Digital management method and system for water conservancy project construction process

Abstract

The present invention discloses a digital management method and system for a water conservancy project construction process, and belongs to the technical field of water conservancy projects. The method specifically comprises: real-time collection of relevant data from a water conservancy project construction site, and preprocessing, by comparing the actual construction progress with the planned progress, calculating the construction progress deviation, and analyzing the future construction progress trend, analyzing the construction quality data, training a prediction model through historical data, identifying construction quality hazards, combining the construction progress and quality, resource conditions and external environmental factors, intelligently scheduling construction resources, building a risk prediction model, and evaluating the risks in the construction process; in the context of complex water conservancy projects, it is possible to achieve process progress and quality management, reasonable resource scheduling and risk control, and better ensure the smooth progress of the project.

Description

Technical Field

[0001] The present invention belongs to the technical field of water conservancy projects, and more specifically, to a digital management method and system for the construction process of a water conservancy project. Background Art

[0002] The construction of water conservancy projects is a complex process that is highly dependent on resources and time, covering multiple stages from project planning, design, construction to operation. The project management of water conservancy projects usually involves a lot of resource coordination, construction period control, quality supervision, cost management and other aspects. With the rapid development of modern information technology and computer technology, the application of digital and intelligent technology in water conservancy projects has gradually become a key means to improve project management efficiency, reduce construction risks, ensure project quality and control costs.

[0003] However, the existing water conservancy project construction management methods usually have the following problems: 1) Information island phenomenon, information asymmetry and data islands between various management links; 2) The disconnection between progress and quality management. Traditional construction progress management and quality management are often carried out separately, lacking effective connection and coordination; 3) Dynamic scheduling of resources is difficult, and the scheduling process lacks real-time data support and intelligent optimization; 4) Risks are difficult to control and it is impossible to judge the risk situation during the construction process. Summary of the invention

[0004] In view of the shortcomings of the existing technology, the present invention proposes a digital management method and system for the construction process of water conservancy projects. By collecting, processing and analyzing various information at the construction site, it supports progress management, quality management, safety management and risk management in the construction process, ensuring that the project is completed on time, according to quality and on budget.

[0005] To achieve the above object, the present invention provides the following technical solutions: Digital management methods for water conservancy project construction process include: Collect relevant data from water conservancy project construction sites in real time and perform pre-processing; By comparing the actual construction progress with the planned progress, the construction progress deviation is calculated and the future construction progress trend is analyzed; Analyze construction quality data, train prediction models using historical data, and identify construction quality risks; Intelligently dispatch construction resources based on construction progress and quality, resource conditions and external environmental factors; Build a risk prediction model to evaluate the risks in the construction process.

[0006] Specifically, the actual construction progress is compared with the planned progress, the construction progress deviation is calculated, and the future construction progress trend is analyzed, including: According to the real-time situation of water conservancy project construction, the real-time actual construction progress is obtained, compared with the planned progress, and the construction progress deviation is calculated. The specific formula is: , Among them, P represents the construction progress deviation, n represents the number of construction tasks, and m represents the total number of factors. represents the actual completion time of the i-th construction task, represents the planned completion time of the i-th construction task, represents the planned start time of the i-th construction task, represents the weight of the kth adjustment factor, represents the value of the kth adjustment factor at time t; Use machine learning models to learn historical construction data and obtain task progress at future times; When the construction progress deviation exceeds the set threshold, the construction progress is judged to be lagging, the early warning mechanism is activated, the weight of the impact of each factor on the construction progress is updated, and the task progress at future moments is re-predicted for real-time feedback and dynamic adjustment.

[0007] Specifically, the analysis of construction quality data, training of prediction models through historical data, and identification of construction quality hazards include: Based on the construction quality data, a multi-dimensional analysis is performed to calculate the construction quality error. The specific formula is: , Among them, E represents the construction quality error, S represents the total number of adjustment factors, Indicates the dimensions and quality parameters measured during actual construction. Indicates the quality parameter design requirements, represents the weight of the sth adjustment factor, and Cs represents the sth adjustment factor; During the construction process, data is collected in real time to analyze the construction quality error. When the construction quality error is greater than 0, it is determined that there is a problem with the construction quality, a quality warning is issued, and the quality traceability process is initiated to analyze each construction task and adjustment factor.

