A water conservancy project construction progress forecasting management system based on multiple data modules

Abstract

The present invention relates to the technical field of water conservancy project construction progress prediction, and proposes a water conservancy project construction progress prediction management system based on a multi-data module. The specific implementation steps of the present invention include: first, real-time collection of water flow, silt and meteorological data of the target river channel, and combined with the construction plan and personnel operation conditions, using data cleaning technology to obtain standardized data; then, the construction progress prediction module establishes an impact model of water flow, silt, meteorology, operation conditions and upstream projects through engineering feature screening and comprehensive analysis, and then predicts the construction progress of different water conservancy projects; then, the monitoring and early warning module calculates the difference in construction days according to the construction plan and predicted progress, and issues an early warning when the construction progress is abnormal, ensuring real-time monitoring and optimization of the construction progress of the water conservancy project. The present invention ensures that the progress of the river channel regulation project is effectively monitored and managed, which helps to improve construction efficiency.

Description

Technical Field

[0001] The invention relates to the technical field of water conservancy project construction progress prediction, and in particular to a water conservancy project construction progress prediction management system based on multiple data modules. Background Art

[0002] Water conservancy projects include flood control, irrigation, water supply, power generation, shipping and other engineering construction. Among them, river regulation projects, as one of the important types of water conservancy projects, can solve problems such as river siltation, flood threats and water pollution through dredging, bank protection and embankment reinforcement, and provide safe and reliable conditions for the construction of reservoir dams, sluice gates and pump station equipment. However, river regulation projects are affected by multiple factors such as hydrological conditions, geological environment and climate change, which leads to the inability to achieve the expected construction progress, and even causes problems such as frequent adjustments to construction plans, project cost overruns and reduced construction quality. Therefore, the introduction of water conservancy project construction progress prediction technology can not only reasonably allocate resources to ensure that the project is carried out as planned, but also effectively respond to complex hydrological conditions and changes in the construction environment, and reduce construction risks.

[0003] At present, the existing water conservancy project construction progress prediction method takes into account the construction plan and construction cost to predict the construction progress of different water conservancy projects. It is unable to cope with the particularities of river management projects, such as changes in water flow, changes in river siltation, the impact of river management projects on surrounding water conservancy projects, and the interference of climate change on the construction process, resulting in reduced accuracy and reliability of construction progress prediction, causing delays in construction plans, thereby hindering the construction of surrounding water conservancy projects.

[0004] In order to solve the above problems, the present invention proposes a water conservancy project construction progress prediction and management system based on multiple data modules. Summary of the invention

[0005] The purpose of the present invention is to provide a water conservancy project construction progress prediction management system based on a multi-data module, which collects the water flow, silt and meteorological data of the target river in real time, and uses data cleaning technology to obtain standardized data in combination with the construction plan and personnel operation conditions; then, the construction progress prediction module establishes the influence model of water flow, silt, meteorology, operation conditions and upstream projects through engineering feature screening and comprehensive analysis, and then predicts the construction progress of different water conservancy projects; then, the monitoring and early warning module calculates the difference in construction days according to the construction plan and the predicted progress, and issues an early warning when the construction progress is abnormal, so as to ensure the real-time monitoring and optimization of the construction progress of the water conservancy project. The present invention ensures that the progress of the river regulation project is effectively monitored and managed, which helps to improve the construction efficiency.

[0006] A water conservancy project construction progress forecasting and management system based on multiple data modules, comprising: Multi-data acquisition module, real-time collection of target river flow data, silt data and meteorological data; obtain the construction plan and personnel operation status of the river management project; The multi-data cleaning module cleans the water flow data, silt data and meteorological data to obtain standard water flow data, standard silt data and standard meteorological data; The standard water flow data, standard silt data, standard meteorological data and the personnel operation conditions are respectively screened and integrated for engineering characteristics to obtain water flow impact characteristics, silt impact characteristics, meteorological impact characteristics and operation condition impact characteristics; a water flow physical model is constructed to simulate the impact of changes in the upstream water conservancy project of the target river on the progress of different projects to obtain the upstream project impact characteristics; for the dredging project, bend cutting project, branch blocking project and bank protection project of river management, the water flow impact characteristics, the silt impact characteristics, the meteorological impact characteristics, the operation condition impact characteristics and the upstream project impact characteristics are combined to construct an initial project construction progress prediction model and a subsequent project construction progress prediction model to predict the construction progress of different projects; The monitoring and early warning module calculates the difference in construction days according to the predicted construction progress and the construction plan; if the difference in construction days is less than a predetermined difference threshold, an early warning is issued to the management personnel.

[0007] Preferably, before multiple data are collected, the target river channel is divided into several river channel areas; the water flow data includes the flow rate, flow velocity, flow direction and water level of all river channel areas; the silt data includes the silt thickness, silt distribution image, silt sedimentation rate and silt scouring rate of all river channel areas; the meteorological data includes the precipitation, temperature, humidity, wind speed and wind direction of the target river channel; the construction plan is the daily planned completion amount of each stage of the project; the personnel work situation includes the number of personnel, work location and daily completion amount.

[0008] Preferably, before the multi-data cleaning module cleans the sludge data, the multi-data cleaning module further includes: The silt distribution image of each river channel area in the silt data is obtained, and the overlapping areas in the silt distribution images corresponding to adjacent river channel areas are deleted through coordinate correction and image recognition to obtain non-overlapping silt distribution images of the river channel areas; according to the image preprocessing operation, the non-overlapping silt distribution images are filtered and image enhancement operations are performed to obtain a silt distribution enhanced image; the silt area in the silt distribution enhanced image is segmented by combining the regional growing method, and the number of pixel points in the silt area is calculated to obtain the number of silt pixels; according to the resolution of the silt distribution image, the number of silt pixels is converted into the corresponding silt distribution area; and the new silt data is generated by combining the silt distribution area and other silt data in the silt data.

