Progress optimization analysis method based on rail transit engineering quantity and performance database

Abstract

The invention provides a progress optimization analysis method based on a rail transit engineering quantity and performance database, and belongs to the technical field of data processing, and the method comprises the steps: 1, building a data collection system, and collecting the key data of a construction site in real time; 2, preprocessing the real-time key data of the construction site, and storing the preprocessed real-time key data in a database based on a preset method; 3, performing project progress management based on the real-time key data and historical data stored in the database; 4, analyzing the real-time key data stored in the rail transit engineering quantity and performance database and the stored historical data, determining a progress evaluation value, and performing resource optimization configuration based on the progress evaluation value and the real-time key data; and 5, generating a visual chart based on the progress evaluation value, the progress management effect of the project and the resource optimization configuration effect. And the accuracy, predictability and resource allocation efficiency of progress management are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a progress optimization analysis method based on a rail transit engineering quantity and performance database. Background Art

[0002] Rail transit projects are characterized by large scale, long cycle, and complex links, and project progress management is a key link. Traditional progress management methods mainly rely on manual statistics and scattered monitoring tools, which have problems such as low efficiency, data lag, and difficulty in overall control.

[0003] In the existing technology, some solutions have introduced engineering quantity databases and progress analysis tools to achieve auxiliary functions of project management through simple data comparison. However, these methods still have shortcomings in real-time data collection, dynamic analysis and intelligent resource allocation, and cannot effectively respond to the multi-variable and multi-level data processing needs in rail transit projects. In addition, the existing methods lack intuitive visualization means, which limits managers' rapid understanding and decision-making ability of the project status. The data collection and processing methods are lagging behind, and there is a lack of dynamic monitoring and integration of real-time key data on the construction site. The database structure is single and cannot fully combine historical data and real-time data for in-depth analysis. It lacks early warning and resource optimization functions, making it difficult to quickly respond to project progress deviations, and it is impossible to present analysis results in a visual way, which affects decision-making efficiency.

[0004] Therefore, the present invention provides a schedule optimization analysis method based on rail transit engineering quantity and performance database. Summary of the invention

[0005] The present invention provides a progress optimization analysis method based on a rail transit engineering quantity and performance database, which is used to realize intelligent progress management and early warning optimization by building a rail transit engineering quantity and performance database, combining real-time data collection, preprocessing and historical data analysis, linking progress evaluation with resource allocation, generating visual charts, and improving the accuracy, predictability and resource allocation efficiency of progress management. The present invention provides a schedule optimization analysis method based on a rail transit engineering quantity and performance database, comprising: Step 1: Based on the requirements of the preset rail transit project, build a data collection system and collect key data from the construction site in real time; Step 2: Preprocess the real-time key data of the construction site, and store the preprocessed real-time key data in the rail transit engineering quantity and performance database based on a preset method; Step 3: Manage the project progress based on the real-time key data stored in the rail transit engineering quantity and performance database and the stored historical data; Step 4: Analyze the real-time key data and stored historical data stored in the rail transit engineering quantity and performance database, and determine the progress evaluation value, and then optimize resource allocation based on the progress evaluation value and real-time key data; Step 5: Generate a visualization chart based on the progress evaluation value, the project progress management effect, and the resource optimization configuration effect.

[0006] The present invention provides a progress optimization analysis method based on a rail transit engineering quantity and performance database, and progress management, including: progress evaluation, progress optimization and progress early warning.

[0007] The present invention provides a progress optimization analysis method based on a rail transit engineering quantity and performance database. Based on the requirements of a preset rail transit engineering project, a data acquisition system is built to collect key data of the construction site in real time, including: Determine the key data that needs to be collected during the construction process based on the requirements of the preset rail transit project; Determine the engineering quantity and performance requirements based on the preset rail transit project needs; Determine the method of collecting key data based on engineering volume and performance requirements; Determine a number of preset types of data acquisition devices based on the key data to be collected; Based on all preset types of collection devices, key data of the corresponding construction site are collected in real time.

[0008] The present invention provides a progress optimization analysis method based on a rail transit engineering quantity and performance database, preprocesses the real-time key data of the construction site, and stores the preprocessed real-time key data in the rail transit engineering quantity and performance database based on a preset method, including: Conduct initial analysis on the real-time key data collected at the construction site and divide the real-time key data into several categories; Based on the preset category-processing method data table, a preprocessing method for real-time key data of each category is obtained, and the real-time key data of each category is preprocessed; A data table is created for each category of real-time key data based on the preset table structure, and then all real-time key data are stored in the rail transit engineering quantity and performance database using data tables.

