, data integration module, indicator analysis module and early warning module; The data acquisition module is used to collect design parameters, construction progress data, and construction resource data in the BIM project, and transmit them to the data integration module; The data integration module unifies the format and integrates the received data, and then transmits it to the indicator analysis module; The indicator analysis module calculates and analyzes the integrated data based on a preset algorithm, and transmits the analysis results to the early warning module; The warning module determines whether to trigger a warning signal based on the analysis result.

2. According to the BIM-based engineering supervision system of claim 1, it is characterized in that: The data acquisition module includes a design data acquisition unit, a progress data acquisition unit, and a resource data acquisition unit. The design data acquisition unit is used to obtain structural dimensions and material specification data in the BIM model, the progress data acquisition unit is used to collect actual construction progress data at the construction site, and the resource data acquisition unit is used to collect human, material, and equipment resource usage data during the construction process.

3. The BIM-based engineering supervision system according to claim 1 is characterized in that: The data integration module includes a data format unification unit and a data association unit, wherein the data format unification unit converts data from different sources into a unified data format by normalization; The normalization process may adopt linear normalization or standardization.

4. The BIM-based engineering supervision system according to claim 1, characterized in that: The preset algorithm of the indicator analysis module includes the following steps: Step 1: Establish a standard engineering index library and extract key index data in normal BIM projects as the standard index set I = {i1, i2, ..., i m }, where i j represents the jth standard indicator, m is the total number of standard indicators; at the same time, the indicator vector i to be analyzed is extracted from the current engineering data test ; Step 2: Calculate the index vector i to be analyzed test With each indicator i in the standard indicator set j The deviation coefficient C(i test ,i j ); Step 3: Determine the engineering status according to the deviation coefficient and set the deviation threshold S. test ,i j )>S, the project is judged to have abnormal conditions in this indicator. Among them, the key indicators include structural safety index, which is calculated through the stress and strain of structural components in the BIM model; construction progress deviation index, which is obtained by comparing the planned progress with the actual progress; resource utilization index, which is calculated by the ratio of actual resource usage to planned usage.

5. The BIM-based engineering supervision system according to claim 4 is characterized in that: In step 2, different weights w are assigned to different types of indicators. j .

6. The BIM-based engineering supervision system according to claim 1 is characterized in that: The early warning module includes an early warning rule setting unit and an early warning signal sending unit. The early warning rule setting unit determines whether to trigger an early warning based on the results of the indicator analysis module and preset early warning rules. The early warning signal sending unit sends early warning information to relevant project management personnel when an early warning is triggered.

7. The BIM-based engineering supervision system according to claim 1 is characterized in that: The indicator analysis module also includes an indicator model optimization unit. When new standard engineering data is added, the indicator model optimization unit optimizes and updates the standard engineering indicator library and algorithm model based on the new data.

8. The BIM-based engineering supervision system according to claim 1, characterized in that: The data acquisition module also includes a data verification unit, which is used to verify the accuracy of the collected data and eliminate erroneous data before transmission.

9. The BIM-based engineering supervision system according to claim 1, characterized in that: The data integration module also includes a data backup unit, which regularly backs up the integrated data so as to restore the data when it is lost or damaged.

10. A BIM-based engineering supervision method, characterized in that: Collect design parameters, construction progress data, and construction resource data in BIM projects; Unify the format and integrate the received data; Calculate and analyze indicators on the integrated data; Determine whether to trigger an early warning signal based on the analysis results.

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