[0009] Preferably, the specific implementation process of respectively screening and integrating the engineering features of the standard water flow data, the standard silt data, the standard meteorological data and the personnel operation conditions includes: Obtain historical river management data, and calculate the attribute correlation coefficients between the standard water flow data, the standard silt data, the standard meteorological data, and the different attribute data in the personnel operation conditions in the historical time and the actual construction progress of different projects through the Pearson correlation coefficient; screen the engineering characteristics of each attribute according to the attribute correlation coefficient to obtain key water flow data, key silt data, key meteorological data, and key personnel operation data; According to the key data of water flow, key data of silt, key data of meteorology, key data of personnel operation and the regional distance of different river areas, the water flow impact characteristics, the silt impact characteristics, the meteorological impact characteristics and the operation situation impact characteristics are obtained through the comprehensive engineering characteristics; the specific calculation formula is expressed as: ; ; in, Representing a collection Middle Factors The impact characteristics; Indicates The river area The corresponding key data Data of attributes; express Impact factor; express The number of attributes in the corresponding key data; Indicates and The straight-line distance between the center points of the river area; Indicates The weight coefficient of each of the river areas; represents the total number of river areas; Indicates the characteristics of the meteorological impact; Represents an exponential function with a natural constant as base; Indicates the key meteorological data Data of attributes; express Impact factor; represents the meteorological impact threshold; represents the nonlinear adjustment parameter; Indicates the number of attributes in the meteorological key data.

[0010] Preferably, the specific implementation process of obtaining the upstream engineering impact characteristics includes: The data of the water conservancy projects within the predetermined range upstream of the target river are obtained, including reservoir data, sluice data and pump station data, and the water flow physical model is constructed to obtain the impact characteristics of the upstream projects; the specific calculation formula is expressed as follows: ; ; ; in, Indicates the impact characteristics of the upstream project; A function representation representing the water flow physical model; and denote the opening cross-sectional area vectors of the reservoir and sluice gate respectively; and Respectively represent the distance vectors between the reservoir and the sluice and the target river; and The water level difference vectors respectively represent the water level of the reservoir and the sluice gate and the water level upstream of the target river channel; represents the regulation coefficient vector of the reservoir flood discharge; represents the total number of reservoirs; represents the opening vector of the sluice gate; Indicates the total number of sluice gates; Indicates the impact characteristics of the upstream pumping station; , and represent the weight coefficients of reservoirs, sluice gates and pumping stations respectively; It represents the flow coefficient; Reservoir The cross-sectional area of ​​the opening; Reservoir The distance from the target river channel; represents the acceleration due to gravity; Reservoir The water level difference between the water level at the upstream of the target river channel and the water level at the upstream of the target river channel; Reservoir Regulation coefficient of flood discharge; Indicates pump station The difference in water level between inlet and outlet; Indicates pump station The distance from the target river channel; Indicates pump station empirical index of efficiency; Indicates the flow control parameters of the pump station; Indicates the total number of pumping stations.

[0011] Preferably, the initial engineering construction progress prediction model includes a time series feature extraction layer, a feature fusion layer and a construction progress prediction layer; The time series feature extraction layer is used to extract the time series impact features of different input data respectively; The feature fusion layer is used to capture the correlation between features with different temporal effects and perform feature fusion to obtain multi-data fusion features; The construction progress prediction layer predicts the construction progress of the initial project by fusion of multiple data features.

[0012] Preferably, the specific implementation process of constructing the subsequent project construction progress prediction model includes: The water flow impact characteristics, the silt impact characteristics, the meteorological impact characteristics, the operation situation impact characteristics and the upstream project impact characteristics of the project to be predicted are obtained, and the subsequent project construction progress prediction model is constructed by combining the operation time of the previous project and the initial project construction progress prediction model; wherein, the operation time of the previous project is spliced ​​with the multi-data fusion characteristics after the dimension expansion operation and input into the construction progress prediction layer for prediction; If the previous project is an initial project, the construction forecast progress of the initial project is obtained according to the initial project construction progress forecast model, and the operation completion time of the initial project is determined; if the previous project is a non-initial project, the operation completion time of the non-initial project is determined according to the subsequent project construction progress forecast model.

[0013] Preferably, according to the predicted construction progress and the construction plan, the specific calculation formula for calculating the difference in construction days is: ; in, Indicates days difference in the said construction days; Indicates The forecast construction progress for the day; Indicates The construction plan for the day.

[0014] Preferably, the water conservancy project construction progress forecasting and management system further includes: The construction plan adjustment module adjusts the work status of personnel when the monitoring and early warning module issues an early warning, that is, adjusts the work positions and number of different personnel; based on the adjusted work status of the personnel, re-forecasts the construction forecast progress until the construction plan is reached.

[0015] Compared with the prior art, the present invention has the following beneficial effects: 1. The present invention can effectively identify and quantify the impact characteristics of different factors on river management projects through engineering feature screening and integration processes. By using the Pearson correlation coefficient to perform attribute correlation analysis on standard water flow data, standard silt data, standard meteorological data and personnel operation conditions, the key data most relevant to the actual construction progress can be accurately screened out to ensure the scientificity and accuracy of engineering decision-making. At the same time, by integrating the spatial distance relationship between each key data and the river area, the comprehensive impact of water flow, silt, meteorology and operation conditions can be further evaluated. This method not only improves the efficiency and quality of river management, but also helps to optimize resource allocation and improve the accuracy of construction progress prediction, thereby improving the efficiency of engineering construction.

[0016] 2. The water flow physical model proposed in the present invention can accurately simulate the impact of the upstream water conservancy project of the target river on the progress of the river management project. By comprehensively considering the different influencing factors of facilities such as reservoirs, sluices and pumping stations, such as the opening cross-sectional area, opening, flow coefficient, distance and water level difference, the model can reflect the dynamic changes of water conservancy facilities and their actual impact on the river flow in real time. This process not only improves the construction efficiency, but also promotes the smooth progress of the construction of surrounding water conservancy projects, ensuring the overall coordination and stability of the river management project.

[0017] 3. The present invention can effectively solve the problem that the existing construction progress prediction method cannot cope with the particularity of river management projects by combining multiple data modules and different influencing features. By constructing a construction progress prediction model for the initial project and subsequent projects, the correlation between various influencing factors can be captured in real time, providing a more accurate construction progress prediction. This process not only improves the accuracy of the construction plan and avoids the occurrence of progress delays, but also can identify potential engineering bottlenecks in advance, optimize the allocation of construction resources, promote the smooth progress of water conservancy project construction, and avoid the obstruction of surrounding water conservancy project construction due to construction delays. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 A flowchart of a water conservancy project construction progress prediction management system based on multiple data modules proposed in an embodiment of the present invention; Figure 2 A system structure diagram of a water conservancy project construction progress prediction management system based on multiple data modules proposed in an embodiment of the present invention; Figure 3 This is a data flow diagram of the initial engineering construction progress prediction model proposed in the embodiment of the present invention. DETAILED DESCRIPTION

[0019] 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.

