Construction engineering supervision informatization platform and management and control system based on artificial intelligence

Abstract

The invention belongs to the technical field of constructional engineering supervision informatization, and relates to a constructional engineering supervision informatization platform and management and control system based on artificial intelligence. The system is based on a constructed data acquisition module, a data analysis module, an association early warning module and a database. A data basis is provided for subsequent analysis of the engineering evaluation condition of the current constructional engineering and the work evaluation condition of a target supervisor, and meanwhile, the traceability of the data is also ensured; the method avoids the traditional manual inspection and subjective judgment, improves the efficiency and accuracy of anomaly detection, quantifies the performance such as the problem discovery rate and the processing efficiency of a supervisor, reduces the management subjectivity of only passing supervision log assessment, and facilitates the formation of a standardized evaluation system. The causal relationship between the supervision efficiency and the engineering risk is identified, the risk can be warned in advance, the problem rectification closed-loop rate is promoted, and the rework cost and the accident rate are reduced.

Description

Technical Field

[0001] The present invention belongs to the technical field of construction engineering supervision informatization, and relates to a construction engineering supervision informatization platform and a management and control system based on artificial intelligence. Background Art

[0002] The current construction industry is moving towards large-scale and complex development. Engineering projects involve many professional fields and participants, and the difficulty of information flow and coordination has increased. For example, urban complex projects cover multiple functions such as commercial, office, and residential. The construction of each part is interrelated. Traditional construction project supervision relies on manual recording and simple data analysis, which is inefficient and prone to errors. When supervisors record project progress and quality data, omissions and errors may occur. Faced with massive data, manual analysis is difficult to quickly gain insight into potential problems, resulting in quality risks that are difficult to discover and deal with in a timely manner. Therefore, the construction project supervision platform based on artificial intelligence has realized the intelligent and information-based transformation of construction project supervision.

[0003] In the prior art, there are also some related solutions related to construction project supervision, for example, the invention patent application of an intelligent integrated cloud platform for construction project supervision based on BIM technology, with Chinese patent publication number CN116542469A, includes a terminal BIM, a supervision terminal computer, a mobile terminal and a construction team terminal. The supervision terminal computer is connected to the BIM model database, and a supervision management system is provided inside the supervision terminal computer. The supervision management system has information of supervisors input inside, and a supervision personnel allocation system is provided inside the supervision management system. The supervision computer and the construction team terminal are both connected to the cloud server. A positioning module and an error calculation system are provided in the mobile terminal. The present invention can realize the data intercommunication and sharing of supervision and construction by connecting the supervision terminal computer and the construction terminal to the cloud, and the supervision terminal can also directly read the standard BIM model data and send it to the mobile terminal to realize direct comparison and calculation during supervision work, so that the management of supervision work is more convenient and the efficiency of supervision work is improved.

[0004] Another invention patent application for a BIM-based engineering supervision information management system with a Chinese patent publication number of CN113065756A includes: BIM information model building module: a complete engineering project is divided into multiple sub-units, and each sub-unit establishes its own information model according to its own work tasks. In the present invention, the control of engineering quality is of great significance, and it can avoid the problem of missing inspections to a certain extent, improve the accuracy and objectivity of engineering inspection data, more intuitively reflect the construction progress, and have strong coordination, effectively guarantee the real-time progress and quality of the project, and realize mutual supervision between each sub-unit, and use information sharing between each sub-unit to achieve platformization, further improve the efficiency of problem solving, and strictly control each link to ensure the quality of the project.

[0005] Although the above scheme proposes some solutions for construction project supervision, it still has certain limitations: on the one hand, some existing technologies have realized direct comparison and calculation during supervision work, making the management of supervision work more convenient and improving the efficiency of supervision work. Some existing technologies have avoided the problem of missed inspections to a certain extent, improved the accuracy and objectivity of engineering inspection data, and ensured the quality of the project. However, the existing technology lacks the analysis of construction project anomalies through actual detection, and also ignores the fusion analysis of actual detection and supervision log to obtain the performance evaluation of the supervisor, thereby unable to avoid the subjective judgment of traditional supervisors' inspections, unable to improve the efficiency and accuracy of anomaly detection, and unable to avoid the subjectivity of management of supervisor assessment only through supervision logs.

[0006] On the other hand, existing technologies have not linked abnormal conditions of construction projects with the supervisor's supervision work performance evaluation, making it impossible to identify the causal relationship between supervision effectiveness and detection project risks, which is not conducive to early warning of risks, and cannot promote the closed-loop rate of problem rectification and reduce rework costs and accident rates. Summary of the invention

[0007] In view of this, in order to solve the problems raised in the above background technology, an artificial intelligence-based construction project supervision information platform and management and control system is proposed.

[0008] The purpose of the present invention can be achieved through the following technical solutions: The present invention provides an artificial intelligence-based construction engineering supervision information platform and management and control system, including: a data acquisition module, which stores the implementation data and supervision log of the target supervisor corresponding to the current construction project collected in real time in a database. The implementation data includes project progress information, information on each actual implementation method, and project quality information.

[0009] The data analysis module analyzes the engineering assessment of the current construction project and the work evaluation of the target supervisor based on the implementation data and the supervision log, stores them in the database, and performs management and control at the same time.

