Water conservancy construction project management system based on data analysis

Abstract

The present application relates to the technical field of construction management, and specifically relates to a water conservancy construction project management system based on data analysis, comprising: a water flow monitoring module that collects water flow data in a construction area in real time through a sensor; a meteorological data acquisition module that obtains future precipitation around and correlates and analyzes the future precipitation with the water flow data to predict future water level change trends; a construction progress monitoring module that divides construction progress into multiple construction stages in a construction progress management unit, and the construction stages are uploaded in real time by on-site construction managers; a data analysis module that identifies the influence of water flow change trends on specific construction stages; and a self-adaptive water flow regulation module that formulates water flow management measures according to the analysis results of the correlation influence model. The present application provides intuitive quantitative indicators for construction managers, optimizes resource allocation and construction scheduling, thereby significantly improving construction management efficiency and reducing the probability of schedule delays and safety accidents.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of construction management, and particularly relates to a water conservancy construction project management system based on data analysis. BACKGROUND

[0002] Water conservancy construction projects often involve complex hydrological conditions, such as the influence of river flow rate, water level fluctuation and rainfall, which have a direct and significant impact on construction progress, resource allocation and construction safety. However, existing water conservancy construction project management systems still have the following technical bottlenecks in dynamically responding to changes in water flow, weather changes and construction progress coordination:

[0003] Current water conservancy construction management mostly relies on traditional monitoring equipment to collect water flow and weather data, and the data is scattered and lacks intelligent integration. In particular, in complex construction areas, key parameters such as water level, flow rate and flow cannot form a comprehensive and real-time dynamic monitoring network, making it difficult for managers to timely perceive sudden changes in water flow and their potential impact.

[0004] The specific impact of water flow changes on construction progress and construction stages is often ignored. Most current systems separate water flow management from construction scheduling, lacking a model that can quantify the dynamic impact of water flow changes on construction plans. This disconnection leads to construction delays and safety risks.

[0005] Traditional water flow management methods are usually based on static construction plans and lack flexibility and adaptability. In the event of severe water flow changes (such as during flood periods or heavy rainfall), existing systems' drainage equipment or water flow regulation equipment cannot intelligently adjust based on real-time monitoring data, often requiring manual intervention, which is time-consuming and inefficient, and may even cause greater safety hazards. SUMMARY

[0006] The present application provides a water conservancy construction project management system based on data analysis.

[0007] The water conservancy construction project management system based on data analysis comprises:

[0008] A water flow monitoring module: real-time collection of water flow data in the construction area through sensors, including river flow rate, water flow, and water level changes;

[0009] A weather data collection module: through a data interface with a weather monitoring center, future rainfall is obtained and associated with water flow data to predict future water level trends;

[0010] A construction progress monitoring module: a construction progress management unit is established, in which the construction progress is divided into multiple construction stages, and the construction stages are uploaded in real time by on-site construction management personnel;

[0011] Data analysis module: establish the correlation between water level change trend and construction progress, and identify the influence of water flow change trend on specific construction stage;

[0012] Adaptive water flow regulation module: according to the analysis result of the correlation influence model, formulate water flow management measures, including adjusting the water flow direction of the construction area, adjusting the water flow management equipment such as water pump and gate, to cope with the sudden change of water flow and ensure the stability of the construction environment.

[0013] Optionally, the water flow monitoring module collects water flow data in the construction area in real time through a plurality of sensors and flow meters installed in the construction area, the sensors include water level sensor, flow rate sensor and flow meter, the water level sensor is used to monitor the real-time change of water level, the flow rate sensor is used to collect the flow rate data of the river in real time, and the flow meter is used to measure the change of river water flow.

[0014] Optionally, the data interface in the meteorological data acquisition module communicates with the data platform of the meteorological monitoring center through network connection mode, obtains the precipitation prediction data within 24 to 72 hours in the future, and analyzes the correlation between the obtained future precipitation data and the water flow data collected by the water flow monitoring module, combines the water flow change trend and the precipitation data in the weather forecast, and calculates the future water level change trend by using the prediction algorithm, so as to provide basis for water flow management and construction progress adjustment, and predict the influence of water level change in advance.