[0020] In order to effectively manage the construction progress of river channel regulation projects and improve construction efficiency, a construction company introduced a water conservancy project construction progress prediction management system based on multiple data modules to predict and manage the construction progress of river channel regulation projects. In order to illustrate the effectiveness of the water conservancy project construction progress prediction management system proposed by the present invention, it will be specifically described in conjunction with the accompanying drawings of this embodiment and the following two embodiments.

[0021] Embodiment 1

[0022] Before a construction company conducted river channel regulation on River A, it conducted a detailed survey of the current status of River A. Since the bank slope of River A is soft and has unstable rock layers, the construction sequence of each stage of the river channel regulation process is: bank protection project, dredging project, bend cutting project and branch blocking project. In order to improve the accuracy of the construction progress prediction of River A, a water conservancy project construction progress prediction management system based on multiple data modules is introduced to predict and manage the construction progress of the river channel regulation project of River A. Figure 1 and Figure 2 , which are respectively a step flow chart and a system structure diagram of a water conservancy project construction progress prediction management system based on multiple data modules; the specific implementation steps of the system include: S1, real-time collection of water flow data, silt data and meteorological data, and acquisition of construction plans and personnel work conditions; S2, data cleaning of water flow data, silt data and meteorological data to obtain standard water flow data, standard silt data and standard meteorological data; S3, engineering feature screening and engineering feature synthesis of standard water flow data, standard silt data, standard meteorological data and personnel work conditions; S4, constructing a water flow physical model to simulate the impact of changes in water conservancy projects upstream of the target river on the progress of different projects, and obtaining the impact characteristics of upstream projects; S5, constructing an initial project construction progress prediction model and a subsequent project construction progress prediction model, and predicting the construction prediction progress of different projects; S6, calculating the difference in construction days based on the construction prediction progress and construction plan, and judging whether to issue an early warning; S7, adjusting the personnel work conditions, and re-predicting the construction prediction progress until the construction plan is reached.

[0023] Furthermore, the multi-data acquisition module collects the water flow data, silt data and meteorological data of the A river in real time; obtains the construction plan and personnel operation status of the river management project, corresponding to the above-mentioned S1 step. Before multi-data acquisition, the target river is divided into several river areas; the water flow data includes the flow, flow velocity, flow direction and water level of all river areas; the silt data includes the silt thickness, silt distribution image, silt sedimentation rate and silt scouring rate of all river areas; the meteorological data includes the precipitation, temperature, humidity, wind speed and wind direction of the target river; the construction plan is the daily planned completion of each stage of the project; the personnel operation status includes the number of personnel, operation location and daily completion.

[0024] Tables 1 and 2 list some examples of flow data and silt data collected in different river areas, respectively.

[0025] Table 1. Some examples of water flow data in different river areas

[0026] Table 2. Some examples of silt data in different river areas

[0027] By collecting the flow data, silt data and meteorological data of River A in real time, combined with the construction plan and personnel operation status of the river management project, strong data support can be provided for river management. The flow data can help analyze the flow characteristics of the river, the silt data provides information on sedimentation changes and scouring conditions, and the meteorological data can predict the impact of extreme weather on the river. Secondly, the collection of construction plans and personnel operation conditions allows the management process to be closely integrated with the actual construction progress and personnel operation conditions, ensuring that the tasks at each stage are completed as planned, improving project efficiency and resource utilization.

[0028] Furthermore, the multi-data cleaning module cleans the water flow data, silt data and meteorological data to obtain standard water flow data, standard silt data and standard meteorological data, corresponding to the above-mentioned step S2; wherein, data cleaning includes missing value processing, outlier processing, denoising, time alignment and data standardization.

[0029] Specifically, before the multi-data cleaning module cleans the sludge data, it also includes: The silt distribution image of each river channel area in the silt data is obtained, and the overlapping areas in the silt distribution images corresponding to the adjacent river channel areas are deleted through coordinate correction and image recognition to obtain non-overlapping silt distribution images of the river channel areas; according to the image preprocessing operation, the non-overlapping silt distribution images are filtered and image enhancement operations are performed to obtain the silt distribution enhanced image; the silt area in the silt distribution enhanced image is segmented by combining the regional growing method, and the number of pixel points in the silt area is calculated to obtain the number of silt pixels; according to the resolution of the silt distribution image, the number of silt pixels is converted into the corresponding silt distribution area; and new silt data is generated by combining the silt distribution area and other silt data in the silt data.

[0030] Through coordinate correction and image recognition technology, the overlapping parts of the silt distribution images of adjacent river areas are deleted, ensuring the independence and accuracy of the silt data of each river area and avoiding errors caused by repeated data calculation. At the same time, the non-overlapping silt distribution images are filtered and enhanced to make the boundaries of the silt area clearer. In addition, the regional growing method is used to segment the silt area and calculate the number of silt pixels, and the number of pixels is accurately converted into the silt distribution area, realizing the accurate quantification of the silt amount, providing a more comprehensive and accurate data basis for subsequent data analysis and decision support.