[0010] The data analysis module includes an engineering anomaly assessment unit and a supervision evaluation unit. The engineering anomaly assessment unit dynamically compares the implementation data with the corresponding standard implementation data to analyze the engineering progress deviation, the implementation method compliance and the quality defects. The supervision evaluation unit integrates the implementation data with the supervision log to obtain the work coverage, problem discovery rate and problem handling efficiency of the target supervisor.

[0011] The correlation warning module will conduct correlation analysis and provide feedback on the engineering assessment and work evaluation situations of each analysis of the target supervisor extracted from the database.

[0012] The database stores the current construction project plan files corresponding to the target supervisor, the project assessment and work evaluation of each supervisor's analysis, and the construction project images corresponding to the completion percentage of each project.

[0013] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. The present invention collects the implementation data and supervision log of the target supervisor corresponding to the construction project in real time and stores them in the database, providing a data basis for subsequent project evaluation and supervision work evaluation, and ensuring data traceability.

[0014] 2. The present invention obtains abnormal conditions such as quality defects and progress delays in the current construction project by performing dynamic deviation comparative analysis on the implementation data and the supervision log, thereby avoiding traditional manual inspections and subjective judgments, and improving the efficiency and accuracy of abnormality detection. At the same time, through multi-source data fusion analysis, the problem discovery rate, processing efficiency and other performance of the supervisor are quantified, reducing the subjectivity of management assessed only by the supervision log, which is conducive to the formation of a standardized evaluation system.

[0015] 3. The present invention identifies the causal relationship between supervision effectiveness and engineering risks by correlating engineering anomaly assessments such as progress deviation, construction method compliance and quality defects with work evaluations such as the supervisor's work coverage, problem discovery rate and problem handling efficiency, which helps to provide early warning of risks, such as hidden dangers in high-risk areas caused by insufficient coverage of a supervisor, promotes the closed-loop rate of problem rectification, and reduces rework costs and accident rates. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for describing the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0017] Figure 1 It is a schematic diagram of system module connection of the present invention.

[0018] Figure 2 It is a flow chart of the module implementation process of the present invention.

[0019] Figure 3 Schematic diagram of the hardware structure of the computer of the present invention.

[0020] Description of the drawings: 200, bus; 201, processor; 202, memory; 203, communication interface. DETAILED DESCRIPTION

[0021] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0022] Embodiment 1

[0023] See also Figure 1 As shown, the present invention provides an information platform and management and control system for construction engineering supervision based on artificial intelligence, and the specific modules are distributed as follows: data acquisition module, data analysis module, associated warning module and database. Among them, the connection mode between the modules is: the data acquisition module is connected to the data analysis module, the data analysis module is connected to the associated warning module, and the database is connected to the data acquisition module, the data analysis module and the associated warning module respectively.

[0024] like Figure 2 The module implementation process flow chart shown in the figure, the data collection module stores the implementation data and supervision log of the target supervisor corresponding to the current construction project in real time in the database. The implementation data includes project progress information, actual implementation method information and project quality information.

[0025] As a preferred feasibility example, the project progress information includes the interval duration of each implemented project node and the project completion percentage.

[0026] It needs to be further explained that the specific method of obtaining the interval duration of each implemented project node and the percentage of project completion is: the cameras that are deployed are convenient for recording the start time and end time of each implemented project node, and the end time of each implemented project node is subtracted from the start time, and the difference obtained is the interval duration of each implemented project node.

[0027] The current construction project image captured by the deployed cameras is matched with the construction project images corresponding to the completion percentages of each project stored in the database to obtain the completion percentage of the project.

[0028] The information of each actual implementation method includes each specific construction process in the current project progress and its corresponding technical parameters.

[0029] It needs to be further explained that the specific method of obtaining the specific construction processes and their corresponding technical parameters in the current project progress is: a high-definition camera is used to shoot real-time footage of the construction site of the construction project to obtain a video image of the construction site of the construction project, and the target detection algorithm in the image recognition technology is used to identify it to obtain the specific construction processes in the current project progress.

[0030] Specifically, the target detection algorithm is used to extract features such as equipment features (different construction equipment has unique features such as shape, structure and color), material features (different construction materials have their own appearance characteristics) and personnel features (personnel's movements and behavior patterns) in video images of construction site of construction projects. Based on the extracted features, template matching or classification algorithms such as support vector machine (SVM) are used to compare and match the image features to be identified with pre-established feature templates of equipment features, material features and personnel features corresponding to each construction process, so as to determine the categories corresponding to the equipment, materials and personnel operations appearing in the image, and then infer the ongoing construction process.

[0031] Specifically, if the equipment features, material features and personnel features in the video image of the construction site of the construction project are consistent with the feature templates of the equipment features, material features and personnel features corresponding to a certain construction process established in advance, then the construction process will be recorded as the specific construction process in the current project progress, and then the specific construction processes in the current project progress will be obtained.

[0032] The corresponding technical parameters of each specific construction process in the current project progress can be obtained by the method of obtaining each specific construction process in the current project progress.

[0033] The project quality information includes the dimensional deviation of each monitoring position, the degree of concrete surface cracks, and the proportion of steel bar corrosion.