[0015] Optionally, the correlation analysis between the precipitation and the water flow is represented as: Q(t)=α·P(t)+β, wherein Q(t) is the water flow (or flow rate) at time t, P(t) is the precipitation at time t, α is the coefficient of the influence of precipitation on water flow, indicating the influence degree of unit precipitation change on water flow, β is a constant term, indicating the water flow without precipitation, and the optimal values of α and β can be obtained through regression analysis of historical data;

[0016] By obtaining the future precipitation data: P(t+1), P(t+2), …, P(t+n), wherein n represents the time step of future prediction, and combining the known water flow Q(t), the prediction algorithm is used to predict the water level change trend, and the prediction algorithm is represented as:

[0017] Wherein, H(t+n) is the predicted water level at time t+n, γ is the coefficient of flow and water level change, indicating the influence of unit flow on water level, is the cumulative water flow in the future n time steps.

[0018] Optionally, the construction progress monitoring module specifically includes:

[0019] Construction phase division: according to the overall plan of the construction project, the construction progress is divided into multiple construction phases, each construction phase includes corresponding construction tasks, resource allocation and estimated completion time, and the construction phases are divided according to construction parts (foundation construction, structure construction, decoration construction), construction content (excavation, concrete pouring, equipment installation) or time node;

[0020] Progress management platform: the construction progress management unit accesses the construction progress information in real time through a digital platform, and the digital platform supports the construction management personnel to access the construction progress information in real time through mobile terminals, tablets or computers;

[0021] Real-time data uploading: the construction site management personnel uploads the actual progress data of each construction phase in real time through the digital platform, and the uploaded data includes the completion ratio, actual time and completed task details of the current construction phase;

[0022] The construction progress management unit generates a comparison report of the actual progress and the planned progress by analyzing the real-time uploaded data, and the report includes the progress deviation of each construction phase.

[0023] Optionally, the influence of the identified water flow change trend on the specific construction phase in the data analysis module includes construction progress delay rate D s and construction safety risk index R s , D s represents the progress delay rate of the construction phase s, R s represents the safety risk index of the construction phase s, and the correlation influence model of the water level change trend H(t+n) and the construction progress delay rate D s and the construction safety risk index R s , the correlation influence model includes a progress delay rate model and a construction safety risk index model, and the progress delay and safety risk of the specific construction phase are quantitatively analyzed through the dynamic influence of the water level change on the construction phase.

[0024] Optionally, the progress delay rate model is represented as: D s =g1(H(t+n),T p ,S c ), wherein H(t+n) is the predicted water level change trend, T p is the planned completion time of the construction phase s, and S c is a set of characteristic parameters of the construction phase s, including:

[0025] Current task type (excavation foundation, concrete pouring);

[0026] Resource usage (machinery and equipment, manpower);

[0027] Time urgency (whether the stage task is urgent);

[0028] g1 is a regression function, obtained by model training;

[0029] Based on the progress delay rate model, the progress delay rate D s is calculated as: wherein,

[0030] T p is the planned completion time, T a is the actual completion time.

[0031] Optionally, the construction safety risk index model is represented as: R s = g2(H(t+n), V s ), wherein V s is a risk sensitivity parameter of the construction phase s, and g2 is a classification function, obtained by training, based on the construction safety risk index model to calculate the construction safety risk index R s , quantifying the influence of water level change trend on the safety hidden danger of the construction phase, calculated as:

[0032] R s = a1·AH(t+n) + a2·V s , wherein a1 is a sensitive coefficient of water level change on safety risk (the risk value increased by 1 m of water level rise), a2 is a sensitive coefficient of construction phase risk characteristics on safety risk, and V s is a risk sensitivity parameter of the construction phase, including task exposure degree (such as whether personnel need to operate underwater) and equipment type (such as whether heavy equipment is used); according to the calculated R s , the safety risk of the construction phase is classified:

[0033] R s ≤1: low risk;

[0034] 1<R s ≤3: medium risk;

[0035] R s >3: high risk.