[0031] Furthermore, the construction progress prediction module screens and integrates engineering features of standard water flow data, standard silt data, standard meteorological data and personnel operation conditions to obtain water flow impact characteristics, silt impact characteristics, meteorological impact characteristics and operation condition impact characteristics; corresponding to the above step S3, the specific implementation process includes: Obtain historical river management data, and calculate the attribute correlation coefficients between different attribute data in historical standard water flow data, standard silt data, standard meteorological data, and personnel operation conditions and the actual construction progress of different projects through the Pearson correlation coefficient; screen the engineering characteristics of each attribute according to the attribute correlation coefficient to obtain key water flow data, key silt data, key meteorological data, and key personnel operation data; According to the key data of water flow, silt, meteorology, personnel operation and the regional distance of different river areas, the water flow impact characteristics, silt impact characteristics, meteorology impact characteristics and operation situation impact characteristics are obtained through the comprehensive engineering characteristics; the specific calculation formula is expressed as: ; ; in, Representing a collection Middle Factors The impact characteristics of the collection Including key data of water flow, key data of silt and key data of personnel operation; Indicates River area The corresponding key data Data of attributes; express The impact factor ; express The number of attributes in the corresponding key data; Indicates and The straight-line distance between the center points of the river areas; Indicates The weight coefficient of each river area is obtained through expert experience scoring; represents the total number of river areas; Indicates the characteristics of meteorological impact; Represents an exponential function with a natural constant as base; Indicates the key meteorological data Data of attributes; express The impact factor ; represents the meteorological impact threshold, which is obtained by fitting the experimental data; represents the nonlinear adjustment parameter, which is obtained by fitting the experimental data; Indicates the number of attributes in the meteorological key data.

[0032] Data with strong correlation with "construction progress" is screened out from "water flow / silt / weather / personnel operations" in multi-source data. The specific method uses the characteristic screening of Pearson correlation coefficient for calculation. The specific process is as follows: Step 1: Data standardization. Perform Z-score standardization on the original data to eliminate dimensional differences: .

[0033] in, represents the data mean, Represents the standard deviation of the data.

[0034] Step 2: Correlation calculation. The calculation formula of Pearson correlation coefficient r is as follows: ; in, Represents the data of water flow / silt / weather / personnel operation at the i-th moment, Indicates the average value of historical data of water flow / silt / weather / personnel operation; It represents the data of construction progress strength at the i-th moment, and it represents the average value of historical data of construction progress strength.

[0035] Step three: screening of key data.

[0036] Key data include four types: water flow, silt, gas phase and personnel. The key data screening basis and results are shown in Table 3 below: Table 3. Key data screening basis

[0037] about Impact Factor :Using the same calculation method as above, the attribute correlation coefficient between different attribute data in historical standard water flow data, standard silt data and personnel operation conditions and the actual construction progress of different projects can be calculated through the "Pearson correlation coefficient". "Attribute data" is the most direct basis for the influencing factors in "water flow data, standard silt data and personnel operation conditions"; the higher the correlation coefficient, the greater the impact of the attribute data on the construction progress, and the higher the corresponding influencing factor should be set. According to the current historical data and engineering experience, The value range is ≥0.6.

[0038] About The weight coefficient of the river area , according to the following three specific restrictions: (1) Importance of river areas.

[0039] Different river areas may have different importance in the entire project. For example, key node areas, areas with high construction difficulty, areas involving sensitive environments, etc. need to be given higher weight coefficients. .

[0040] (2) Differences in regional characteristics.

[0041] There may be significant differences in hydrological conditions, silt characteristics, meteorological conditions, construction conditions, etc. in different river areas. Weight coefficient It can be used to adjust the contribution of different regions to the overall impact characteristics to better reflect regional differences.

[0042] (3) Project planning and objectives.

[0043] Project planning may have different requirements for the construction progress of different areas. For example, in order to ensure the overall construction period, it may be necessary to give priority to the completion of construction in certain key areas, and these areas can be given higher weight coefficients. .

[0044] The value range is positive, indicating the importance of the project in the region, the contribution of the project in the difference area to the whole, and the contribution of the project to the overall construction period.

[0045] Meteorological impact threshold The selection of is directly related to specific meteorological parameters, taking rainfall, wind speed and temperature as examples: (1) Impact of rainfall: Light impact threshold: For example, a daily rainfall of 5-10 mm may only result in a reduction in the efficiency of some open-air operations.

[0046] Moderate impact threshold: For example, daily rainfall of 20-30 mm may cause most open-pit operations to be suspended or delayed.

[0047] Severe impact threshold: For example, daily rainfall of more than 50 mm may lead to a complete shutdown of work or even cause flood disaster risks.

[0048] (2) Impact of wind speed: Mild impact threshold: For example, wind force 4-5 may affect the comfort and safety of high-altitude operations.

[0049] Moderate impact threshold: For example, a wind force of 6-7 may cause some high-altitude operations to be suspended or lifting and hoisting to be restricted.

[0050] Severe impact threshold: For example, winds above level 8 may lead to a complete cessation of high-altitude operations and even endanger structural safety.

[0051] The wind speed threshold is closely related to the operating altitude, machine type, structure type, etc., and requires specific analysis based on the specific situation.

[0052] (3) Impact of temperature: High temperature threshold: For example, temperatures above 35°C may affect the quality of concrete pouring and increase the risk of heat stroke for workers.

[0053] Low temperature threshold: For example, temperatures below 0°C may affect concrete maintenance and foundation construction in frozen areas.

[0054] In actual engineering applications, the meteorological impact threshold It is possible to make dynamic adjustments and iterative changes. As the project progresses, the climate changes, and experience accumulates, the thresholds should be regularly evaluated and adjusted to maintain the effectiveness and accuracy of the model.

[0055] Nonlinear tuning parameters :The impact of meteorological conditions on engineering construction is nonlinear. For example, when meteorological conditions approach a threshold, such as when rainfall approaches a critical value, the impact on construction progress may suddenly increase. Parameters are used to capture this nonlinear characteristic.

[0056] is a positive value. Because the worse the weather conditions, the greater the weather impact characteristics. According to current historical data and engineering experience, The value range of is between [0.1, 10]: Small value, such as The value range is [0.1-1): It means that the change of meteorological influence is relatively gentle and the nonlinearity is low. It is suitable for scenarios where the impact of meteorological conditions on construction progress is relatively mild or the model prediction results need to be more stable.

[0057] The value is moderate, such as The value range is [1-5): It indicates that the nonlinearity of meteorological influence is moderate, which can better capture the sudden change of meteorological influence when the meteorological conditions are close to the threshold. This is a relatively common range, which is suitable for most water conservancy project construction progress prediction.

[0058] The value is large, such as The value range is [5-10]: It indicates that the nonlinearity of meteorological influence is high. Once the meteorological conditions exceed the threshold, the impact will increase rapidly. It is suitable for scenarios where meteorological conditions have a very significant impact on the construction progress and there is an obvious threshold, such as the impact of heavy rain on earthwork excavation projects.