[0009] The present invention provides a progress optimization analysis method based on a rail transit engineering quantity and performance database, and performs project progress management based on real-time key data stored in the rail transit engineering quantity and performance database and stored historical data, including: Obtain the planning cycle of the rail transit engineering project, and split the planning cycle into several sub-cycles based on a preset splitting method; Determine the real-time engineering quantity for each sub-period based on the real-time key data stored in the rail transit engineering quantity and performance database; Determine the real-time quantities and deviations from the planned quantities for each sub-period; Determine the labor performance of each sub-period based on the real-time engineering quantity, real-time construction personnel and construction duration of each sub-period; Based on the manual performance of each segmented period and the preset deviation-performance-scheme database, the staffing optimization plan is obtained, and then the staffing optimization is performed; The preliminary progress warning is determined based on the real-time engineering quantity of each sub-period, the deviation from the planned engineering quantity, and the manual performance of each sub-period.

[0010] The present invention provides a progress optimization analysis method based on a rail transit engineering quantity and performance database, and performs project progress management based on real-time key data stored in the rail transit engineering quantity and performance database and stored historical data, including: Determine the first warning information based on the real-time engineering quantity of each subdivided period, the deviation of the planned engineering quantity, the deviation of the project progress, and the preset deviation threshold; Analyze the labor performance of each sub-period to determine the performance of each work area, each team and each construction worker in each sub-period; Determine the second warning information based on the preset work area performance threshold, team performance threshold, and individual construction worker performance threshold and the performance of each work area, each team, and each construction worker in each subdivided period; A preliminary progress warning situation is determined based on the first warning information and the second warning information.

[0011] The present invention provides a progress optimization analysis method based on a rail transit engineering quantity and performance database, and preliminary progress warning situations, including: minor warning and severe warning.

[0012] The present invention provides a progress optimization analysis method based on a rail transit engineering quantity and performance database, analyzes real-time key data and stored historical data stored in the rail transit engineering quantity and performance database, and determines a progress evaluation value, including: Analyze the real-time key data and stored historical data stored in the rail transit engineering quantity and performance database, and determine the progress evaluation value: ; in, is the progress evaluation value, is the actual engineering quantity, The preset planned engineering quantity, is the actual number of manpower, is the preset planned manpower number, For actual manual performance, For preset planned manual performance, is the progress deviation influencing factor, is the manual performance influence coefficient, is the actual output value completed, is the preset planned output value, is the influencing factor of output value deviation, is the actual resource consumption, is the planned resource consumption, is the resource consumption deviation factor, is the project progress weight, is the resource consumption weight, is the historical performance impact coefficient, is the average of historical performance; The warning degree is determined based on the progress evaluation value and a preset progress evaluation threshold, and the progress evaluation value is determined based on a preset degree-information database.

[0013] The present invention provides a progress optimization analysis method based on rail transit engineering quantity and performance database, and performs resource optimization configuration based on progress evaluation value and real-time key data, including: The optimization formula for determining the number of construction personnel based on progress evaluation value and real-time key data is as follows: ;in, The number of construction workers after optimization: is the actual engineering quantity, The preset planned engineering quantity, is the preset total amount of work, D0 is the planned completion date, and D1 is the date of completion of the actual amount of work. is the preset maximum number of construction personnel, is the influence coefficient of the engineering quantity, is the influence coefficient of time, is the influence coefficient of the number of construction personnel, is the number of construction workers required for each unit of work, To round up; Determine a resource consumption optimization plan based on the progress evaluation value and a preset value-plan database; Optimal configuration is performed based on the optimized number of construction personnel and resource consumption optimization plan.

[0014] Compared with the prior art, the present invention has the following beneficial effects: By building a rail transit engineering quantity and performance database, combining real-time data collection, preprocessing and historical data analysis, we can achieve intelligent progress management and early warning optimization, link progress evaluation with resource allocation, generate visual charts, and improve the accuracy, predictability and resource allocation efficiency of progress management. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0016] Figure 1 It is a flow chart of a schedule optimization analysis method based on a rail transit engineering quantity and performance database provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0017] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are 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.

[0018] Embodiment 1: The embodiment of the present invention provides a progress optimization analysis method based on a rail transit engineering quantity and performance database, such as Figure 1 As shown, including: Step 1: Based on the requirements of the preset rail transit project, build a data collection system and collect key data from the construction site in real time; Step 2: Preprocess the real-time key data of the construction site, and store the preprocessed real-time key data in the rail transit engineering quantity and performance database based on a preset method; Step 3: Manage the project progress based on the real-time key data stored in the rail transit engineering quantity and performance database and the stored historical data; Step 4: Analyze the real-time key data and stored historical data stored in the rail transit engineering quantity and performance database, and determine the progress evaluation value, and then optimize resource allocation based on the progress evaluation value and real-time key data; Step 5: Generate a visualization chart based on the progress evaluation value, the project progress management effect, and the resource optimization configuration effect.