[0008] Specifically, the intelligent scheduling of construction resources based on construction progress and quality, resource conditions and external environmental factors includes: Obtain external environmental factors, calculate the potential impact of external environmental factors on construction progress and resource requirements, and obtain external environmental factors; Set the objective function and constraints of resource scheduling to optimize resource allocation; Based on the construction progress and quality deviation, combined with external environmental factors, the demand for each resource in the next period of time is predicted. The specific formula is: , in, represents the scheduling amount of resource j at time t+1, represents the scheduling amount of resource j at time t, represents the scheduling adjustment rate of resource j, , and They represent the weights of progress deviation, quality deviation and external environmental factors on resource scheduling, represents the impact of the jth external environmental factor on resource scheduling; According to the resource scheduling plan, resources are actually allocated.

[0009] Specifically, the risk prediction model is constructed to evaluate the risks in the construction process, including: Identify risks in the construction process, describe and classify the identified risk factors, and establish a hierarchical model based on the classification and interrelationships of risk factors; Construct the judgment matrix R, perform eigenvalue decomposition on the judgment matrix R, and calculate its maximum eigenvalue And the corresponding eigenvector W, normalize the eigenvector W to get the normalized eigenvector ; The consistency of the judgment matrix is ​​tested and the consistency index CI is calculated. The specific formula is: , where ns represents the order of the judgment matrix. According to the order ns of the judgment matrix, the corresponding average random consistency index RI is found and the consistency ratio CR is calculated. CR=CI / RI. When CR<0.1, the judgment matrix is ​​considered to have satisfactory consistency. Otherwise, the judgment matrix needs to be readjusted until the consistency requirements are met. According to the actual situation of each risk factor in each construction area or operation link, score each risk factor to form a risk assessment matrix Fs, Fs=(fs ab ),fs ab It represents the score of the bth risk factor in the ath construction area or operation link; The normalized feature vector By multiplying it with the risk assessment matrix Fs, we can obtain the comprehensive risk score of each construction area or operation link and divide the risk level.

[0010] Specifically, the relevant data of the water conservancy project construction site include: construction progress data, the use of personnel, equipment and materials at the construction site, construction progress, and task completion; Environmental data, including meteorological data, soil conditions, and surrounding hydrological environment; Quality data: quality of building materials, construction accuracy, and construction errors; Safety data: safety conditions at construction sites, including safety inspections, risk assessments, and accident monitoring; The preprocessing includes: data cleaning, cleaning the collected data related to the water conservancy project construction site to eliminate noise data and abnormal values; standardization processing, converting data from different sources and in different formats into a unified data format.

[0011] A digital management system for a water conservancy project construction process, used to implement the digital management method for a water conservancy project construction process, comprising: a data acquisition module, a progress deviation analysis module, a quality deviation analysis module, an intelligent scheduling module and a risk prediction module; The data acquisition module is used to collect relevant data from the water conservancy project construction site in real time and perform preprocessing; The progress deviation analysis module is used to calculate the construction progress deviation by comparing the actual construction progress with the planned progress, and analyze the future construction progress trend; The quality deviation analysis module is used to analyze construction quality data, train a prediction model through historical data, and identify construction quality hazards; The intelligent scheduling module is used to intelligently schedule construction resources based on construction progress and quality, resource conditions and external environmental factors; The risk prediction module is used to construct a risk prediction model to evaluate the risks in the construction process.

[0012] Specifically, the intelligent scheduling module includes: a target determination unit and a resource allocation unit; The target determination unit is used to set the target function and constraint conditions of resource scheduling and optimize the allocation of resources; The resource allocation unit is used to predict the demand for each resource in the next time period based on the construction progress and quality deviation in combination with external environmental factors to obtain a resource scheduling plan.