[0059] The embodiment of the present application can realize accurate identification and quantification of key factors affecting construction progress by screening and integrating engineering features of standard water flow data, standard silt data, standard meteorological data and personnel operation conditions. Key data with high correlation are screened out by Pearson correlation coefficient. Combining the comprehensive characteristics of water flow, silt, meteorology and operation conditions, factors such as regional distance and weighting coefficient are used to provide more accurate input data for construction progress prediction. This process not only improves the prediction accuracy, but also makes the construction progress management more scientific and reasonable, and can effectively reduce the risks and delays caused by environmental changes and improper operations during the construction process, thereby improving the overall efficiency of the project.

[0060] Furthermore, the construction progress prediction module constructs a water flow physical model to simulate the impact of changes in water conservancy projects upstream of the target river on the progress of different projects, and obtains the impact characteristics of upstream projects; Figure 1 The specific implementation process of step S4 includes: Obtain the data of water conservancy projects within the predetermined range upstream of the target river, including reservoir data, sluice data and pump station data, and build a water flow physical model to obtain the impact characteristics of upstream projects; the specific calculation formula is expressed as: ; ; ; in, Indicates the impact characteristics of upstream projects; Functional representation of water flow physical model; and denote the opening cross-sectional area vectors of the reservoir and sluice gate respectively; and Respectively represent the distance vectors between the reservoir and the sluice and the target river; and The water level difference vectors representing the water level of the reservoir and sluice gate and the water level upstream of the target river; represents the regulation coefficient vector of the reservoir flood discharge; represents the total number of reservoirs; represents the opening vector of the sluice gate; Indicates the total number of sluice gates; Indicates the impact characteristics of the upstream pumping station; , and Represent the weight coefficients of reservoirs, sluice gates and pumping stations respectively, , and The values ​​are all in the interval Inner ; It indicates the discharge coefficient of a reservoir or sluice gate; Reservoir The cross-sectional area of ​​the opening; Reservoir Distance to the target river channel; represents the acceleration due to gravity; Reservoir The water level difference between the water level at the destination and the water level upstream of the target river; Reservoir Regulation coefficient of flood discharge; Indicates pump station The difference in water level between inlet and outlet; Indicates pump station Distance to the target river channel; Indicates pump station empirical index of efficiency; Indicates the flow control parameters of the pump station; Indicates the total number of pumping stations.

[0061] (Reservoir weight coefficient): represents the relative importance of the impact of upstream reservoir flood discharge on the water flow conditions in the target river.

[0062] (Sluice weight coefficient): represents the relative importance of upstream sluice regulation on the flow conditions in the target river.

[0063] (Pumping station weight coefficient): represents the relative importance of the impact of upstream pumping station operation on the water flow conditions in the target river.

[0064] Three weight coefficients ( , and ) are selected based on the following: Balancing the impact of different types of upstream projects: The formula combines the impact of three different types of upstream water conservancy projects, namely reservoirs, sluice gates and pumping stations, in a linear weighted manner. Since different types of projects may have different mechanisms and degrees of impact on downstream river flows, it is necessary to introduce weight coefficients for adjustment and balance.

[0065] Reflect project characteristics and focus: The value of the weight coefficient can be set according to the specific project characteristics and focus. For example, in some projects, the flood discharge of the reservoir may be the main factor affecting the water flow, while in other projects, the pumping and drainage of the pumping station may be more critical. The weight coefficient allows the model to flexibly adjust the contribution ratio of different factors according to actual conditions.

[0066] Achieve comprehensive quantification of impact characteristics: Through the weighted summation of weight coefficients, the final UF value (upstream project impact characteristics) is a comprehensive indicator that quantifies the overall impact of all considered upstream projects on the water flow conditions of the target river.

[0067] Here are three specific setting examples under different dominant factors: (1) Flood control pressure is the dominant factor The flood control pressure in this area is relatively high, and the reservoir's flood regulation capacity is key. The reservoir's flood discharge has the most significant impact on the downstream water level.

[0068] k1 (reservoir weight) = 0.6 (reservoir flood discharge has the greatest impact and has a higher weight) k2 (sluice gate weight) = 0.3 (sluice gate regulation has a certain influence, medium weight) k3 (pump station weight) = 0.1 (the pump station is small in scale, has relatively small impact, and has a lower weight) (2) Shipping security is the dominant factor The river also serves an important navigation function, and refined regulation of the sluices is crucial to ensuring the water depth of the waterway.

[0069] k1 (reservoir weight) = 0.3 (although the reservoir discharge has a large flow, the scheduling flexibility is poor, and the impact on shipping is negative) k2 (sluice gate weight) = 0.6 (sluice gates are flexible to control, play a big role in ensuring the water depth of the channel, and have a higher weight) k3 (pump station weight) = 0.1 (the pump station has a limited impact range and has little impact on shipping, so the weight is low) (3) Ecological environmental protection is the dominant factor There is an important wetland ecosystem in the lower reaches of the river, which needs to maintain a certain ecological base flow. The ecological water replenishment function of the pump station k1 (reservoir weight) = 0.4 (reservoir discharge can provide a certain ecological base flow, but the scheduling is limited) k2 (sluice weight) = 0.3 (sluice regulation can fine-tune water levels, with limited impact on ecology) k3 (pump station weight) = 0.3 (the ecological water replenishment of the pump station directly affects the improvement of the ecological environment, and the weight is increased) The steps for building the "water flow physical model" are as follows: Step 1: Identify three types of influencing factors of the "water flow physical model": Reservoirs affect downstream rivers through flood discharge from reservoirs.

[0070] A sluice gate regulates the water flow in the downstream river by adjusting the degree of its opening.

[0071] Pumping station, which affects the flow of river water by pumping or discharging water.

[0072] Step 2: Construction of reservoir impact model:

[0073] The overall impact of all upstream reservoirs is accumulated because the model considers multiple reservoirs acting together on the target river.

[0074] It is an empirical coefficient used to modify the theoretical flow formula to take into account the complexity of actual water flow, such as orifice shape, head loss, etc. Practical engineering explanation: The value of depends on the specific form of the reservoir flood discharge facility and needs to be determined by referring to hydraulic manuals or relevant engineering experience. For example, for a regular rectangular orifice, The value range is usually between 0.6-0.8.