[0036] Optionally, the adaptive water flow adjustment module determines the target priority and classification decision based on D s and R s , and the target priority determination includes:

[0037] If R s >3: take safety protection measures to reduce the direct impact of water flow on the construction area;

[0038] If D s >30% and R s≤3: Prioritize reducing resistance or optimizing flow direction of drainage system to ensure construction progress.

[0039] Optionally, the classification decision specifically includes:

[0040] D s ≤10%, R s ≤1, low impact: no significant impact on construction, maintain current water flow management equipment operation status;

[0041] 10% < D s ≤30%, 1 < R s ≤3, medium impact: partially optimize drainage system or adjust flow direction to reduce potential impact;

[0042] D s >30% or R s >3, serious impact: comprehensively adjust the flow direction of the construction area, adjust the water flow management equipment, and prioritize construction safety.

[0043] Advantages of the present application:

[0044] The present application quantifies the specific impact of water level change trend on construction progress and safety risk by constructing a progress delay rate model and a construction safety risk index model. The progress delay rate model can dynamically calculate the time deviation of water level change on construction plan, and the construction safety risk index model can classify and evaluate the risk sensitivity of construction tasks. This dual-model collaborative analysis method not only provides intuitive quantitative indicators for construction managers, but also identifies risk points in real time during construction, optimizes resource allocation and construction scheduling, thereby significantly improving construction management efficiency and reducing the probability of progress delay and safety accidents.

[0045] The present application, through the adaptive water flow regulation module combined with the analysis results of progress delay rate and safety risk index, uses target priority analysis and classification decision to intelligently generate dynamic water flow management strategy, including optimizing drainage system, adjusting flow direction, and real-time regulating water pumps, gates and other equipment, which can quickly respond to sudden water flow changes, ensure construction environment stability, and reduce construction delay and resource waste.

[0046] The present application integrates water flow monitoring module, meteorological data acquisition module, construction progress monitoring module and data analysis module to form a closed-loop management system from real-time monitoring to intelligent regulation. The construction progress monitoring module finely divides and dynamically manages the construction stage, and combines the correlation analysis of water flow monitoring and meteorological data to provide accurate prediction basis for construction plan. The data analysis module identifies the impact of water flow change on the progress and safety of specific construction stages through correlation influence model, and provides reliable decision support for construction scheduling. BRIEF DESCRIPTION OF DRAWINGS

[0047] In order to more clearly illustrate the technical solutions in the present application or the prior art, the drawings needed to be used in the embodiments or prior art description will be briefly introduced. Obviously, the drawings in the following description are only a part of the present application, and other drawings can be obtained by those skilled in the art without any creative effort based on these drawings.

[0048] Fig. 1 The management system function module schematic diagram of the embodiment of the present application;

[0049] Fig. 2 The correlation influence model schematic diagram of the embodiment of the present application. DETAILED DESCRIPTION

[0050] The present application will be described in detail below with reference to the drawings and specific embodiments. It should be noted that in order to make the embodiments more detailed, the following embodiments are the best, preferred embodiments, and other alternative ways can also be used by those skilled in the art to implement some known technologies; and the drawings are only used to more specifically describe the embodiments, and are not intended to specifically limit the present application.

[0051] It should be noted that in the specification, "one embodiment", "embodiment", "exemplary embodiment", "some embodiments" and the like indicate that the described embodiments can include specific features, structures or characteristics, but not necessarily every embodiment includes the specific features, structures or characteristics. In addition, when a specific feature, structure or characteristic is described in combination with an embodiment, it should be within the knowledge of those skilled in the related art to realize this feature, structure or characteristic in combination with other embodiments (whether or not explicitly described).

[0052] Generally, the terms can be understood at least in part from the context of their usage. For example, depending at least in part upon the context, the term "one or more" as used herein can be used to describe any feature, structure, or characteristic in the singular or can be used to describe combinations of features, structures, or characteristics. In addition, the term "based on" can be understood as not necessarily of a set of exclusive factors, but can instead be permitted to exist provided at least in part upon a context, allowing for the existence of other factors not explicitly described.