[0013] Specifically, the risk prediction module includes: a risk identification unit, a judgment matrix unit and a risk calculation unit; The risk identification unit is used to identify risks during the construction process; The judgment matrix unit is used to construct a judgment matrix and perform consistency check; The risk calculation unit is used to calculate the comprehensive risk score according to the actual situation of each risk factor in each construction area or operation link.

[0014] Compared with the prior art, the present invention has the following beneficial effects: 1. The present invention proposes a digital management method for the construction process of water conservancy projects, which can dynamically adjust and optimize the construction progress. According to the real-time collected data and analysis results, it can automatically generate a variety of progress adjustment plans, simulate and evaluate each plan, select the optimal plan, and ensure the smooth progress of the project.

[0015] 2. The present invention proposes a digital management method for the construction process of water conservancy projects, which breaks the information island, realizes efficient communication and collaborative management among various units, ensures that various tasks are carried out in an orderly manner, and tracks the execution of tasks in real time, and promptly feedbacks on task progress and quality issues, thereby promoting collaborative cooperation and responsibility implementation among units.

[0016] 3. The present invention proposes a digital management method for the construction process of water conservancy projects, which comprehensively considers various factors such as environmental risks, operational risks, equipment risks, etc. in the construction of water conservancy projects, calculates the comprehensive risk score of each construction area or operation link, divides the risk level according to the score, and sets the corresponding early warning threshold. When the risk level rises to the preset threshold, the safety warning is automatically triggered, and the emergency command system is linked to generate and push the emergency response plan. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 A flow chart of the digital management method for the water conservancy project construction process provided by the present invention; Figure 2 This is an architecture diagram of the digital management system for the water conservancy project construction process provided by the present invention. DETAILED DESCRIPTION

[0018] The present application is described in detail below in conjunction with specific embodiments. The following embodiments will help those skilled in the art to further understand the present application, but do not limit the present application in any form. It should be noted that, for those of ordinary skill in the art, several variations and improvements can also be made without departing from the concept of the present application. These all belong to the protection scope of the present application.

[0019] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0020] It should be noted that, if there is no conflict, the various features in the embodiments of the present application can be combined with each other and are all within the scope of protection of the present application. In addition, although the functional modules are divided in the device schematic diagram and the logical order is shown in the flow chart, in some cases, the steps shown or described can be performed in a different order than the module division in the device or the order in the flow chart. In addition, the " The words "first", "second", "third", etc. do not limit the data and execution order, but only distinguish the same items or similar items with basically the same functions and effects.

[0021] Unless otherwise defined, all technical and scientific terms used in this specification have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used in this specification and in the specification of this application are only for the purpose of describing specific embodiments and are not intended to limit this application. The term "and / or" used in this specification includes any and all combinations of one or more of the related listed items.

[0022] Example 1 See also Figure 1 , an embodiment provided by the present invention: a digital intelligent management method for a water conservancy project construction process, comprising the following specific steps: Step S1: real-time collection of relevant data from the water conservancy project construction site and pre-processing; The relevant data of the water conservancy project construction site include: construction progress data, the use of personnel, equipment and materials at the construction site, construction progress, task completion, etc.; environmental data, meteorological data (such as temperature, humidity, wind speed, etc.), soil conditions, surrounding hydrological environment, etc.; quality data: the quality of building materials, construction accuracy, construction errors, etc.; safety data: the safety situation of the construction site, including safety inspections, risk assessments, accident monitoring, etc.

[0023] Data preprocessing includes: data cleaning, cleaning the collected data related to the water conservancy project construction site to eliminate noise data and outliers; standardization processing, converting data from different sources and in different formats into a unified data format to facilitate subsequent analysis.