[0075] Represents the cross-sectional area of ​​the reservoir flood discharge outlet. Practical engineering explanation: In actual engineering, It can be calculated or measured by the opening of the reservoir flood discharge gate. The larger the gate opening, The bigger it is, the stronger its flood discharge capacity is.

[0076] The key factor representing the downstream flow, this part is derived from the simplified form of Bernoulli's equation or Torricelli's formula, which represents the velocity term related to the head difference. Practical Engineering Explanation: is the water level difference between the water level of the ith reservoir and the water level upstream of the target river. The greater the water level difference, the greater the driving force of the water flow and the greater the flow velocity; g is the acceleration due to gravity.

[0077] The artificial control factors of the flood discharge of the reservoir can reflect the scheduling strategy of the reservoir.

[0078] For example, = 1 may represent normal flood discharge status; < 1 may represent a reduction in flood discharge; > 1 In special cases, it may mean an increase in flood discharge (but this condition is not common, usually ≤1).

[0079] By accurately simulating the changes in the upstream water conservancy projects of the target river, such as the regulating effects of reservoirs, sluices and pumping stations, the impact of these projects on the construction progress can be comprehensively analyzed. The model can derive the dynamic impact characteristics of the upstream water conservancy projects on the construction progress based on the flow characteristics and water conservancy project parameters (such as the opening cross-sectional area of ​​the reservoir, the water level difference, the efficiency of the pumping station, etc.), combined with the weight coefficient and the flow coefficient. This method not only improves the accuracy of the prediction of the construction progress, but also optimizes the project scheduling and resource allocation, thereby reducing project delays and improving the construction efficiency of the project.

[0080] Furthermore, the construction progress prediction module combines the water flow impact characteristics, silt impact characteristics, meteorological impact characteristics, operation situation impact characteristics and upstream project impact characteristics for the dredging project, bend cutting project, branch blocking project and bank protection project of river management to build the initial project construction progress prediction model and the subsequent project construction progress prediction model, and predict the construction progress of different projects, corresponding Figure 1 Step S5; See also Figure 3 , which is a data flow diagram of the initial project construction progress prediction model; wherein the initial project construction progress prediction model includes a time series feature extraction layer, a feature fusion layer and a construction progress prediction layer; The time series feature extraction layer is used to extract the time series impact features of different input data respectively; The feature fusion layer is used to capture the correlation between features with different temporal effects and perform feature fusion to obtain multi-data fusion features; The construction progress prediction layer predicts the construction progress of the initial project by fusion of multiple data features.

[0081] Specifically, the time series feature extraction layer uses a convolutional neural network to extract the time series influence features of water flow influence features, silt influence features, meteorological influence features, operating conditions influence features and upstream engineering influence features, and perform dimensional splicing; the feature fusion layer uses a long short-term memory network or an attention mechanism to capture the correlation between different time series influence features after splicing, and performs feature fusion; the construction progress prediction layer predicts the construction progress of the initial project through a fully connected layer and a prediction head.

[0082] The embodiment of the present application can effectively improve the accuracy of construction progress prediction of river management projects through the initial construction progress prediction model. By comprehensively considering multiple influencing factors such as water flow, silt, weather, operating conditions and upstream projects, the model can not only accurately extract and integrate the temporal characteristics of these influencing factors, but also capture the correlation between the characteristics, thereby providing a more accurate construction progress prediction, which helps project managers to reasonably dispatch resources and optimize construction plans in the initial and subsequent stages, reduce the risk of project delays, and improve construction efficiency.

[0083] Furthermore, the specific implementation process of constructing the subsequent project construction progress prediction model includes: Obtain the water flow impact characteristics, silt impact characteristics, meteorological impact characteristics, operation situation impact characteristics and upstream project impact characteristics of the project to be predicted, and build a subsequent project construction progress prediction model by combining the operation time of the previous project and the initial project construction progress prediction model; among them, the operation time of the previous project is spliced ​​with the multi-data fusion features after the dimension expansion operation and input into the construction progress prediction layer for prediction; If the previous project is an initial project, the construction forecast progress of the initial project is obtained according to the initial project construction progress forecast model to determine the completion time of the initial project; if the previous project is a non-initial project, the completion time of the non-initial project is determined according to the subsequent project construction progress forecast model.

[0084] Specifically, for the four stages of the construction process of the river channel regulation project of River A: bank protection project, dredging project, bend cutting project and branch blocking project, the initial project construction progress prediction model is used to predict the predicted construction progress of the bank protection project; combined with the completion time of the bank protection project, the predicted construction progress of the dredging project is predicted through the subsequent project construction progress prediction model; combined with the completion time of the dredging project, the predicted construction progress of the bend cutting project is predicted through the subsequent project construction progress prediction model; combined with the completion time of the dredging project, the predicted construction progress of the bend cutting project is predicted through the subsequent project construction progress prediction model; combined with the completion time of the bend cutting project, the predicted construction progress of the branch blocking project is predicted through the subsequent project construction progress prediction model.

[0085] In order to clearly illustrate the accuracy of the initial project construction progress prediction model and the subsequent project construction progress prediction model proposed in the present invention, referring to Table 4, construction prediction progress for different construction dates is exemplarily given.

[0086] Table 4. Some examples of construction forecast progress for different construction dates

[0087] By building a prediction model for the construction progress of subsequent projects, we can effectively integrate multiple influencing factors, including water flow, silt, weather, operation conditions, and the operation time of upstream and previous projects, to provide accurate predictions for the construction progress of subsequent projects. This model can adapt to the construction progress prediction of projects at different stages, ensuring that the project progress can be accurately estimated based on actual conditions. This method can improve the accuracy of construction plans, reduce time waste during construction, and optimize resource allocation.

[0088] Furthermore, corresponding Figure 1 In the S6 step, the monitoring and early warning module calculates the difference in construction days according to the construction forecast progress and construction plan; the specific calculation formula is: ; in, Indicates days of construction difference; Indicates The construction forecast progress in days; Indicates 1. Construction plan for the day; If the difference between construction days is less than the predetermined difference threshold, an early warning is issued to the management personnel.