[0053] As Figs. 1-2 The water conservancy construction project management system based on data analysis includes:

[0054] Water flow monitoring module: real-time acquisition of water flow data in the construction area through sensors, including river flow rate, water flow, water level change;

[0055] The meteorological data acquisition module: through the establishment of data interface with the meteorological monitoring center, the future precipitation of the surrounding area is obtained, and the future precipitation is associated with the water flow data for analysis to predict the future water level change trend;

[0056] The construction progress monitoring module: a construction progress management unit is established, and the construction progress management unit divides the construction progress into multiple construction stages, which are uploaded by the site construction management personnel in real time;

[0057] The data analysis module: a correlation influence model between water flow and water level change trend and construction progress is established to identify the influence of water flow change trend on specific construction stages;

[0058] The adaptive water flow regulation module: according to the analysis results of the correlation influence model, water flow management measures are developed, including adjusting the water flow direction of the construction area and adjusting the water flow management equipment (such as water pump, gate, etc.) to cope with sudden water flow changes and ensure the stability of the construction environment.

[0059] The water flow monitoring module collects water flow data in the construction area in real time through multiple sensors and flow meters installed in the construction area. The sensors include water level sensors, flow rate sensors, and flow meters. The water level sensor is used to monitor the real-time change of the water level, the flow rate sensor is used to collect the flow rate data of the river in real time, and the flow meter is used to measure the change of the river water flow. The sensors and flow meters transmit the collected water flow data to the system data processing unit through wireless transmission for subsequent analysis and processing to support dynamic adjustment of water flow management and construction progress.

[0060] The data interface in the meteorological data acquisition module communicates with the data platform of the meteorological monitoring center through network connection to obtain precipitation prediction data within 24 to 72 hours. The meteorological data acquisition module correlates the future precipitation data with the water flow data collected by the water flow monitoring module, combines the water flow change trend and the precipitation data in the weather forecast, and uses a prediction algorithm to calculate the future water level change trend to provide a basis for water flow management and construction progress adjustment, and predict the impact of water level change in advance.

[0061] The correlation analysis between precipitation and water flow is represented as: Q(t) = α·P(t) + β, where Q(t) is the water flow (or flow rate) at time t, P(t) is the precipitation at time t, α is the coefficient of the influence of precipitation on water flow, representing the degree of influence of unit precipitation change on water flow, and β is a constant term representing the water flow without precipitation. Through regression analysis of historical data, the optimal values of α and β can be obtained;

[0062] By acquiring the future precipitation data: P(t+1), P(t+2), …, P(t+n), where n represents the time step of future prediction, in combination with the known water flow Q(t), a prediction algorithm is used to predict the water level change trend, and the prediction algorithm is represented as:

[0063] wherein H(t+n) is the predicted water level at time t+n, γ is the coefficient of flow and water level change, indicating the influence of unit flow on water level, is the cumulative of water flow in the future n time steps.

[0064] The construction progress monitoring module specifically includes:

[0065] Construction phase division: according to the overall plan of the construction project, the construction progress is divided into multiple construction phases, each construction phase includes corresponding construction tasks, resource allocation and estimated completion time, and the construction phase is divided according to construction parts (foundation construction, structure construction, decoration construction), construction content (excavation, concrete pouring, equipment installation) or time node;

[0066] Progress management platform: the construction progress management unit accesses the construction progress information in real time through an integrated digital platform, and the digital platform supports the construction management personnel to access the construction progress information in real time through mobile terminals, tablets or computers;

[0067] Real-time data uploading: the construction site management personnel uploads the actual progress data of each construction phase in real time through the digital platform, and the uploaded data includes the completion ratio, actual time and completed task details of the current construction phase;

[0068] The construction progress management unit generates a comparison report of actual progress and planned progress by analyzing the real-time uploaded data, and the report includes the progress deviation of each construction phase.

[0069] The influence of identifying the water flow change trend in the data analysis module on the specific construction phase includes the construction progress delay rate D s and the construction safety risk index R s , D s represents the progress delay rate of construction phase s, R s represents the safety risk index of construction phase s, and a correlation influence model of water level change trend H(t+n) and construction progress delay rate D s and construction safety risk index R s is established, the correlation influence model includes a progress delay rate model and a construction safety risk index model, and the progress delay and safety risk of the specific construction phase are quantitatively analyzed through the dynamic influence of water level change on the construction phase.