[0024] Step S2: By comparing the actual construction progress with the planned progress, the construction progress deviation is calculated, and the future construction progress trend is analyzed; The specific steps of step S2 are: Step S201: According to the real-time situation of the water conservancy project construction, the real-time actual construction progress is obtained, compared with the planned progress, and the construction progress deviation is calculated. The specific formula is: , Among them, P represents the construction progress deviation, n represents the number of construction tasks, and m represents the total number of factors. represents the actual completion time of the i-th construction task, represents the planned completion time of the i-th construction task, represents the planned start time of the i-th construction task, represents the weight of the kth adjustment factor, describing the influence of this factor on the construction progress. It represents the value of the kth adjustment factor at time t, representing the dynamic impact of different factors on the progress; In this embodiment, common adjustment factors include: It represents the resource deviation factor, which takes into account the deviation impact of resources such as manpower, machinery, and materials, and is usually calculated from the difference between the resource utilization rate and the planned utilization rate; Represents environmental factors, taking into account the impact of weather and emergencies, and can estimate the degree of delay in construction progress by comparing real-time weather data (such as precipitation, temperature, wind speed) with historical data; It represents process factors, taking into account the impact of changes in construction technology, changes in process difficulty, design changes and other factors on the progress. represents the time lag factor, taking into account the temporal interdependence between tasks. For example, the delay of task i may cause the delay of task i+1; The principle of this formula is to measure and adjust the deviation of task progress by comprehensively considering the influence of multiple factors. Specifically, first calculate the difference between the actual completion time of the task and the planned completion time, then make weighted adjustments based on multiple influencing factors (such as resources, weather, and technology), and finally standardize the weighted progress deviation with the time span of the task to obtain a relative progress deviation percentage. The item is used to standardize the progress deviation and make it a relative ratio. By comparing the progress deviation with the task plan time span, the progress deviation distortion caused by the different task sizes can be avoided. The standardized result allows the progress deviations of tasks of different sizes to be compared under the same standard. Measure the gap between the actual completion of tasks and the planned completion. Dynamically changing factors that affect the construction progress are introduced, taking into account multiple factors such as resources, environment, and technology; through this formula, the progress deviation of the construction task can be accurately and real-time reflected, combined with dynamic multi-factor analysis and machine learning optimization to ensure that the construction project can be flexibly adjusted under different influencing factors, achieving more efficient and accurate construction progress management.

[0025] Step S202: Using a machine learning model to learn historical construction data to obtain task progress at a future time; Step S203: When the construction progress deviation exceeds the set threshold, the construction progress is determined to be lagging, the early warning mechanism is activated, the weight of each factor affecting the construction progress is updated, and the task progress at future moments is re-predicted for real-time feedback and dynamic adjustment.

[0026] In this embodiment, if a resource is in short supply, resource allocation is automatically adjusted; if there is an unexpected environmental change (such as worsening weather), the construction plan is automatically adjusted to postpone certain tasks or speed up certain tasks.

[0027] Step S3: Analyze construction quality data, train prediction models through historical data, and identify construction quality hazards; The specific steps of step S3 are: Step S301: Based on the construction quality data, a multi-dimensional analysis is performed to calculate the construction quality error. The specific formula is: , Among them, E represents the construction quality error. If E is greater than 0, it means that the error exceeds the design standard and may affect the quality. If E is less than 0, it means that the quality is qualified. S represents the total number of adjustment factors. Indicates the size, quality or other parameters measured during actual construction. Indicates the quality parameter design requirements, represents the weight of the sth adjustment factor, describing the influence of this factor on the quality error, and Cs represents the sth adjustment factor; In this embodiment, the adjustment factors include: C1: construction error factor, which takes into account the errors caused by factors such as operators and equipment accuracy during the construction process; C2: material quality factor, which indicates the impact of quality differences in building materials on construction quality; C3: environmental factors, which takes into account the impact of factors such as climate, humidity, and temperature on construction quality. For example, if C1 is an error caused by improper operation of construction personnel or insufficient equipment accuracy, C1 is a positive value; if C2 is a material quality problem, C2 is a positive value; if C3 is a quality problem caused by changes in the external environment, C3 is a positive value.

[0028] The principle of this formula is based on weighted error analysis, which calculates the construction quality error by weighted summation of multi-dimensional influencing factors, combining two parts: direct error This part is the absolute deviation between the actual quality of the task and the design standard, representing the traditional quality error analysis part, such as the deviation of structural dimensions, the deviation of concrete strength, etc.; dynamic quality influencing factor This part takes into account various factors that affect the quality during the construction process and uses weight coefficients To reflect the impact of each factor on quality, these factors are not only physical parameters (such as temperature and humidity, material quality), but also include construction resources, process difficulty, etc., thereby improving the comprehensiveness and accuracy of quality analysis.