[0034] It needs to be further explained that the specific method of obtaining the dimensional deviation of each monitoring position, the degree of concrete surface cracks, and the proportion of steel bar corrosion is: using image recognition technology for multiple groups of images of the construction site of the construction project, and using edge detection algorithms (such as the Canny algorithm) to extract edge features of the monitoring position; for objects of regular shapes, their boundaries can be determined by fitting geometric figures (such as rectangles, circles, etc.), and then according to a predetermined image scale, the pixel distance in the image is converted into an actual physical size, and then the difference between the actual size and the design size of the monitoring position is calculated to obtain the dimensional deviation of each monitoring position.

[0035] By using cameras with high resolution and good optical performance, concrete products at the construction site of the construction project are photographed in real time, and multiple groups of images of concrete products at the construction site of the construction project are obtained. The images are enhanced (such as contrast stretching, histogram equalization, etc.) to highlight the difference between the cracks and the concrete surface. Then, the cracks are separated from the background by image segmentation algorithms (such as threshold segmentation, region growing algorithm, etc.) to obtain a binary image containing only the cracks. The segmented crack images are further feature extracted (including the length, width, area, direction, etc. of the cracks). The length and area of ​​the cracks are counted by calculating the number and distribution of crack pixels. The direction and width of the cracks are determined by fitting and analyzing the crack edges. Then, according to the characteristic parameters of the cracks, the degree of cracks on the concrete surface is evaluated (such as classifying cracks into different grades according to relevant standards).

[0036] Specifically, if the evaluation is based on the length of the crack, its grade classification example is: when the crack length is less than 0.5 meters, it is classified as a mild crack; a length between 0.5 meters and 1 meter is a moderate crack; and a length exceeding 1 meter is considered a severe crack.

[0037] If evaluated based on crack width, the grade classification example is: when the crack width is less than 0.2 mm, it is classified as a mild crack; when the crack width is between 0.2 mm and 0.3 mm, it is classified as a moderate crack; and when it exceeds 0.3 mm, it is classified as a severe crack.

[0038] If the evaluation is based on the crack area, the grade classification example is: if the crack area accounts for less than 5% of the total concrete surface area, it can be regarded as a light crack; a ratio between 5% and 10% is a moderate crack; and a ratio exceeding 10% is considered a severe crack.

[0039] If the evaluation is based on the direction of the cracks, the grade classification example is: if the crack direction is relatively regular, basically parallel to the force direction of the structure or at a small angle, and the parameters such as length and width are relatively small, it can be classified as a light crack; if the crack direction is perpendicular to the force direction or at a large angle, even if the length and width are not large, it is judged to be a moderate crack; if the cracks are distributed in an irregular network, multiple cracks intersect with each other and the direction is complex, then it is a severe crack.

[0040] Use equipment such as concrete radar to first determine the location of the steel bars, then drill small holes in the concrete surface or use local damage methods to expose the steel bar surface for shooting, and then perform color space conversion, noise reduction and other preprocessing on the collected images, and use color features and texture features to identify the steel bar corrosion area. Use image analysis software to calculate the ratio of the number of pixels in the rusted area to the number of pixels in the entire steel bar image, and then convert it into the ratio of the actual rusted area to the total area of ​​the steel bar according to the image scale, so as to obtain the steel bar corrosion ratio.

[0041] It should be explained that the corroded steel bars usually present different colors from the uncorroded parts, such as red, yellow, etc. The corroded areas can be initially screened out by setting the color threshold. At the same time, the corrosion will make the texture of the steel bar surface rough and uneven, and the texture analysis algorithm can be used to further refine the identification of the corroded areas.

[0042] The present invention collects the implementation data and supervision log of the corresponding construction project of the target supervisor in real time and stores them in the database, which provides a data basis for the subsequent project evaluation and supervision work evaluation and ensures the data traceability.

[0043] The data analysis module analyzes the engineering assessment of the current construction project and the work evaluation of the target supervisor based on the implementation data and the supervision log, stores them in the database, and performs management and control at the same time.

[0044] The data analysis module includes an engineering anomaly assessment unit and a supervision evaluation unit. The engineering anomaly assessment unit dynamically compares the implementation data with the corresponding standard implementation data to analyze the engineering progress deviation, the implementation method compliance and the quality defects. The supervision evaluation unit integrates the implementation data with the supervision log to obtain the work coverage, problem discovery rate and problem handling efficiency of the target supervisor.

[0045] Among them, the connection method between the units is: the engineering abnormality assessment unit is connected with the supervision evaluation unit.

[0046] What needs further explanation is that by integrating implementation data (such as construction progress, quality inspection results, etc.) and supervision logs (such as inspection records, problem rectification records, etc.), the supervisor's work coverage, problem detection ability and processing efficiency can be quantitatively analyzed to replace traditional subjective evaluation, improve the objectivity of the evaluation, reduce differences in human operations, and improve the standardization of supervision (for example, the number of inspections recorded in the supervision log is automatically compared with the actual arrival time monitored by the sensor to avoid false reporting); and then identify the shortcomings of the supervisor's work (such as low coverage in a certain area, high missed detection rate of a certain type of problem), which will help to adjust the inspection plan in a targeted manner and improve the overall supervision efficiency; realize the closed-loop management of "monitoring → analysis → feedback → improvement".

[0047] As a preferred feasibility example, the specific analysis method of the project progress deviation is: extract the interval time of each implemented project node of the current construction project corresponding to the target supervisor and percentage of project completion ,in , is the number of each implemented project node, is the number of implemented project nodes.