[0019] In this embodiment, the requirements of the rail transit project are the goals and standards determined when the project is started, which are used to guide the construction process and data management, including: project cycle, construction phase division, resource allocation plan, key node goals (such as opening time or segmented construction completion time), quality standards and risk control requirements. For example: for a subway line construction project, the requirements may include: completing the excavation of 10 kilometers of tunnels within 6 months; using smart equipment to improve excavation efficiency by 20%; controlling daily man-hours within 2,000 hours; and preventing the noise in the construction area from exceeding the specified upper limit; In this embodiment, key data refers to information that has a significant impact on the progress, quality, and resource use of the project. According to the needs of the project, these data need to be collected through sensors, monitoring equipment, or manual methods, and updated in real time in digital form. For example: progress data: the length of tunnels excavated daily; equipment data: the operating time and fault records of the drilling rig; environmental data: the air quality, temperature, and humidity in the tunnel excavation area; safety data: displacement changes monitored by tunnel stability sensors.

[0020] In this embodiment, the rail transit engineering quantity and performance database is an information platform that integrates real-time data storage, historical data management and data analysis functions. It can support the rapid retrieval, analysis and comparison of multi-dimensional data, and provide a reliable basis for construction progress and performance management. For example, the engineering quantity data stored in the database: the length of completed tunnels, the number of kilometers of laid tracks, etc.; performance data: the difference between the actual construction period and the planned construction period, daily equipment efficiency (such as the amount of excavation per unit time). Through comprehensive analysis of historical and real-time data, the database can generate a phased report on the construction, for example, pointing out that the equipment utilization rate in a certain area is low and making adjustment suggestions.

[0021] In this embodiment, resource optimization is a method to adjust resource allocation according to the real-time status and early warning information of the project to maximize efficiency and reduce costs. It involves the rescheduling of resources such as construction equipment, materials, and manpower. For example, if the construction progress of a certain area lags behind, the system will analyze and recommend: deploy two idle excavators to work in this area; reduce the input of personnel in other areas and transfer more workers to this area to speed up construction; In this embodiment, visualization charts are the process of converting complex engineering data into intuitive graphs, curves or tables, which facilitate managers to quickly understand and make decisions. For example: Progress Gantt chart: displays the time schedule of different construction stages and marks delayed areas in real time; Resource usage bar chart: compares the equipment and personnel usage of each work area, highlighting idle or overloaded situations; Line trend chart: displays the changes in daily excavation progress and compares it with the target progress line; Heat map: highlights areas with problems or resource shortages in construction. For example, in a subway tunnel construction, the chart can dynamically display: the equipment utilization rate of a certain work area has dropped to 70%, prompting managers to check the problem.

[0022] The beneficial effects of the above technical solution are: by building a rail transit engineering quantity and performance database, combining real-time data collection, preprocessing and historical data analysis, the intelligentization of progress management and early warning optimization is realized, the progress evaluation is linked with resource allocation, and visual charts are generated, which improves the accuracy, predictability and resource allocation efficiency of progress management.

[0023] Embodiment 2: The embodiment of the present invention provides a progress optimization analysis method based on a rail transit engineering quantity and performance database, and progress management, including: progress evaluation, progress optimization and progress early warning.

[0024] In this embodiment, the progress assessment is to comprehensively evaluate the current construction progress of the project by analyzing the key construction data and historical data collected in real time, compare the difference between the actual progress and the planned progress, and judge whether the construction is progressing as planned. The evaluation results are presented in quantitative indicators, such as the completion rate of the construction period, the deviation amount and the degree of impact, etc. In this embodiment, schedule optimization is based on the results of schedule evaluation, and optimization plans are proposed for construction tasks with deviations. Delays are reduced and efficiency is improved by adjusting resource allocation or construction plans. The optimization process usually combines historical data and real-time status in the database, and uses intelligent algorithms or rule systems to recommend resource scheduling plans.

[0025] In this embodiment, the progress warning is based on real-time data and historical trend analysis to give advance warnings of possible progress deviations and help managers take preventive measures. The warning mechanism is usually divided into three levels: green (normal), yellow (slight deviation) and red (serious lag), and is presented through visualization tools. For example: Track laying warning: A track laying plan is to complete 500 meters per day, but the real-time data from the database shows that only 400 meters have been completed on average in the past three days, with a completion rate of 80%. The system triggers a yellow warning and prompts possible reasons: increased equipment maintenance time or untimely material supply. Suggested measures: increase the number of track laying workers or adjust the equipment maintenance plan. Overall project warning: The overall completion of the project needs to reach 50%, but the evaluation data shows that the actual completion rate is only 45%, and the unfinished work is mostly concentrated on key lines (such as tunnel excavation). The system triggers a red warning, prompting: If the resource allocation is not adjusted, the overall construction period is expected to be delayed by 5 days. Suggested measures: speed up the tilt of key line resources and delay non-critical construction tasks in secondary areas.