[0089] The embodiment of the present application can timely detect the deviation between the construction progress and the plan through the monitoring and early warning module to ensure that the project is advanced according to the predetermined plan. The module calculates the difference in construction days based on the predicted construction progress and the actual construction plan, and issues an early warning when the deviation is small, helping managers to identify potential problems in advance and take necessary measures, which can effectively avoid waste of resources and time delays caused by progress delays and improve construction efficiency.

[0090] Furthermore, corresponding Figure 1 In step S7, when the monitoring and early warning module issues an early warning, the construction plan adjustment module adjusts the work status of the personnel, that is, adjusts the work positions and the number of personnel of different personnel; based on the adjusted work status of the personnel, the construction forecast progress is re-forecasted until the construction plan is reached.

[0091] Specifically, based on the early warning information, determine which river areas need to increase personnel or adjust personnel distribution. For example, if the construction period of a river area is delayed, the work efficiency can be improved by mobilizing personnel, adjusting the number of personnel and work locations to ensure that the personnel assigned to key tasks have sufficient capabilities. According to the adjusted personnel work situation, the construction progress is recalculated using the construction progress prediction model to evaluate the impact of the adjusted personnel allocation on the progress and predict whether it can catch up with the original construction plan. Set a threshold for the number of adjustments. If the adjusted progress is still delayed, the construction plan can be further adjusted.

[0092] By introducing the linkage mechanism between the monitoring and early warning module and the construction plan adjustment module, it is possible to monitor changes on the construction site in real time and issue early warnings in a timely manner, thereby effectively preventing the occurrence of potential problems. When an abnormal situation occurs, the construction plan adjustment module can respond quickly and adjust the location and number of personnel to ensure the continuity and efficiency of the operation, optimize the allocation of construction resources, and ensure that the project is completed on time and with quality.

[0093] Embodiment 2

[0094] The embodiment of the present application applies a water conservancy project construction progress prediction management system based on multiple data modules to a certain engineering construction company's river channel regulation project on River B to improve the efficiency of the project construction. After an on-site survey of River B, it was found that there was a large amount of silt in the riverbed of River B, which seriously affected the water quality of River B. Therefore, the construction sequence of the projects at different stages in the river channel regulation process of River B is: dredging project, bend cutting project, branch blocking project and bank protection project.

[0095] Specifically, the multi-data acquisition module collects the water flow data, silt data and meteorological data of the B river in real time to obtain the construction plan and personnel work status of the river management project.

[0096] Furthermore, the multi-data cleaning module cleans the water flow data, silt data and meteorological data to obtain standard water flow data, standard silt data and standard meteorological data.

[0097] Furthermore, the construction progress prediction module screens and integrates the engineering characteristics of the standard water flow data, standard silt data, standard meteorological data and personnel operation conditions, and obtains the water flow impact characteristics, silt impact characteristics, meteorological impact characteristics and operation condition impact characteristics; constructs a water flow physical model to simulate the impact of changes in water conservancy projects upstream of the target river on the progress of different projects, and obtains the impact characteristics of upstream projects; for dredging projects, bend cutting projects, branch blocking projects and bank protection projects for river management, the initial project construction progress prediction model and the subsequent project construction progress prediction model are constructed in combination with the water flow impact characteristics, silt impact characteristics, meteorological impact characteristics, operation condition impact characteristics and upstream project impact characteristics, and the construction forecast progress of different projects is predicted.

[0098] Furthermore, the monitoring and early warning module calculates the difference in construction days according to the construction forecast progress and the construction plan; if the difference in construction days is less than a predetermined difference threshold, an early warning is issued to the management personnel.

[0099] Furthermore, when the monitoring and early warning module issues an early warning, the construction plan adjustment module adjusts the work status of the personnel, that is, adjusts the work positions and number of different personnel; based on the adjusted work status of the personnel, the construction forecast progress is re-forecasted until the construction plan is achieved.

[0100] The embodiment of the present application can accurately identify and quantify the key factors affecting the progress of river management projects through engineering feature screening and comprehensive processes, thereby effectively improving construction efficiency and quality. Based on Pearson correlation coefficient analysis, key data highly related to the actual construction progress are accurately screened. By comprehensively considering factors such as water flow, silt, weather and personnel operation conditions, combined with the spatial relationship of the river area, the comprehensive impact of various factors on the project is comprehensively evaluated, greatly improving the accuracy of construction progress prediction. Secondly, based on the physical model of water flow, it is possible to accurately simulate the dynamic impact of water conservancy facilities (such as reservoirs, sluices, pumping stations, etc.) on river management projects, reflect the changes in water conservancy facilities in real time, ensure the coordinated advancement of water conservancy projects and river management, and avoid the impact caused by facility adjustments during construction. Finally, combining multiple data modules and construction progress prediction models not only improves the accuracy of the construction plan, but also captures the correlation between various influencing factors in real time, identifies potential bottlenecks in advance, optimizes resource allocation, and avoids the obstruction of surrounding water conservancy project construction due to delayed progress.

[0101] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A water conservancy project construction progress prediction management system based on multiple data modules, characterized in that: include: Multi-data acquisition module, real-time collection of water flow data, silt data and meteorological data of the target river, to obtain the construction plan and personnel operation status of the river management project; The multi-data cleaning module cleans the water flow data, silt data and meteorological data to obtain standard water flow data, standard silt data and standard meteorological data; The construction progress prediction module screens and integrates engineering features of standard water flow data, standard silt data, standard meteorological data and personnel operation conditions, and obtains water flow impact characteristics, silt impact characteristics, meteorological impact characteristics and operation condition impact characteristics; constructs a water flow physical model to simulate the impact of changes in upstream water conservancy projects in the target river on the progress of different projects, and obtains the impact characteristics of upstream projects; for dredging projects, bend cutting projects, branch blocking projects and bank protection projects for river management, the initial project construction progress prediction model and the subsequent project construction progress prediction model are constructed in combination with the water flow impact characteristics, silt impact characteristics, meteorological impact characteristics, operation condition impact characteristics and upstream project impact characteristics to predict the construction progress of different projects; The monitoring and early warning module calculates the difference in construction days based on the construction forecast progress and construction plan; if the difference in construction days is less than the predetermined difference threshold, an early warning is issued to the management personnel; The initial engineering construction progress prediction model includes a time series feature extraction layer, a feature fusion layer, and a construction progress prediction layer; The time series feature extraction layer is used to extract the time series impact features of different input data respectively; The feature fusion layer is used to capture the correlation between features with different temporal effects and perform feature fusion to obtain multi-data fusion features; The construction progress prediction layer predicts the construction progress of the initial project by fusion of multiple data features.