[0048] Match the current time point with the target supervisor's corresponding project completion percentage at each time point in the current construction project plan file stored in the database to obtain the project completion percentage at the current time point .

[0049] According to the calculation formula Get the target supervisor's project progress deviation for the current construction project ,in The target supervisor extracted from the database corresponds to the duration between the project nodes in the current construction project plan file and the project nodes that have been implemented.

[0050] It needs to be further explained that The purpose is to determine whether the project is progressing on time by identifying the difference between the actual construction period and the planned construction period. If the ratio of the cumulative sum of the intervals between the implemented engineering nodes of the current construction project corresponding to the target supervisor and the cumulative sum of the intervals between the engineering nodes corresponding to the implemented engineering nodes in the plan file of the current construction project corresponding to the target supervisor is greater than 1, it means that the target supervisor has not promoted the current construction project on time, and the actual construction period is longer than the planned construction period, which will have a negative impact on it and there is a risk of project delay. If the ratio of the cumulative sum of the intervals between the implemented engineering nodes of the current construction project corresponding to the target supervisor and the cumulative sum of the intervals between the engineering nodes corresponding to the implemented engineering nodes in the plan file of the current construction project corresponding to the target supervisor is less than 1, it means that the actual construction period of the current construction project corresponding to the target supervisor is less than the planned construction period, which will have a positive impact on it and there is no risk of project delay.

[0051] Said By comparing the actual percentage of project completion with the planned percentage of project completion, the progress of the project can be judged. If the ratio of the actual percentage of project completion to the planned percentage of project completion is greater than 1, it means that the actual progress of the project completion is better than the planned progress of the project completion, which has a positive impact on it and there is no risk of project delay; if the ratio of the actual percentage of project completion to the planned percentage of project completion is less than 1, it means that the actual progress of the project completion is worse than the planned progress of the project completion, which has a negative impact on it and there is a risk of project delay.

[0052] The target supervisor uses the progress deviation analysis formula for the current construction project to analyze the progress deviation from the two dimensions of "time management" and "execution efficiency" to avoid the one-sidedness of a single indicator (such as only looking at the construction period). It not only reflects the difference between the actual time consumed and the planned time consumed in each construction stage, and reflects the supervisor's ability to connect processes and control time; it also compares the actual completion volume with the time progress to reflect the overall progress efficiency.

[0053] As a preferred feasibility example, the specific analysis method for the compliance of the implementation method is: extract the specific construction processes and their corresponding technical parameters in the current project progress of the target supervisor corresponding to the current construction project, and compare them with the specific construction processes and their corresponding technical parameters in the corresponding project progress in the plan file of the target supervisor corresponding to the current construction project stored in the database, screen out the specific construction processes and their corresponding technical parameters in the current project progress of the target supervisor corresponding to the current construction project that are consistent with the plan file, and record them as the specific construction processes and their corresponding technical parameters in the current project progress of the target supervisor corresponding to the current construction project, and then obtain the non-compliant specific construction processes and their corresponding non-compliant technical parameters in the current project progress of the target supervisor corresponding to the current construction project, which are collectively referred to as the compliance of the implementation method of the target supervisor corresponding to the current construction project.

[0054] Specifically, if a specific construction process in the current project progress of the current construction project of the target supervisor is consistent with a specific construction process in the corresponding project progress in the planning document, then the specific construction process in the current project progress of the current construction project of the target supervisor is recorded as the target supervisor's current project progress corresponding to the specific construction process. If the technical parameters corresponding to a specific construction process in the current project progress of the current construction project of the target supervisor are consistent with the technical parameters corresponding to the specific construction process in the corresponding project progress in the planning document, then the technical parameters corresponding to the specific construction process in the current project progress of the target supervisor's current construction project are recorded as the target supervisor's specific construction process in the current project progress corresponding to the technical parameters, thereby obtaining the specific construction processes and their corresponding technical parameters in the current project progress of the current construction project of the target supervisor.

[0055] It needs to be further explained that different construction methods are formed based on a lot of practical experience and scientific research. They have specific operating procedures and quality control points for specific engineering parts, building materials and construction environments. For example, the "layered pouring and secondary vibration" method is used in large-volume concrete construction, which can effectively control the internal temperature stress of concrete, prevent cracks, and ensure the integrity and durability of the concrete structure. Selecting appropriate methods and strictly implementing them can standardize the construction process and ensure that the quality of the project meets the expected standards. Scientific and reasonable methods fully consider the safety factors in the construction process and formulate corresponding safety technical measures. Construction according to the selected method can reduce the risk of safety accidents.

[0056] If the pre-selected construction method is not used, quality defects may occur due to mismatch of construction technology. In addition, each construction method has corresponding safety operating procedures. Changing the construction method may expose construction personnel to unknown safety risks without corresponding safety training and technical briefing. The pre-selected construction method is the basis of the construction schedule. If the construction method is changed midway, it may disrupt the original construction rhythm and resource allocation plan.

[0057] Therefore, the present invention helps to ensure the quality, construction safety and construction efficiency of the project by detecting and analyzing the compliance of the implementation method.