[0026] The beneficial effects of the above technical solution are: by integrating the three major links of progress evaluation, optimization and early warning, it realizes all-round dynamic management of the progress of rail transit projects, identifies progress deviations in real time and makes optimization adjustments, and warns of potential risks in advance, effectively improving the project's execution efficiency and risk control capabilities, and ensuring that the project proceeds smoothly as scheduled.

[0027] Embodiment 3: The embodiment of the present invention provides a progress optimization analysis method based on a rail transit engineering quantity and performance database. Based on the requirements of a preset rail transit engineering project, a data collection system is built to collect key data of the construction site in real time, including: Determine the key data that needs to be collected during the construction process based on the requirements of the preset rail transit project; Determine the engineering quantity and performance requirements based on the preset rail transit project needs; Determine the method of collecting key data based on engineering volume and performance requirements; Determine a number of preset types of data acquisition devices based on the key data to be collected; Based on all preset types of collection devices, key data of the corresponding construction site are collected in real time.

[0028] In this example, the engineering quantity and performance requirements are the core indicators in rail transit project management, which are used to define the workload and execution standards of construction tasks. The engineering quantity is usually expressed in physical measurements, such as tunnel length, track laying mileage, etc.; performance requirements include efficiency, quality, safety and other goals, such as construction progress per unit time, equipment utilization rate and failure rate, etc. Example: Engineering quantity: The tunnel excavation task needs to be completed at 20 kilometers, which will be achieved in stages at 2 kilometers per month; a section of track needs to be laid at 1,000 meters, with a daily target of 50 meters; the number of support installations is 10 groups per 100 meters. Performance requirements: daily excavation efficiency ≥ 40 meters; failure rate ≤ 5 times / day; environmental noise ≤ 70 decibels; construction period deviation is controlled within ±5%.

[0029] In this embodiment, the key data collection method is determined based on the engineering quantity and performance requirements. In order to meet the engineering quantity and performance requirements, an efficient data collection method must be designed to ensure the acquisition of key data. Collection methods include sensor collection, real-time recording by automated equipment, and manual regular input. The specific method is determined by the data type, accuracy requirements, and collection environment. For example: Tunnel excavation: Use laser ranging sensors to collect daily excavation progress; obtain the working hours of the drilling rig through equipment operation records; set up a vibration monitoring device to collect the stability data of the support structure in real time. Track laying: Install GPS positioning equipment to track track laying progress; use automatic quality inspection instruments to collect track flatness and spacing deviation data; arrange on-site staff to regularly input manual adjustment data (such as manual laying deviation correction values), environmental monitoring: deploy noise sensors to collect environmental noise in the construction area; use air quality detection equipment to collect dust concentration.

[0030] In this embodiment, the preset type data acquisition device is to deploy several preset types of data acquisition devices according to different key data types and acquisition requirements. These devices include automated monitoring equipment, sensor networks, and mobile handheld devices, etc., which are suitable for different construction scenarios and data requirements. For example: Type 1: Progress acquisition device laser ranging sensor, used to monitor the tunnel excavation length in real time; high-precision GPS equipment, monitoring the track laying position and path completion. Type 2: Equipment status acquisition device equipment sensor, used to record mechanical operating time, speed and power; fault detection equipment (such as vibration sensor), real-time collection of equipment abnormality information.

[0031] The beneficial effect of the above technical solution is: by combining project requirements with precise engineering quantities and performance requirements, a flexible data collection system is built, and different types of collection devices are intelligently selected and deployed according to the specific needs of the construction site to achieve real-time and accurate capture of key data. Its beneficial effect is that it provides comprehensive and dynamic data support for rail transit projects, which helps to refine progress management and optimize decision-making.

[0032] Embodiment 4: The embodiment of the present invention provides a progress optimization analysis method based on a rail transit engineering quantity and performance database, preprocesses the real-time key data of the construction site, and stores the preprocessed real-time key data in the rail transit engineering quantity and performance database based on a preset method, including: Conduct initial analysis on the real-time key data collected at the construction site and divide the real-time key data into several categories; Based on the preset category-processing method data table, a preprocessing method for real-time key data of each category is obtained, and the real-time key data of each category is preprocessed; A data table is created for each category of real-time key data based on the preset table structure, and then all real-time key data are stored in the rail transit engineering quantity and performance database using data tables.