[0070] The progress delay rate model is represented as: D s= g1(H(t + n), T p , c s), where H(t + n) is the predicted water level change trend, T p s is the planned completion time of construction phase s, S c s is the characteristic parameter set of construction phase s, including:

[0071] Current task type (excavation foundation, concrete pouring);

[0072] Resource usage (machinery, manpower);

[0073] Duration urgency (whether the phase task is urgent);

[0074] g1 is a regression function obtained by model training;

[0075] Based on the progress delay rate model, the progress delay rate D s is calculated as: where,

[0076] T p s is the planned completion time, T a s is the actual completion time: T a s = T p s + k1·ΔH(t + n) + k2·S c , k1 is the sensitivity coefficient of water level change to time delay, representing the influence of unit water level change on construction delay, and k2 is the influence coefficient of construction phase characteristics on delay.

[0077] The construction safety risk index model is represented as: R s = g2(H(t + n), V s s), where V s s is the risk sensitivity parameter of construction phase s, g2 is a classification function obtained by training, based on the construction safety risk index model to calculate the construction safety risk index R s , quantifying the influence of water level change trend on construction phase safety hazards, calculated as:

[0078] R s = a1·ΔH(t + n) + a2·V s , where a1 is the sensitivity coefficient of water level change to safety risk (the risk value increased by 1 m of water level rise), a2 is the sensitivity coefficient of construction phase risk characteristics to safety risk, and V s is the risk sensitivity parameter of construction phase, including task exposure (such as whether personnel need to operate underwater), equipment type (such as whether heavy equipment is used); according to the calculated R s , the safety risk of construction phase is classified:

[0079] Rs ≤1: Low risk;

[0080] 1 < R s ≤3: Medium risk;

[0081] R s >3: High risk.

[0082] The specific training of g1, g2 and the selection of the training model are as follows:

[0083] 1. Data preparation:

[0084] 1.1 Water flow and water level change data:

[0085] Actual measured water level change trend H(t+n);

[0086] Historical precipitation, flow and flow rate data, associated with water level change trend.

[0087] 1.2. Construction phase characteristic data:

[0088] Scheduled completion time T p of each construction phase;

[0089] Actual completion time T a ;

[0090] Phase characteristic parameter S c and risk sensitivity parameter V s .

[0091] 1.3. Actual impact data:

[0092] Actual progress delay rate of each construction phase

[0093] Actual level of safety risk R s actual of the construction phase (derived from on-site safety event statistics or expert assessment).

[0094] In order to train g1 and g2, the data needs to be labeled:

[0095] Target variable for g1;

[0096] R s actual Target variable for g2.

[0097] 2. Feature engineering:

[0098] 2.1 Features of the progress delay rate model (g1):

[0099] Input features:

[0100] ΔH(t+n): predicted water level change amplitude;

[0101] T p : planned completion time;

[0102] S c : construction phase characteristic parameters.

[0103] Target variable:

[0104] 2.2 Characteristics of construction safety risk index model (g2):

[0105] Input features:

[0106] ΔH(t+n): predicted water level change amplitude;

[0107] V s : construction phase risk sensitivity parameters.

[0108] 2.3. Target variable: R s actual : generated by safety event statistics in historical data or expert evaluation.

[0109] 2.4, Feature preprocessing:

[0110] Normalize numerical features;

[0111] Encode categorical features (task type).

[0112] 3. Model selection:

[0113] The model type of progress delay rate model (g1) adopts time series model, which is used to capture time series characteristics;

[0114] Loss function:

[0115] Construction safety risk index model (g2) adopts support vector machine classification model;

[0116] The loss function is represented as:

[0117]

[0118] 4. Model training:

[0119] Data division: divide the dataset into training set (70%), validation set (15%) and test set (15%);

[0120] Optimize the hyperparameters of the model using grid search;

[0121] Training process: train g1 and g2 respectively, and iteratively optimize the model parameters based on the loss function.

[0122] Verification and testing: the performance of the model is evaluated by the verification set, and the generalization ability of the model is evaluated by the test set.