[0058] As a preferred feasibility example, the specific analysis method of the quality defect situation is: extract the dimensional deviation, concrete surface crack degree and steel bar corrosion ratio of each monitoring position corresponding to the current construction project of the target supervisor, and compare them with the preset corresponding monitoring position dimensional allowable deviation, concrete surface allowable crack degree and steel bar corrosion ratio, respectively, to determine the quality defects of each monitoring position, concrete surface quality defects and steel bar quality defects of the target supervisor corresponding to the current construction project, which are collectively referred to as the quality defects of the target supervisor corresponding to the current construction project.

[0059] Specifically, if the dimensional deviation of a certain monitoring position corresponding to the current construction project of the target supervisor is greater than or equal to the allowable dimensional deviation of the corresponding monitoring position, then the quality defect of the monitoring position corresponding to the current construction project of the target supervisor will be recorded as a dimensional deviation defect, and then the quality defect of each monitoring position of the target supervisor corresponding to the current construction project will be obtained.

[0060] If the degree of cracks on the concrete surface corresponding to the current construction project of the target supervisor is greater than or equal to the allowable degree of cracks on the concrete surface, the quality defect of the concrete surface corresponding to the current construction project of the target supervisor will be recorded as the presence of cracks.

[0061] If the steel bar corrosion rate of the target supervisor's corresponding current construction project is greater than or equal to the permitted steel bar corrosion rate, the steel bar quality defect of the target supervisor's corresponding current construction project will be recorded as the presence of corrosion.

[0062] What needs to be further explained is that by testing the quality conditions such as dimensional deviation at each monitoring position of the building structure, the degree of concrete surface cracks and the proportion of steel bar corrosion, it is possible to promptly discover quality defects that may affect the bearing capacity and stability of the structure, such as voids inside the concrete and severe corrosion of the steel bars, and take reinforcement or repair measures in advance to ensure that the acceptance standards are met, reduce the cost of subsequent rectification, and ensure that the quality is traceable throughout the process. At the same time, by reducing rework and delays, it directly reduces project costs.

[0063] As a preferred feasibility example, the specific analysis method of the target supervisor's work coverage rate is: extract the spatial coverage rate and time coverage rate from the target supervisor's supervision log, and calculate the target supervisor's work coverage rate by summing them according to the corresponding weights. The work coverage rate is the degree of coverage of the key areas, processes and time periods of the construction site by the supervisor, reflecting the comprehensiveness of his work. The spatial coverage rate is to use GIS tools (such as ArcGIS, QGIS) to superimpose the movement trajectory of the supervisor with the key areas and calculate the coverage percentage. The time coverage rate is to count the frequency of the supervisor's presence in key construction periods (such as concrete pouring and steel structure hoisting).

[0064] As a preferred feasibility example, the specific analysis method of the target supervisor's problem discovery rate is: extract the number of problems found in the target supervisor's current construction project from the target supervisor's supervision log, and compare it with the total number of problems in the current construction project. The quotient obtained is the target supervisor's problem discovery rate. The problem discovery rate is the supervisor's ability to proactively discover construction quality, safety, and progress problems, reflecting his professionalism and initiative.

[0065] As a preferred feasibility example, the specific analysis method of the problem handling efficiency of the target supervisor is as follows: the average processing time, the proportion of overtime problems and the problem recurrence rate are extracted from the supervision log of the target supervisor, and recorded as , find out the problem-solving efficiency of the target supervisor ,in is the preset average processing time threshold, To prevent the denominator from being a minimum value of 0, the unit is hours. They are the corresponding weight coefficients of the average processing time, the proportion of timeout problems and the problem recurrence rate obtained through experimental fitting.

[0066] Specifically, the average processing time is equal to the average of the difference between the time it takes to complete rectification of all problems and the time it takes to discover the problems; the proportion of overdue problems is equal to the proportion of problems that exceed the rectification time limit agreed in the contract; the problem recurrence rate is equal to (the number of times the same problem occurs repeatedly / the total number of problems) × 100%).

[0067] A specific example, .

[0068] The average processing time is the most direct and intuitive indicator to measure the efficiency of supervisors in handling problems. It reflects the average time spent by supervisors in handling various problems, and can directly reflect the speed and work rhythm of supervisors in handling problems. A shorter average processing time means that supervisors can respond and solve problems quickly, making the project progress smoother, reducing the impact of problems on project progress and quality, and improving overall project efficiency.

[0069] The recurrence rate of problems reflects the quality and depth of the supervisor's handling of problems. If the recurrence rate of problems is high, it means that the supervisor may not have solved the problem fundamentally, but only temporarily covered up or alleviated the symptoms, which may cause the problem to recur, not only increasing the cost of handling the problem, but also causing greater hidden dangers and losses to the project, affecting the stability and reliability of the project. Controlling the recurrence rate of problems is crucial to ensuring the quality of the project and long-term stable operation.

[0070] The proportion of overtime problems mainly reflects whether the supervisor can complete the problem handling task within the specified time. Although overtime may have a certain impact on the progress of the project, if the overall average handling time is short and the problem recurrence rate is low, the occasional overtime problem may have a relatively small impact on the overall project. In addition, some complex problems may indeed require more time to handle, and the supervisor's work efficiency and ability cannot be completely denied simply because of overtime.