[0033] In this embodiment, initial analysis is the first step in data preprocessing, which aims to classify and manage the complex data collected at the construction site. By analyzing the source, content and purpose of the data, the real-time key data is divided into several categories, making the subsequent processing more efficient and accurate. The classification standards are usually based on data type (numeric, text), purpose (progress, quality, safety), source (equipment sensor, manual input), etc. For example: In rail transit construction, the initial analysis may divide the real-time key data into the following categories: progress data: daily progress of tunnel excavation, track laying length; equipment status data: mechanical operation time, fault records; environmental data: temperature, humidity and air quality in the construction area; safety data: stability monitoring of the support structure, the status of the equipment worn by the workers (such as whether the sensor is working properly). For example, the data sent back by the temperature sensor and humidity sensor are classified as the environment category, and the mechanical fault alarm data are classified as the equipment status category; In this embodiment, the preset category-processing method data table defines the preprocessing method corresponding to each data category. These methods include data cleaning (removing noise data), formatting (timestamp standardization), conversion (unit conversion) or compression (reducing redundancy). The table serves as a set of rules to guide the preprocessing process.

[0034] In this embodiment, the preset table structure is a template for storing different types of data in the rail transit engineering quantity and performance database. The table structure of each category is designed according to the data characteristics, including field name, data type, index, etc. The unified table structure ensures standardized storage and efficient retrieval of data. Taking the track laying progress data table as an example, its preset structure is as follows:

[0035] The beneficial effects of the above technical solution are: through the intelligent classification and preprocessing of real-time key data at the construction site, combined with the preset categorized storage structure, an efficient and standardized database system is built, which improves the standardization and accuracy of data storage and management, and provides a reliable data foundation for real-time analysis and subsequent optimization of rail transit projects.

[0036] Embodiment 5: The embodiment of the present invention provides a progress optimization analysis method based on a rail transit engineering quantity and performance database, and performs project progress management based on real-time key data and stored historical data stored in the rail transit engineering quantity and performance database, including: Obtain the planning cycle of the rail transit engineering project, and split the planning cycle into several sub-cycles based on a preset splitting method; Determine the real-time engineering quantity for each sub-period based on the real-time key data stored in the rail transit engineering quantity and performance database; Determine the real-time quantities and deviations from the planned quantities for each sub-period; Determine the labor performance of each sub-period based on the real-time engineering quantity, real-time construction personnel and construction duration of each sub-period; Based on the manual performance of each segmented period and the preset deviation-performance-scheme database, the staffing optimization plan is obtained, and then the staffing optimization is performed; The preliminary progress warning is determined based on the real-time engineering quantity of each sub-period, the deviation from the planned engineering quantity, and the manual performance of each sub-period.

[0037] In this embodiment, the planning cycle refers to a pre-set period of time during the planning and execution of a rail transit project, which is used to define the overall duration and main phase goals of the project. In this embodiment, the planning cycle is the basic data for analyzing the progress. By splitting it into several sub-cycles, more refined management can be performed on different phases of the project. For example, if the planning cycle of a rail transit project is one year (12 months), it can be split into quarterly, monthly or shorter cycles according to the needs of the project; In this embodiment, the preset splitting method refers to a rule or strategy that is set in advance according to the characteristics and needs of the rail transit project and is used to decompose the planning cycle into multiple sub-cycles. Common splitting bases: by natural time (such as weeks, months, quarters); by construction stage (such as civil engineering, track laying, electromechanical installation); by key processes (such as tunnel excavation, shield advancement, track laying). For example: If the planning cycle is one year, the preset splitting method may be to split it into 12 sub-cycles by month, or to divide it into several construction stages by project stage, and each stage corresponds to a sub-cycle; In this embodiment, the subdivision cycle refers to a small time unit or stage decomposed from the planning cycle according to a preset splitting method, which facilitates more specific management and analysis of the progress of the rail transit engineering project. In each subdivision cycle, the engineering volume, performance, etc. will be analyzed in detail through real-time key data and historical data. For example: If the planning cycle is one year and is split into months, each month is a subdivision cycle. In a certain subdivision cycle (such as the third month), it may be necessary to analyze the difference between the actual tunnel excavation volume and the planned engineering volume, as well as the performance of labor efficiency.

[0038] The beneficial effects of the above technical solution are: by combining real-time data of subdivided periods with historical data, dynamic monitoring and evaluation of engineering volume and labor performance can be achieved, and personnel allocation can be optimized based on the deviation-performance database, progress warnings can be generated, and the accuracy and flexibility of rail transit project management can be improved, problems can be discovered and solved in advance, and the smooth progress of the project can be ensured.

[0039] Embodiment 6: The embodiment of the present invention provides a progress optimization analysis method based on a rail transit engineering quantity and performance database, and performs project progress management based on real-time key data and stored historical data stored in the rail transit engineering quantity and performance database, including: Determine the first warning information based on the real-time engineering quantity of each subdivided period, the deviation of the planned engineering quantity, the deviation of the project progress, and the preset deviation threshold; Analyze the labor performance of each sub-period to determine the performance of each work area, each team and each construction worker in each sub-period; Determine the second warning information based on the preset work area performance threshold, team performance threshold, and individual construction worker performance threshold and the performance of each work area, each team, and each construction worker in each subdivided period; A preliminary progress warning situation is determined based on the first warning information and the second warning information.