[0123] The adaptive water flow regulation module is based on D s And R s Determine the target priority and classification decision, the target priority determination includes:

[0124] If R s > 3: take safety measures to reduce the direct impact of water flow on the construction area;

[0125] If D s > 30% and R s ≤ 3: prioritize reducing the resistance of the drainage system or optimizing the direction of water flow to ensure construction progress.

[0126] The classification decision specifically includes:

[0127] D s ≤ 10%, R s ≤ 1, low impact: the water flow change has no obvious impact on construction, and the current water flow management equipment operation state is maintained;

[0128] 10% < D s ≤ 30%, 1 < R s ≤ 3, moderate impact: partially optimize the drainage system or adjust the direction of water flow to reduce potential impact;

[0129] D s > 30% or R s > 3, serious impact: comprehensively adjust the direction of water flow in the construction area and adjust the water flow management equipment to prioritize construction safety.

[0130] The classification decision details are applicable to hierarchical implementation strategies:

[0131] No large-scale action is needed for low impact, saving resources;

[0132] Limited optimization measures are taken for moderate impact;

[0133] Comprehensive adjustment and emergency measures are started for serious impact.

[0134] The present application covers any substitutions, modifications, equivalent methods and solutions made on the essence and scope of the present application. In order to make the public have a thorough understanding of the present application, specific details are described in the following preferred embodiments of the present application, and the present application can also be fully understood without the description of these details to those skilled in the art. In addition, in order to avoid unnecessary confusion to the essence of the present application, well-known methods, processes, procedures, elements and circuits, etc. are not described in detail.

[0135] The above description is only the preferred embodiment of the present application, it should be pointed out that for those skilled in the art, without departing from the principles of the present application, can make several improvements and refinements, these improvements and refinements should also be considered as the protection scope of the present application.

Claims

1. A water construction project management system based on data analysis, characterized in that, Comprise: Water flow monitoring module: real-time acquisition of water flow data in the construction area through sensors, including river flow rate, water flow, water level change; Weather data acquisition module: through the data interface with the meteorological monitoring center, the future precipitation around is obtained, and the future precipitation is associated with the water flow data for correlation analysis, and the future water level change trend is predicted; Construction progress monitoring module: establish a construction progress management unit, and divide the construction progress into multiple construction stages in the construction progress management unit, and the construction stage is uploaded by the site construction management personnel in real time; Data analysis module: establish a correlation influence model between water flow and water level change trend and construction progress, and identify the influence of water flow change trend on specific construction stage; Self-adaptive water flow regulation module: according to the analysis result of the correlation influence model, water flow management measures are formulated, including adjusting the water flow direction of the construction area, adjusting the water flow management equipment to cope with sudden water flow change, and ensuring the stability of the construction environment; The influence of the identified water flow change trend in the data analysis module on the specific construction stage includes construction progress delay rate D s and construction safety risk index R s , D s represents the progress delay rate of the construction stage s, R s represents the safety risk index of the construction stage s, the correlation influence model of the water level change trend H(t+n) and the construction progress delay rate D s and the construction safety risk index R s includes a progress delay rate model and a construction safety risk index model, and the dynamic influence of the water level change on the construction stage is quantitatively analyzed. The progress delay rate model is represented as: D s = g1(H(t + n), T p , S c ), wherein H(t + n) is a predicted water level change trend, T p is a planned completion time of a construction phase s, S c is a characteristic parameter set of the construction phase s, including a current task type, resource usage, and a schedule urgency, and g1 is a regression function obtained through model training. Based on the schedule delay rate model, the schedule delay rate D s is calculated as: where T p is the planned completion time, T a is the actual completion time, expressed as: T a = T p + k1 · ΔH(t + n) + k2 · S c , k1 is the sensitivity coefficient of water level change to time delay, indicating the influence of unit water level change on construction delay, and k2 is the influence coefficient of construction stage characteristics on delay. The construction safety risk index model is represented as: R s = g2(H(t+n), V s s), wherein V s s is a risk sensitivity parameter of the construction stage s, g2 is a classification function obtained by training, and the construction safety risk index R s is calculated based on the construction safety risk index model. The influence of the water level change trend on the safety hidden danger of the construction stage is quantified and calculated as: R s = a1*DeltaH(t+n) + a2*V s , wherein a1 is a sensitivity coefficient of water level change to safety risk, a2 is a sensitivity coefficient of construction phase risk characteristics to safety risk, V s is a risk sensitivity parameter of the construction phase, including task exposure degree, equipment type; according to the calculated R s , the safety risk of the construction phase is classified.