[0071] It is necessary to further explain that the above weight coefficient ranking is not absolute. In practical applications, it can be adjusted according to the characteristics and requirements of specific projects and the degree of emphasis on different indicators. For example, for some emergency projects with extremely strict time requirements, the weight of the proportion of overtime problems may be appropriately increased; for some key projects with extremely high quality requirements and no recurrence of problems, the weight of the recurrence rate of problems may be raised to the highest level.

[0072] The present invention obtains abnormal conditions such as quality defects and progress delays in the current construction project by performing dynamic deviation comparative analysis on the implementation data and the supervision log, thereby avoiding traditional manual inspections and subjective judgments and improving the efficiency and accuracy of abnormality detection. At the same time, through multi-source data fusion analysis, the present invention quantifies the supervisor's problem discovery rate, processing efficiency and other performance, reduces the subjectivity of management assessed only by supervision logs, and is conducive to the formation of a standardized evaluation system.

[0073] The correlation warning module will conduct correlation analysis and provide feedback on the engineering assessment and work evaluation situations of each analysis of the target supervisor extracted from the database.

[0074] As a preferred feasibility example, the specific operations for managing and controlling the engineering evaluation status of the current construction project and the work evaluation status of the target supervisor include: extracting the engineering evaluation status of the target supervisor corresponding to the current construction project.

[0075] If the project progress deviation of the target supervisor corresponding to the current construction project is greater than or equal to the deviation threshold, the cause will be further analyzed and feedback will be given.

[0076] Specifically, the reasons why the target supervisor's project progress deviation is large for the current construction project are found from the target supervisor's supervision log, and timely adjustments are made accordingly.

[0077] Extract the target supervisor's corresponding implementation method compliance status of the current construction project, the target supervisor's corresponding current construction project progress, the non-compliant specific construction processes and their corresponding non-compliant technical parameters, and further obtain the reasons for the non-compliance.

[0078] Specifically, the reasons for non-compliance with the specific construction processes and the corresponding technical parameters in the current project progress of the current construction project of the target supervisor are found from the supervision log of the target supervisor, and the rationality of the reasons for non-compliance are analyzed. If they are reasonable, the existing construction processes and the corresponding technical parameters are kept unchanged. If they are unreasonable, the existing construction processes and the corresponding technical parameters are adjusted to the construction processes and the corresponding technical parameters in the plan document.

[0079] Provide feedback to the target supervisor on the quality defects of the current construction project and make corrections based on the specific causes of the quality defects.

[0080] Extract the performance evaluation results of the target supervisor.

[0081] If the work coverage of the target supervisor is less than the preset work coverage threshold, the target supervisor is reminded to increase the supervision coverage.

[0082] If the problem discovery rate of the target supervisor is lower than the preset problem discovery rate threshold, the target supervisor is reminded to strengthen quality inspection, and at the same time, the supervision platform manager increases the quality inspection learning indicators of the target supervisor.

[0083] If the target supervisor's problem handling efficiency is lower than the preset problem handling efficiency, the target supervisor is reminded to increase the problem handling speed. At the same time, the supervision platform manager increases the target supervisor's problem handling learning indicators.

[0084] As a preferred feasibility example, the specific analysis content of the associated warning module is: extracting from the database the project progress deviation incidence rate, implementation method non-compliance incidence rate and quality defect incidence rate of each analysis belonging to the target supervisor, as well as the work coverage rate, problem discovery rate and problem handling efficiency.

[0085] According to the Pearson correlation coefficient calculation formula The correlation between the target supervisor's engineering evaluation and work evaluation is obtained, where The target supervisor belongs to The project progress deviation rate, the non-compliance rate of the implementation method, or the quality defect rate analyzed in this analysis, The target supervisor belongs to The work coverage rate or problem discovery rate or problem handling efficiency of the analysis, The average rate of deviation in the project progress, the average rate of non-compliance in the implementation method, or the average rate of quality defects of the target supervisor. The average work coverage rate, problem discovery rate or problem handling efficiency of the target supervisor. , is the number of each analysis, is the number of analyses.

[0086] As a specific example, if the correlation between the occurrence rate of project progress deviation and the work coverage rate is 0.6, it means that the work coverage rate has a greater impact on the occurrence rate of project progress deviation, and it is necessary to further strengthen the coverage of the supervisor's supervision work to reduce the deviation of the project progress.

[0087] If the correlation between the non-compliance rate of the implementation method and the work coverage rate is 0.1, it means that the work coverage rate has little impact on the non-compliance rate of the implementation method.

[0088] The database is used to store the implementation data and supervision logs of each supervisor corresponding to each construction project, store the target supervisor's corresponding construction project plan files, store the project assessment and work evaluation of each supervisor's analysis, and store the construction project images corresponding to the completion percentage of each project.

[0089] The present invention identifies the causal relationship between supervision effectiveness and engineering risks by correlating and analyzing engineering anomaly assessments such as progress deviation, construction method compliance and quality defects with work evaluations such as the supervisor's work coverage, problem discovery rate and problem handling efficiency. This helps to provide early warning of risks, such as hidden dangers in high-risk areas caused by insufficient coverage of a supervisor, promotes the closed-loop rate of problem rectification, and reduces rework costs and accident rates.

[0090] Embodiment 2

[0091] like Figure 3 As shown, in the second embodiment of the present invention, the embodiment of the present invention provides the following technical solution, a computer, including a memory 202, a processor 201, and a computer program stored in the memory 202 and executable on the processor 201, when the processor 201 executes the computer program, the method for predicting and analyzing the efficacy of shunt surgery for patients with hydrocephalus based on machine learning as described above is implemented.