[0029] Step S302: During the construction process, real-time data is collected to analyze the construction quality error. When the construction quality error is greater than 0, it is determined that there is a problem with the construction quality, a quality warning is issued, and the quality traceability process is started. Analyze each construction task and adjustment factor.

[0030] By using blockchain technology, all kinds of quality data during the construction process are stored on the chain to ensure that the data cannot be tampered with. By tracing back the data chain, the links, responsible persons and related operation records where quality problems occur can be accurately located, facilitating timely rectification and accountability.

[0031] Step S4: intelligently dispatch construction resources based on construction progress and quality, resource conditions and external environmental factors; The specific steps of step S4 are: Step S401: Obtain external environmental factors (such as precipitation, temperature, soil moisture, etc.), calculate the potential impact of external environmental factors on construction progress and resource requirements, and obtain external environmental factors; Step S402: setting the objective function and constraints of resource scheduling to optimize resource allocation; The specific formula of the objective function is: , where min represents the minimum value function, and Z represents the total cost objective function. represents the total time cost, i.e. the total time required to complete the entire construction project, Represents the total quality cost, i.e. the cost caused by construction progress or construction quality problems (such as defects, rework, etc.), Represents resource cost, which is the cost of using construction resources (such as labor, machinery and equipment, building materials, etc.). Represents environmental cost, that is, the impact of external environment (such as weather, soil conditions, etc.) on construction, resulting in additional costs. , , and It represents the weight coefficient, that is, the importance of each cost item in the total cost. The weight coefficient is set according to the actual situation and reflects the decision-making preference of the construction manager; Constraints include progress and quality constraints, environmental constraints and resource constraints. Resource constraints: the resources required for each task cannot exceed the available resources; environmental constraints: such as weather factors, soil conditions, construction time, etc.; progress and quality constraints: ensure that each task is completed within the specified time and meets the minimum quality requirements; Step S403: Based on the construction progress and quality deviation, combined with external environmental factors, the demand for each resource in the next time period is predicted. The specific formula is: , in, represents the scheduling amount of resource j at time t+1, represents the scheduling amount of resource j at time t, represents the scheduling adjustment rate of resource j, , and They represent the weights of progress deviation, quality deviation and external environmental factors on resource scheduling, represents the impact of the jth external environmental factor (such as climate change, soil moisture, etc.) on resource scheduling; Step S404: actually allocating resources according to the resource scheduling plan.

[0032] Step S5: Construct a risk prediction model to evaluate the risks in the construction process.

[0033] The specific steps of step S5 are: Step S501: Identify risks in the construction process, describe and classify the identified risk factors, and establish a hierarchical model based on the classification and interrelationships of the risk factors; Encyclopedia of risks during construction: environmental risks (natural disaster risks such as floods and mudslides), operational risks (falling from heights, mechanical injuries, etc.), equipment risks (special equipment failure risks), management risks (such as imperfect safety management systems, inadequate safety training, etc.), personnel risks (such as poor physical condition of construction workers, illegal operations, etc.), social and environmental risks (such as interference from surrounding residents, social security issues, etc.), etc.; For example, the flood risk can be further subdivided into sub-factors such as the possibility of flood occurrence, flood water level, and flood inundation range; the height-fall risk can be subdivided into sub-factors such as the stability of the high-altitude working platform, the use of safety belts, and the safety awareness of workers. By refining the risk factors, the risk can be assessed more accurately. The water conservancy project construction safety risk assessment is taken as the target layer, environmental risk, operational risk, equipment risk, management risk, personnel risk, social environmental risk, etc. are taken as the criterion layer, and the specific risk factors under each criterion layer are taken as the indicator layer. For example, under the environmental risk criterion layer, floods, mudslides, earthquakes, etc. are indicator layer risk factors; under the operational risk criterion layer, high-altitude falls, mechanical injuries, electric shocks, etc. are indicator layer risk factors. The hierarchical model clearly shows the hierarchical relationship between risk factors and provides a framework for subsequent weight determination and risk assessment.