[0040] In this embodiment, the first warning information is generated based on the deviation between the real-time engineering quantity and the planned engineering quantity of the rail transit project in each subdivided period, combined with a preset deviation threshold. The generation of the first warning information is mainly used to identify the risk of project progress deviation and to adjust the progress plan or resource allocation in a timely manner. For example, the first warning information content: Warning level: high. Problem description: In the second week, the actual excavation work completed was 30 cubic meters, and the planned completion was 50 cubic meters. The deviation rate was -40%, which exceeded the allowable range. Suggestion: Increase equipment investment or extend the construction time to catch up with the progress.

[0041] In this embodiment, the second warning information is generated based on the analysis results of manual performance in each subdivided period, and is mainly used to identify progress risks caused by insufficient work efficiency of personnel. The second warning information can further refine the root cause of the problem, such as identifying low-performance teams or individual personnel, to facilitate targeted improvements. For example, the second warning information content: Work area warning: The overall performance of the work area is 70%, which is lower than the threshold of 80%. Team warning: Team B’s performance is 65%, which is lower than the threshold of 75%. Personnel warning: Construction worker Zhang’s performance is 60%, which is lower than the threshold of 70%. Recommendation: Conduct performance analysis on Team B, focus on training Zhang, or adjust his work arrangements.

[0042] In this embodiment, after combining the first warning information (based on the deviation of the engineering quantity) and the second warning information (based on the performance of human resources), a comprehensive preliminary progress warning can be formed to clarify the specific reasons for the progress deviation. For example: the deviation of the mining workload in the second week is 40% (first warning information). The main reasons are: the performance of team B is low (65%), and Zhang’s personal efficiency is low (60%) (second warning information). Suggestion: Optimize the work arrangement of team B, improve the performance of inefficient personnel, and adjust resources to catch up with the workload progress.

[0043] The beneficial effects of the above technical solution are: by integrating engineering quantity deviation analysis and multi-level manual performance evaluation, a multi-dimensional early warning system is constructed that is refined to the work area, team and individual. Based on the dual early warning information, the progress risk source is accurately identified and located, the accuracy and response efficiency of rail transit project management are improved, and dynamic monitoring and hierarchical intervention of progress problems are realized.

[0044] Embodiment 7: The embodiment of the present invention provides a progress optimization analysis method based on a rail transit engineering quantity and performance database, and preliminary progress warning situations, including: minor warning and severe warning.

[0045] In this embodiment, a minor warning means that the progress deviation is small and still within the controllable range, but if it is not paid attention to and adjusted in time, it may gradually accumulate and have an adverse impact on the subsequent construction period. The triggering conditions are: The deviation between the real-time engineering volume and the planned engineering volume is less than 5%; the decline in manual performance is within 10%; the delay in the completion of certain processes does not exceed 10% of the scheduled time. Countermeasures: Make partial adjustments to the configuration of construction personnel; reallocate resources (such as equipment or materials); make slight adjustments to the construction plan to compensate for deviations. For example: In the third sub-cycle (such as the third month) of a rail transit project, it is planned to excavate 500 meters of tunnels, but the actual completion volume is 480 meters, a deviation of 4%. At the same time, manual performance is slightly below the standard, but it does not affect the construction period of major nodes. At this time, the system triggers a minor warning, prompting the project manager to optimize the allocation of personnel and resources for the next sub-cycle; In this embodiment, a serious warning means that the progress deviation is large, which may pose a great risk to the overall project progress. Corrective measures must be taken immediately, otherwise the planned cycle may not be completed on time. Trigger conditions: the deviation between the real-time project volume and the planned project volume is greater than 10%; the decline in labor performance exceeds 20%; the process delay time exceeds the scheduled time by more than 15%; the cumulative deviation has a substantial impact on the subsequent key construction period. Countermeasures: Emergency adjustment of personnel, equipment, and material configuration; Modification of construction organization design and schedule plan; Introducing overtime or external resource support to rush work.

[0046] For example, in the sixth sub-cycle (e.g., the sixth month), the plan was to lay 1,000 meters of track, but only 850 meters were actually completed, with a deviation of 15%. At the same time, the construction time was extended by 30% due to efficiency issues. The system triggered a serious warning, requiring immediate additional personnel, accelerated equipment maintenance, and adjustment of the construction plan for the seventh month to remedy the situation.