[0092] Specifically, the processor 201 may include a central processing unit (CPU), or an application specific integrated circuit (ASIC), or may be configured to implement one or more integrated circuits of the embodiments of the present application.

[0093] Among them, the memory 202 may include a large-capacity memory for data or instructions. For example, but not limitation, the memory 202 may include a hard disk drive (HDD), a floppy disk drive, a solid state drive (SSD), a flash memory, an optical disk, a magneto-optical disk, a magnetic tape, or a universal serial bus (USB) drive or a combination of two or more of these. In appropriate cases, the memory 202 may include a removable or non-removable (or fixed) medium. In appropriate cases, the memory 202 may be inside or outside the data processing device. In a specific embodiment, the memory 202 is a non-volatile memory. In a specific embodiment, the memory 202 includes a read-only memory (ROM) and a random access memory (RAM). Where appropriate, the ROM may be a mask-programmed ROM, a programmable ROM (PROM), an erasable PROM (EPROM), an electrically erasable PROM (EEPROM), an electrically alterable ROM (EAROM) or a flash memory (FLASH), or a combination of two or more of these. Under appropriate circumstances, the RAM can be a static random access memory (SRAM) or a dynamic random access memory (DRAM), wherein the DRAM can be a fast page mode dynamic random access memory (FPMDRAM), an extended data output dynamic random access memory (EDODRAM), a synchronous dynamic random access memory (SDRAM), etc.

[0094] The memory 202 may be used to store or cache various data files that need to be processed and / or used for communication, as well as possible computer program instructions executed by the processor 201 .

[0095] The processor 201 reads and executes the computer program instructions stored in the memory 202 to implement the above-mentioned method for predicting and analyzing the efficacy of shunt surgery for hydrocephalus patients based on machine learning.

[0096] In some embodiments, the computer may further include a communication interface 203 and a bus 200. Figure 3 As shown, the processor 201, the memory 202, and the communication interface 203 are connected via a bus 200 and communicate with each other.

[0097] The communication interface 203 is used to implement communication between the modules, devices, units and / or equipment in the embodiment of the present application. The communication interface 203 can also implement data communication with other components such as: external devices, image / data acquisition equipment, databases, external storage, and image / data processing workstations.

[0098] The bus 200 includes hardware, software or both, and couples the components of the computer to each other. The bus 200 includes but is not limited to at least one of the following: a data bus, an address bus, a control bus, an expansion bus, and a local bus. By way of example and not limitation, bus 200 may include an Accelerated Graphics Port (AGP) or other graphics bus, an Extended Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), a Hyper Transport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an InfiniBand interconnect, a Low Pin Count (LPC) bus, a memory bus, a Micro Channel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local Bus (VLB) bus, or other suitable buses or a combination of two or more of these. Where appropriate, bus 200 may include one or more buses. Although embodiments of the present application describe and illustrate a particular bus, the present application contemplates any suitable bus or interconnect.

[0099] The above contents are merely examples and explanations of the concept of the present invention. The technicians in this technical field may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the concept of the invention or exceed the scope defined by the present invention, they should all fall within the protection scope of the present invention.

Claims

1. The construction engineering supervision information platform and management and control system based on artificial intelligence is characterized by: include: The data collection module stores the implementation data and supervision log of the current construction project collected by the target supervisor in real time in the database; The implementation data includes project progress information, actual implementation method information and project quality information; A data analysis module analyzes the engineering evaluation of the current construction project and the work evaluation of the target supervisor based on the implementation data and the supervision log, stores them in the database, and performs management and control at the same time; The data analysis module includes an engineering anomaly assessment unit and a supervision evaluation unit; the engineering anomaly assessment unit dynamically compares the implementation data with the corresponding standard implementation data to analyze the engineering progress deviation, the implementation method compliance and the quality defects; the supervision evaluation unit integrates the implementation data with the supervision log for analysis to obtain the target supervisor's work coverage, problem discovery rate and problem handling efficiency; The correlation warning module will analyze the correlation of the project assessment and work evaluation of each analysis of the target supervisor extracted from the database and provide feedback; The database stores the current construction project plan files corresponding to the target supervisor, the project assessment and work evaluation of each supervisor's analysis, and the construction project images corresponding to the completion percentage of each project.

2. The construction engineering supervision information platform and management and control system based on artificial intelligence according to claim 1 is characterized by: The project progress information includes the interval length of each implemented project node and the completion percentage of the project; The actual implementation method information includes each specific construction process in the current project progress and its corresponding technical parameters; The project quality information includes the dimensional deviation of each monitoring position, the degree of concrete surface cracks, and the proportion of steel bar corrosion.

3. The construction engineering supervision information platform and management and control system based on artificial intelligence according to claim 2 is characterized by: The specific analysis method of the project progress deviation is as follows: Extract the target supervisor's corresponding time interval between each implemented project node of the current construction project and percentage of project completion in , is the number of each implemented project node, is the number of implemented project nodes; Match the current time point with the target supervisor's corresponding project completion percentage at each time point in the current construction project plan file stored in the database to obtain the project completion percentage at the current time point ; According to the calculation formula Get the target supervisor's project progress deviation for the current construction project ,in The target supervisor extracted from the database corresponds to the duration between the project nodes in the current construction project plan file and the project nodes that have been implemented.