2. The data analysis based water construction project management system according to claim 1, wherein, The water flow monitoring module acquires water flow data in the construction area in real time through multiple sensors and flow meters installed in the construction area, the sensors include water level sensor, flow rate sensor and flow meter, the water level sensor is used to monitor the real-time change of water level, the flow rate sensor is used to acquire the flow rate data of river in real time, and the flow meter is used to measure the change of river water flow.

3. The data analysis based water construction project management system as claimed in claim 1, wherein, The data interface in the weather data acquisition module communicates with the data platform of the meteorological monitoring center through network connection mode, and obtains the precipitation prediction data within 24 to 72 hours in the future; The weather data acquisition module correlates the obtained future precipitation data with the water flow data collected by the water flow monitoring module, combines the water flow change trend and the precipitation data in the weather forecast, and calculates the future water level change trend by using a prediction algorithm.

4. The data analysis based water construction project management system as claimed in claim 3, wherein, The correlation analysis between the precipitation and the water flow is represented as: Q(t) = a P(t) + b, wherein Q(t) is the water flow at time t, P(t) is the precipitation at time t, a is the coefficient of the influence of the precipitation on the water flow, and b is a constant term representing the water flow without precipitation; By obtaining the future precipitation data: P(t+1), P(t+2), …, P(t+n), wherein n represents the time step of future prediction, and combining the known water flow Q(t), a prediction algorithm is used to predict the water level change trend, and the prediction algorithm is represented as: where H(t + n) is the predicted water level at time t + n, γ is the coefficient of flow and water level change, indicating the influence of unit flow on water level, is the cumulative water flow in the future n time steps.

5. The data analysis based water construction project management system as claimed in claim 1, wherein, The construction progress monitoring module specifically comprises: Construction stage division: according to the overall plan of the construction project, the construction progress is divided into multiple construction stages, each construction stage includes corresponding construction task, resource allocation and expected completion time, and the construction stage is divided according to construction part, construction content or time node; Progress management platform: the construction progress management unit is supported by an integrated digital platform, which allows construction management personnel to access construction progress information in real time through mobile terminals, tablets or computers; Real-time data uploading: the actual progress data of each construction stage is uploaded in real time by the construction site management personnel through the digital platform, and the uploaded data includes the completion ratio, actual time and completed task details of the current construction stage; The construction progress management unit generates a comparison report of the actual progress and the planned progress by analyzing the real-time uploaded data, and the report includes the progress deviation of each construction stage.

6. The data analysis based water construction project management system as claimed in claim 1, wherein, The R s The classification of the safety risk of the construction phase specifically includes: R s ≤1: low risk; 1 < R s ≤3: medium risk; R s >3: High risk.

7. The data analysis based water construction project management system as claimed in claim 6, wherein, The adaptive water flow regulation module is based on D s and R s determining target priorities and classification decisions, the target priority determination comprising: If R s >3: Take security measures to reduce the direct impact of water flow on the construction area; If D s > 30% and R s ≤ 3: Prioritize reducing the resistance of the drainage system or optimizing the direction of water flow with the goal of ensuring construction progress.

8. The data analysis based water construction project management system as claimed in claim 7, wherein, The classification decision specifically includes: D s ≤10%, R s ≤1, low impact: i.e. the water flow change has no significant impact on the construction, the current water flow management device operational state is maintained; 10% < D s ≤ 30%, 1 < R s ≤ 3, moderate impact: then partially optimize the drainage system or adjust the water flow direction, reducing the potential impact; D s >30% or R s >3, serious impact: overall adjustment of the flow direction of the construction area, adjustment of the water flow management equipment, and priority protection of construction safety.

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