[0047] The beneficial effects of the above technical solution are: by setting up a two-level mechanism of minor warning and severe warning, and using real-time data and historical data to accurately identify progress deviations, hierarchical management and early intervention of potential risks are achieved, avoiding the accumulation of progress problems, optimizing resource allocation, and improving the stability and reliability of project execution, providing efficient and intelligent early warning support for rail transit projects.

[0048] Embodiment 8: The embodiment of the present invention provides a progress optimization analysis method based on a rail transit engineering quantity and performance database, which analyzes real-time key data and stored historical data stored in the rail transit engineering quantity and performance database and determines a progress evaluation value, including: Analyze the real-time key data and stored historical data stored in the rail transit engineering quantity and performance database, and determine the progress evaluation value: ; in, is the progress evaluation value, is the actual engineering quantity, The preset planned engineering quantity, is the actual number of manpower, is the preset planned manpower number, For actual manual performance, For the preset planned manual performance, is the progress deviation influencing factor, is the manual performance influence coefficient, is the actual output value completed, is the preset planned output value, is the influencing factor of output value deviation, is the actual resource consumption, is the planned resource consumption, is the resource consumption deviation factor, is the project progress weight, is the resource consumption weight, is the historical performance impact coefficient, is the average of historical performance; The warning degree is determined based on the progress evaluation value and a preset progress evaluation threshold, and the progress evaluation value is determined based on a preset degree-information database.

[0049] In this embodiment, the progress evaluation value is a comprehensive indicator for measuring the progress of the rail transit project, which is based on multiple factors such as engineering volume, labor performance, resource consumption, and historical performance, and is used to intuitively evaluate the current progress status of the project; In this embodiment, the progress deviation impact factor is used to measure the impact of the deviation between the actual engineering quantity and the planned engineering quantity on the overall progress; In this embodiment, the manual performance impact coefficient is used to measure the impact of the difference between the actual manual performance and the preset manual performance on the progress; In this embodiment, the resource consumption deviation factor is used to measure the impact of the deviation between actual resource consumption and planned resource consumption on the progress; In this embodiment, the historical performance impact coefficient is used to comprehensively evaluate the reference value of historical performance to the current progress, reflecting the weight of historical data.

[0050] The beneficial effects of the above technical solution are: by comprehensively analyzing the real-time key data and historical data of rail transit projects, a multi-factor progress evaluation model is constructed, and related factors such as engineering volume, labor performance, output value and resource consumption are included in the quantitative evaluation. In combination with preset thresholds, early warning information is accurately generated, and the dynamic database and historical performance optimization are used to achieve intelligent prediction and graded early warning of progress risks, thereby improving the refinement, foresight and decision-making efficiency of rail transit project management.

[0051] Embodiment 9: The embodiment of the present invention provides a progress optimization analysis method based on a rail transit engineering quantity and performance database, and performs resource optimization configuration based on progress evaluation values ​​and real-time key data, including: The optimization formula for determining the number of construction personnel based on progress evaluation value and real-time key data is as follows: ; in, The number of construction workers after optimization: is the actual engineering quantity, The preset planned engineering quantity, is the preset total amount of work, D0 is the planned completion date, and D1 is the date of completion of the actual amount of work. is the preset maximum number of construction personnel, is the influence coefficient of the engineering quantity, is the influence coefficient of time, is the influence coefficient of the number of construction personnel, is the number of construction workers required for each unit of work, To round up; Determine a resource consumption optimization plan based on the progress evaluation value and a preset value-plan database; Optimal configuration is performed based on the optimized number of construction personnel and resource consumption optimization plan.

[0052] The beneficial effects of the above technical solution are: through the optimization formula based on progress evaluation value and real-time key data, the number of construction personnel is dynamically adjusted, and the resource consumption configuration is optimized in combination with the value-scheme database, thereby improving the scientificity and rationality of resource allocation, ensuring that the project progress is carried out as planned, and significantly improving construction efficiency and resource utilization.

[0053] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A schedule optimization analysis method based on rail transit engineering quantity and performance database, characterized in that: include: Step 1: Based on the requirements of the preset rail transit project, build a data collection system and collect key data from the construction site in real time; Step 2: Preprocess the real-time key data of the construction site, and store the preprocessed real-time key data in the rail transit engineering quantity and performance database based on a preset method; Step 3: Manage the project progress based on the real-time key data stored in the rail transit engineering quantity and performance database and the stored historical data; Step 4: Analyze the real-time key data and stored historical data stored in the rail transit engineering quantity and performance database, and determine the progress evaluation value, and then optimize resource allocation based on the progress evaluation value and real-time key data; Step 5: Generate a visualization chart based on the progress evaluation value, the project progress management effect, and the resource optimization configuration effect.

2. The method for optimizing the progress of railway transportation engineering quantity and performance database according to claim 1 is characterized in that: Progress management, including: progress evaluation, progress optimization and progress warning.