[0034] Step S502: Construct a judgment matrix R, perform eigenvalue decomposition on the judgment matrix R, and calculate its maximum eigenvalue And the corresponding eigenvector W, normalize the eigenvector W to get the normalized eigenvector ; Normalized feature vector That is, the weight vector of each risk factor; In this embodiment, the 1-9 scaling method is used for value assignment. For example, for the flood risk and debris flow risk under the environmental risk criterion layer, if the flood risk is considered to be slightly more important than the debris flow risk, the value is assigned to 3; if the flood risk is considered to be significantly more important than the debris flow risk, the value is assigned to 5; if the flood risk is considered to be extremely important than the debris flow risk, the value is assigned to 9. The judgment matrix R of the environmental risk criterion layer relative to the target layer is: , where r 12 =3 means that the first factor in environmental risk (assuming flood risk) is 3 more important than the second factor (assuming debris flow risk). 21 =1 / 3 means that the importance of debris flow risk relative to flood risk is 1 / 3, and so on, to construct judgment matrices at each level.

[0035] Taking the above judgment matrix as an example, the maximum eigenvalue can be obtained by calculation =3.0092, the corresponding eigenvector , zz represents the transpose of the vector. After normalization, the weight vector of each factor under the environmental risk criterion layer is obtained as follows: , that is, the weight of flood risk is 0.54, the weight of mudslide risk is 0.16, and the weight of earthquake risk is 0.30 (assuming that earthquake risk is the third factor).

[0036] Step S503: Check the consistency of the judgment matrix and calculate the consistency index CI. The specific formula is: , where ns represents the order of the judgment matrix. According to the order ns of the judgment matrix, the corresponding average random consistency index RI is found and the consistency ratio CR is calculated. CR=CI / RI. When CR<0.1, the judgment matrix is ​​considered to have satisfactory consistency. Otherwise, the judgment matrix needs to be readjusted until the consistency requirements are met. In this embodiment, for the judgment matrix of the above environmental risk criterion layer, , when ns=3, RI=0.58, then CR=0.0046 / 0.58=0.0079<0.1, indicating that the judgment matrix has satisfactory consistency and the calculated weight vector is reliable; Step S504: Score each risk factor according to the actual situation of each risk factor in each construction area or operation link to form a risk assessment matrix Fs, Fs=(fs ab ),fs ab It represents the score of the bth risk factor in the ath construction area or operation link; Step S505: normalize the feature vector By multiplying it with the risk assessment matrix Fs, we can obtain the comprehensive risk score of each construction area or operation link and divide the risk level.

[0037] In this example, it is assumed that there are three risk factors in a construction area, and the weight vector , risk assessment matrix , the comprehensive risk score of each construction area or operation link = By calculating the comprehensive risk score of each construction area or operation link, the safety risks of different areas or links can be quantitatively compared, providing a basis for safety warning and management decision-making; The risk level is divided into four levels: low risk, medium risk, high risk and extremely high risk. For example, the comprehensive risk score is set at 0-2 points for low risk, indicating that the safety risk of the construction area or operation link is low, and only routine safety management and monitoring are required; 2-4 points for medium risk, indicating that there is a certain safety risk, and it is necessary to strengthen safety inspections and management measures; 4-6 points for high risk, indicating that the safety risk is relatively high, and targeted risk control measures need to be taken, and on-site supervision needs to be strengthened; 6 points or more for extremely high risk, indicating that the safety risk is very high, and work must be stopped immediately for rectification until the risk is reduced to an acceptable level.

[0038] Example 2 See also Figure 2 , another embodiment provided by the present invention: a digital intelligent management system for a water conservancy project construction process, comprising: a data acquisition module, a progress deviation analysis module, a quality deviation analysis module, an intelligent scheduling module and a risk prediction module; The data acquisition module is used to collect relevant data from the water conservancy project construction site in real time and perform preprocessing; The progress deviation analysis module is used to calculate the construction progress deviation by comparing the actual construction progress with the planned progress, and analyze the future construction progress trend; The quality deviation analysis module is used to analyze construction quality data, train a prediction model through historical data, and identify construction quality hazards; The intelligent scheduling module is used to intelligently schedule construction resources based on construction progress and quality, resource conditions and external environmental factors; The risk prediction module is used to construct a risk prediction model to evaluate the risks in the construction process.