2. A water conservancy project construction progress forecasting and management system based on multiple data modules according to claim 1, characterized in that: Before multi-data collection, the target river channel is divided into several river channel areas; water flow data includes the flow, velocity, direction and water level of all river channel areas; silt data includes the silt thickness, silt distribution image, silt sedimentation rate and silt scouring rate of all river channel areas; meteorological data includes precipitation, temperature, humidity, wind speed and wind direction of the target river channel; the construction plan is the daily planned completion volume of each stage of the project; personnel work situation includes the number of personnel, work location and daily completion volume.

3. A water conservancy project construction progress forecasting and management system based on multiple data modules according to claim 1, characterized in that: Before the multi-data cleaning module cleans the sludge data, it also includes: The silt distribution image of each river channel area in the silt data is obtained, and the overlapping areas in the silt distribution images corresponding to adjacent river channel areas are deleted through coordinate correction and image recognition to obtain non-overlapping silt distribution images of the river channel areas; according to the image preprocessing operation, the non-overlapping silt distribution images are filtered and image enhancement operations are performed to obtain the silt distribution enhanced image; the silt area in the silt distribution enhanced image is segmented by combining the regional growing method, and the number of pixel points in the silt area is calculated to obtain the number of silt pixels; according to the resolution of the silt distribution image, the number of silt pixels is converted into the corresponding silt distribution area; and new silt data is generated by combining the silt distribution area and other silt data in the silt data.

4. A water conservancy project construction progress forecasting and management system based on multiple data modules according to claim 1, characterized in that: The specific implementation process of engineering feature screening and engineering feature integration for standard water flow data, standard silt data, standard meteorological data and personnel operation conditions includes: Obtain historical river management data, and calculate the attribute correlation coefficients between different attribute data in historical standard water flow data, standard silt data, standard meteorological data, and personnel operation conditions and the actual construction progress of different projects through the Pearson correlation coefficient; screen the engineering characteristics of each attribute according to the attribute correlation coefficient to obtain key water flow data, key silt data, key meteorological data, and key personnel operation data; According to the key data of water flow, silt, meteorology, personnel operation and the regional distance of different river areas, the water flow impact characteristics, silt impact characteristics, meteorology impact characteristics and operation situation impact characteristics are obtained through the comprehensive engineering characteristics; the specific calculation formula is expressed as: ； ； in, Representing a collection Middle Factors The impact characteristics; Indicates The river area The corresponding key data Data of attributes; express Impact factor; express The number of attributes in the corresponding key data; Indicates and The straight-line distance between the center points of the river areas; Indicates The weight coefficient of each river area; represents the total number of river areas; Indicates the characteristics of meteorological impact; Represents an exponential function with a natural constant as base; Indicates the key meteorological data Data of attributes; express Impact factor; represents the meteorological impact threshold; represents the nonlinear adjustment parameter; Indicates the number of attributes in the meteorological key data.

5. A water conservancy project construction progress forecasting and management system based on multiple data modules according to claim 1, characterized in that: The specific implementation process of obtaining the impact characteristics of upstream projects includes: Obtain the data of water conservancy projects within the predetermined range upstream of the target river, including reservoir data, sluice data and pump station data, and build a water flow physical model to obtain the impact characteristics of upstream projects; the specific calculation formula is expressed as: ； ； ； in, Indicates the impact characteristics of upstream projects; Functional representation of water flow physical model; and denote the opening cross-sectional area vectors of the reservoir and sluice gate respectively; and Represent the distance vectors between the reservoir and sluice and the target river, respectively; and The water level difference vectors representing the water level of the reservoir and sluice gate and the water level upstream of the target river; represents the regulation coefficient vector of the reservoir flood discharge; represents the total number of reservoirs; represents the opening vector of the sluice gate; Indicates the total number of sluice gates; Indicates the impact characteristics of the upstream pumping station; , and represent the weight coefficients of reservoirs, sluice gates and pumping stations respectively; It represents the flow coefficient; Reservoir The cross-sectional area of ​​the opening; Reservoir Distance to the target river channel; represents the acceleration due to gravity; Reservoir The water level difference between the water level at the upstream of the target river channel and the water level at the upstream of the target river channel; Reservoir Regulation coefficient of flood discharge; Indicates pump station The difference in water level between inlet and outlet; Indicates pump station Distance to the target river channel; Indicates pump station empirical index of efficiency; Indicates the flow control parameters of the pump station; Indicates the total number of pumping stations.

6. A water conservancy project construction progress forecasting and management system based on multiple data modules according to claim 1, characterized in that: The specific implementation process of building a subsequent project construction progress prediction model includes: Obtain the water flow impact characteristics, silt impact characteristics, meteorological impact characteristics, operation situation impact characteristics and upstream project impact characteristics of the project to be predicted, and build a subsequent project construction progress prediction model by combining the operation time of the previous project and the initial project construction progress prediction model; among them, the operation time of the previous project is spliced ​​with the multi-data fusion features after the dimension expansion operation and input into the construction progress prediction layer for prediction; If the previous project is an initial project, the construction forecast progress of the initial project is obtained according to the initial project construction progress forecast model to determine the completion time of the initial project; if the previous project is a non-initial project, the completion time of the non-initial project is determined according to the subsequent project construction progress forecast model.

7. A water conservancy project construction progress forecasting and management system based on multiple data modules according to claim 1, characterized in that: According to the construction forecast progress and construction plan, the specific calculation formula for calculating the difference in construction days is: ； in, Indicates days of construction difference; Indicates The construction forecast progress in days; Indicates Day construction plan.

8. The water conservancy project construction progress forecasting and management system based on multiple data modules according to claim 1 is characterized in that: The water conservancy project construction progress forecast management system also includes: The construction plan adjustment module adjusts the work status of personnel when the monitoring and early warning module issues an early warning, that is, adjusts the work positions and number of different personnel; based on the adjusted work status of personnel, re-forecasts the construction progress until the construction plan is achieved.

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