3. The method for optimizing the progress of railway transportation engineering quantity and performance database according to claim 1 is characterized in that: Based on the requirements of the preset rail transit project, a data collection system is built to collect key data of the construction site in real time, including: Determine the key data that needs to be collected during the construction process based on the requirements of the preset rail transit project; Determine the engineering quantity and performance requirements based on the preset rail transit project needs; Determine the method of collecting key data based on engineering volume and performance requirements; Determine a number of preset types of data acquisition devices based on the key data to be collected; Based on all preset types of collection devices, key data of the corresponding construction site are collected in real time.

4. The method for optimizing the progress of railway transportation engineering quantity and performance database according to claim 1 is characterized in that: Preprocess the real-time key data of the construction site and store the preprocessed real-time key data in the rail transit engineering quantity and performance database based on the preset method, including: Conduct initial analysis on the real-time key data collected at the construction site and divide the real-time key data into several categories; Based on the preset category-processing method data table, a preprocessing method for real-time key data of each category is obtained, and the real-time key data of each category is preprocessed; A data table is created for each category of real-time key data based on the preset table structure, and then all real-time key data are stored in the rail transit engineering quantity and performance database using data tables.

5. The method for optimizing the progress of railway transportation engineering quantity and performance database according to claim 1 is characterized in that: Project progress management is performed based on real-time key data stored in the rail transit engineering quantity and performance database as well as stored historical data, including: Obtain the planning cycle of the rail transit engineering project, and split the planning cycle into several sub-cycles based on a preset splitting method; Determine the real-time engineering quantity for each sub-period based on the real-time key data stored in the rail transit engineering quantity and performance database; Determine the real-time quantities and deviations from the planned quantities for each sub-period; Determine the labor performance of each sub-period based on the real-time engineering quantity, real-time construction personnel and construction duration of each sub-period; Based on the manual performance of each segmented period and the preset deviation-performance-scheme database, the staffing optimization plan is obtained, and then the staffing optimization is performed; The preliminary progress warning is determined based on the real-time engineering quantity of each sub-period, the deviation from the planned engineering quantity, and the manual performance of each sub-period.

6. The method for optimizing the progress of railway transportation engineering quantity and performance database according to claim 5 is characterized in that: Project progress management is performed based on real-time key data stored in the rail transit engineering quantity and performance database as well as stored historical data, including: Determine the first warning information based on the real-time engineering quantity of each subdivided period, the deviation of the planned engineering quantity, the deviation of the project progress, and the preset deviation threshold; Analyze the labor performance of each sub-period to determine the performance of each work area, each team and each construction worker in each sub-period; Determine the second warning information based on the preset work area performance threshold, team performance threshold, and individual construction worker performance threshold and the performance of each work area, each team, and each construction worker in each subdivided period; A preliminary progress warning situation is determined based on the first warning information and the second warning information.

7. The method for optimizing the progress of railway transportation engineering quantity and performance database according to claim 6 is characterized in that: Preliminary progress warning situations include: minor warning and serious warning.

8. The method for optimizing the progress of railway transportation engineering quantity and performance database according to claim 1 is characterized in that: Analyze the real-time key data and stored historical data stored in the rail transit engineering quantity and performance database, and determine the progress evaluation value, including: The progress evaluation value is determined by analyzing the real-time key data stored in the rail transit engineering quantity and performance database and the stored historical data: ； in, is the progress evaluation value, is the actual engineering quantity, The preset planned engineering quantity, is the actual number of manpower, is the preset planned manpower number, is the actual manual performance, For preset planned manual performance, is the progress deviation influencing factor, is the manual performance influence coefficient, is the actual output value completed, is the preset planned output value, is the influencing factor of output value deviation, is the actual resource consumption, is the planned resource consumption, is the resource consumption deviation factor, is the project progress weight, is the resource consumption weight, is the historical performance impact coefficient, is the average of historical performance.

9. The method for optimizing the progress of railway transportation engineering quantity and performance database according to claim 1 is characterized in that: Optimize resource allocation based on progress evaluation values ​​and real-time key data, including: The optimization formula for determining the number of construction personnel based on progress evaluation value and real-time key data is as follows: ； in, The number of construction workers after optimization: is the actual engineering quantity, The preset planned engineering quantity, is the preset total amount of work, D0 is the planned completion date, and D1 is the date of completion of the actual amount of work. is the preset maximum number of construction personnel, is the influence coefficient of the engineering quantity, is the influence coefficient of time, is the influence coefficient of the number of construction personnel, is the number of construction workers required for each unit of work, To round up; Determine a resource consumption optimization plan based on the progress evaluation value and a preset value-plan database; Optimal configuration is performed based on the optimized number of construction personnel and resource consumption optimization plan.

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