[0039] Intelligent scheduling module, including: a target determination unit and a resource allocation unit; The target determination unit is used to set the target function and constraint conditions of resource scheduling and optimize the allocation of resources; The resource allocation unit is used to predict the demand for each resource in the next time period based on the construction progress and quality deviation in combination with external environmental factors to obtain a resource scheduling plan.

[0040] The risk prediction module includes: a risk identification unit, a judgment matrix unit and a risk calculation unit; The risk identification unit is used to identify risks during the construction process; The judgment matrix unit is used to construct a judgment matrix and perform consistency check; The risk calculation unit is used to calculate the comprehensive risk score according to the actual situation of each risk factor in each construction area or operation link.

[0041] In addition, the parts of the above-mentioned technical solutions provided in the embodiments of the present application that are consistent with the implementation principles of the corresponding technical solutions in the prior art are not described in detail to avoid excessive redundancy.

[0042] The specific implementation modes as described above further describe the purpose, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation mode of the present invention and is not intended to limit the present invention. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A digital management method for the construction process of a water conservancy project, characterized in that: include: Collect relevant data from water conservancy project construction sites in real time and perform pre-processing; By comparing the actual construction progress with the planned progress, the construction progress deviation is calculated and the future construction progress trend is analyzed; Analyze construction quality data, train prediction models using historical data, and identify construction quality risks; Intelligently dispatch construction resources based on construction progress and quality, resource conditions and external environmental factors; Build a risk prediction model to evaluate the risks in the construction process.

2. The digital intelligent management method for the water conservancy project construction process according to claim 1, characterized in that: By comparing the actual construction progress with the planned progress, calculating the construction progress deviation, and analyzing the future construction progress trend, the following are included: According to the real-time situation of water conservancy project construction, the real-time actual construction progress is obtained, compared with the planned progress, and the construction progress deviation is calculated. The specific formula is: ， Among them, P represents the construction progress deviation, n represents the number of construction tasks, and m represents the total number of factors. represents the actual completion time of the i-th construction task, represents the planned completion time of the i-th construction task, represents the planned start time of the i-th construction task, represents the weight of the kth adjustment factor, represents the value of the kth adjustment factor at time t; Use machine learning models to learn historical construction data and obtain task progress at future times; When the construction progress deviation exceeds the set threshold, the construction progress is judged to be lagging, the early warning mechanism is activated, the weight of the impact of each factor on the construction progress is updated, and the task progress at future moments is re-predicted for real-time feedback and dynamic adjustment.

3. The digital intelligent management method for the water conservancy project construction process according to claim 2 is characterized in that: The analysis of construction quality data, training of prediction models through historical data, and identification of construction quality hazards include: Based on the construction quality data, a multi-dimensional analysis is performed to calculate the construction quality error. The specific formula is: ， Among them, E represents the construction quality error, S represents the total number of adjustment factors, Indicates the dimensions and quality parameters measured during actual construction. Indicates the quality parameter design requirements, represents the weight of the sth adjustment factor, and Cs represents the sth adjustment factor; During the construction process, data is collected in real time to analyze the construction quality error. When the construction quality error is greater than 0, it is determined that there is a problem with the construction quality, a quality warning is issued, and the quality traceability process is initiated to analyze each construction task and adjustment factor.