4. The construction engineering supervision information platform and management and control system based on artificial intelligence according to claim 3 is characterized by: The specific analysis method for the implementation method to meet the situation is as follows: Extract each specific construction technology and its corresponding technical parameters in the current project progress of the target supervisor corresponding to the current construction project, compare them with each specific construction technology and its corresponding technical parameters in the corresponding project progress in the plan file of the target supervisor corresponding to the current construction project stored in the database, screen out each specific construction technology and its corresponding technical parameters in the current project progress of the target supervisor corresponding to the current construction project that are consistent with the plan file, and record them as each compliant specific construction technology and its corresponding compliant technical parameters in the current project progress of the target supervisor corresponding to the current construction project, then obtain each non-compliant specific construction technology and its corresponding non-compliant technical parameters in the current project progress of the target supervisor corresponding to the current construction project, which are collectively referred to as the compliance of the implementation method of the target supervisor corresponding to the current construction project.

5. The construction engineering supervision information platform and management and control system based on artificial intelligence according to claim 4 is characterized by: The specific analysis method of the quality defects is as follows: The dimensional deviations, concrete surface crack levels and steel corrosion ratios of each monitoring position corresponding to the current construction project of the target supervisor are extracted, and compared with the preset allowable dimensional deviations, allowable concrete surface crack levels and allowable steel corrosion ratios of the corresponding monitoring positions, respectively, to determine the quality defects of each monitoring position, concrete surface quality defects and steel bar quality defects of the target supervisor corresponding to the current construction project, which are collectively referred to as the quality defects of the target supervisor corresponding to the current construction project.

6. The construction engineering supervision information platform and management and control system based on artificial intelligence according to claim 1 is characterized by: The specific analysis method of the target supervisor's work coverage is as follows: The spatial coverage and temporal coverage are extracted from the supervision log of the target supervisor, and the work coverage of the target supervisor is obtained by summing them up according to the corresponding weights.

7. The construction engineering supervision information platform and management and control system based on artificial intelligence according to claim 1 is characterized by: The specific analysis method of the problem discovery rate of the target supervisor is as follows: The number of problems found in the target supervisor's current construction project is extracted from the target supervisor's supervision log, and it is compared with the total number of problems in the current construction project. The quotient obtained is the problem discovery rate of the target supervisor.

8. The construction engineering supervision information platform and management and control system based on artificial intelligence according to claim 1 is characterized by: The specific analysis method of the target supervisor's problem handling efficiency is as follows: The average processing time, the proportion of overtime problems and the problem recurrence rate are extracted from the supervision log of the target supervisor and recorded as , find out the problem-solving efficiency of the target supervisor ,in is the preset average processing time threshold, To prevent the denominator from being a minimum value of 0, the unit is hours. They are the corresponding weight coefficients of the average processing time, the proportion of timeout problems and the problem recurrence rate obtained through experimental fitting.

9. The construction engineering supervision information platform and management and control system based on artificial intelligence according to claim 5 is characterized by: The specific operations for controlling the current construction project's engineering assessment and the target supervisor's work evaluation include: Extract the engineering evaluation status of the target supervisor corresponding to the current construction project; If the deviation of the project progress of the target supervisor corresponding to the current construction project is greater than or equal to the deviation threshold, further analyze the cause and provide feedback; Extract the specific construction processes and corresponding non-conforming technical parameters in the current construction progress of the target supervisor's corresponding current construction project from the target supervisor's corresponding current construction project implementation method compliance situation, and further obtain the reasons for the non-conformity; Provide feedback to the target supervisor on the quality defects of the current construction project and make corrections based on the specific causes of the quality defects; Extract the work evaluation of the target supervisor; If the target supervisor's work coverage is less than the preset work coverage threshold, the target supervisor is reminded to increase the supervision coverage; If the problem discovery rate of the target supervisor is lower than the preset problem discovery rate threshold, the target supervisor will be reminded to strengthen quality inspection, and the supervision platform manager will increase the quality inspection learning indicators of the target supervisor; If the target supervisor's problem handling efficiency is lower than the preset problem handling efficiency, the target supervisor is reminded to increase the problem handling speed. At the same time, the supervision platform manager increases the target supervisor's problem handling learning indicators.

10. The construction engineering supervision information platform and management and control system based on artificial intelligence according to claim 1 is characterized by: The specific analysis content of the association warning module is: Extract from the database the project progress deviation rate, implementation method non-conformity rate and quality defect rate in the project evaluation of each analysis of the target supervisor, as well as the work coverage rate, problem discovery rate and problem handling efficiency in the work evaluation; According to the Pearson correlation coefficient calculation formula The correlation between the target supervisor's engineering evaluation and work evaluation is obtained, where The target supervisor belongs to The project progress deviation rate, the non-compliance rate of the implementation method, or the quality defect rate analyzed in this analysis, The target supervisor belongs to The work coverage rate or problem discovery rate or problem handling efficiency of the analysis, The average rate of deviation in the project progress, the average rate of non-compliance in the implementation method, or the average rate of quality defects of the target supervisor. The average work coverage rate, problem discovery rate or problem handling efficiency of the target supervisor. , is the number of each analysis, is the number of analyses.

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