4. The digital intelligent management method for the water conservancy project construction process according to claim 3 is characterized in that: The intelligent scheduling of construction resources based on construction progress and quality, resource conditions and external environmental factors includes: Obtain external environmental factors, calculate the potential impact of external environmental factors on construction progress and resource requirements, and obtain external environmental factors; Set the objective function and constraints of resource scheduling to optimize resource allocation; Based on the construction progress and quality deviation, combined with external environmental factors, the demand for each resource in the next period of time is predicted. The specific formula is: ， in, represents the scheduling amount of resource j at time t+1, represents the scheduling amount of resource j at time t, represents the scheduling adjustment rate of resource j, , and They represent the weights of progress deviation, quality deviation and external environmental factors on resource scheduling, represents the impact of the jth external environmental factor on resource scheduling; According to the resource scheduling plan, resources are actually allocated.

5. The digital intelligent management method for the water conservancy project construction process according to claim 4 is characterized in that: The risk prediction model is constructed to evaluate the risks in the construction process, including: Identify risks in the construction process, describe and classify the identified risk factors, and establish a hierarchical model based on the classification and interrelationships of risk factors; Construct the judgment matrix R, perform eigenvalue decomposition on the judgment matrix R, and calculate its maximum eigenvalue And the corresponding eigenvector W, normalize the eigenvector W to get the normalized eigenvector ; The consistency of the judgment matrix is ​​tested and the consistency index CI is calculated. The specific formula is: , where ns represents the order of the judgment matrix. According to the order ns of the judgment matrix, the corresponding average random consistency index RI is found and the consistency ratio CR is calculated. CR=CI / RI. When CR<0.1, the judgment matrix is ​​considered to have satisfactory consistency. Otherwise, the judgment matrix needs to be readjusted until the consistency requirements are met. According to the actual situation of each risk factor in each construction area or operation link, score each risk factor to form a risk assessment matrix Fs, Fs=(fs ab ),fs ab It represents the score of the bth risk factor in the ath construction area or operation link; The normalized feature vector By multiplying it with the risk assessment matrix Fs, we can obtain the comprehensive risk score of each construction area or operation link and divide the risk level.

6. The digital intelligent management method for the water conservancy project construction process according to claim 5, characterized in that: The relevant data of the water conservancy project construction site include: construction progress data, the use of personnel, equipment and materials at the construction site, construction progress, and task completion; Environmental data, including meteorological data, soil conditions, and surrounding hydrological environment; Quality data: quality of building materials, construction accuracy, and construction errors; Safety data: safety conditions at the construction site, including safety inspections, risk assessments, and accident monitoring; The preprocessing includes: data cleaning, cleaning the collected data related to the water conservancy project construction site to eliminate noise data and abnormal values; standardization processing, converting data from different sources and in different formats into a unified data format.

7. A digital intelligent management system for a water conservancy project construction process, used to implement the digital intelligent management method for a water conservancy project construction process as described in any one of claims 1 to 6, characterized in that: include: Data acquisition module, progress deviation analysis module, quality deviation analysis module, intelligent scheduling module and risk prediction module; The data acquisition module is used to collect relevant data from the water conservancy project construction site in real time and perform preprocessing; The progress deviation analysis module is used to calculate the construction progress deviation by comparing the actual construction progress with the planned progress, and analyze the future construction progress trend; The quality deviation analysis module is used to analyze construction quality data, train a prediction model through historical data, and identify construction quality hazards; The intelligent scheduling module is used to intelligently schedule construction resources based on construction progress and quality, resource conditions and external environmental factors; The risk prediction module is used to construct a risk prediction model to evaluate the risks in the construction process.

8. The digital intelligent management system for the water conservancy project construction process according to claim 7, characterized in that: The intelligent scheduling module includes: a target determination unit and a resource allocation unit; The target determination unit is used to set the target function and constraint conditions of resource scheduling and optimize the allocation of resources; The resource allocation unit is used to predict the demand for each resource in the next time period based on the construction progress and quality deviation in combination with external environmental factors to obtain a resource scheduling plan.

9. The digital intelligent management system for the water conservancy project construction process according to claim 8, characterized in that: The risk prediction module includes: a risk identification unit, a judgment matrix unit and a risk calculation unit; The risk identification unit is used to identify risks during the construction process; The judgment matrix unit is used to construct a judgment matrix and perform consistency check; The risk calculation unit is used to calculate the comprehensive risk score according to the actual situation of each risk factor in each construction area or operation link.

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