Comprehensive performance evaluation and early warning system for water conservancy project

Abstract

The invention relates to the technical field, in particular to a hydraulic engineering comprehensive performance evaluation and early warning system. According to the technical scheme, the system comprises a data processing module, an evaluation index construction module, an evaluation method module and an early warning model establishment and training module, the processed data are input into the evaluation index building module, key factor data influencing the comprehensive performance of the water conservancy project are determined through the evaluation process and result analysis of the evaluation method module, and the data processed through the steps are input into the early warning model building and training module. Potential risks are found in time through cooperation of the structures, early warning is made in advance, preventive measures are taken, and therefore safe operation and long-term stability of a project are guaranteed.

Description

Technical Field

[0001] The present invention relates to the technical field, and in particular to a comprehensive performance evaluation and early warning system for water conservancy projects. Background Art

[0002] Water conservancy projects are important facilities for providing clean drinking water and are vital to ensuring the drinking water safety of the people. Water conservancy projects provide irrigation water for agricultural production and play a fundamental role in ensuring food security and sustainable agricultural development. Water conservancy projects provide water resources for industrial production, residents' lives, ecological environment, etc., and are important infrastructure to support economic and social development.

[0003] With the advancement of science and technology, water conservancy projects are constantly being updated. New technologies such as automation, information technology, and intelligent technology are gradually being applied to water conservancy project construction and management. Currently, the management models and systems of many water conservancy management departments may still remain at the traditional stage, suffering from low management efficiency and incomplete institutional mechanisms, and failing to adapt to new management needs and challenges in a timely manner. This lag in management systems has led to low management efficiency and even management loopholes.

[0004] In terms of deep water detection of major water conservancy project dams and emergency monitoring and early warning, current technologies and applications still have certain shortcomings, and more advanced technologies and methods are needed to improve the accuracy and efficiency of the early warning system.

[0005] To this end, we propose a comprehensive performance evaluation and early warning system for water conservancy projects to solve the existing problems. Summary of the Invention

[0006] The purpose of the present invention is to address the problems existing in the background technology and to propose a comprehensive performance evaluation and early warning system for water conservancy projects.

[0007] To achieve the above-mentioned purpose, the present invention provides the following technical solutions: a comprehensive performance evaluation and early warning system for water conservancy projects, comprising a data processing module, an evaluation index construction module, an evaluation method module and an early warning model establishment and training module. The data processing module processes the source data of the water conservancy project, and the processed data is input into the evaluation index construction module. The evaluation process and result analysis of the evaluation method module establish the key factor data affecting the comprehensive performance of the water conservancy project. The data processed through the above steps is input into the early warning model establishment and training module to timely discover potential risks, make early warnings and take preventive measures, thereby ensuring the safe operation and long-term stability of the project.

[0008] Preferably, the data processing module includes data collection and data processing, and the data collection includes monitoring data, engineering data, socio-economic data and environmental data. The data collection methods include sensor monitoring, manual inspection, remote sensing technology and archive query. The collection frequency is determined according to the data type and importance. Real-time monitoring data may need to be collected continuously or at a high frequency, while other data may be collected monthly, quarterly or annually.

[0009] Preferably, the data processing includes deleting data that is obviously erroneous, abnormal or incomplete, filling missing values through interpolation, averaging, regression and other methods, ensuring that all data use the same unit of measurement, normalizing or standardizing the data to make them comparable, combining data from different sources, such as monitoring data and remote sensing data, and integrating data from different time and space scales into a unified spatiotemporal framework.

[0010] Preferably, the evaluation index construction module includes evaluation index selection, index weight determination, evaluation index selection includes technical performance index, economic performance index, environmental impact index and social benefit index, when selecting index, should be appropriately adjusted and supplemented according to the characteristics of the specific project and the evaluation purpose, index weight determination establishes a judgment matrix, for each level, it is necessary to establish a judgment matrix according to the relative importance between the elements in the level. For example, in the criterion layer, it is necessary to compare the degree of influence of each criterion on the target, and construct a corresponding judgment matrix, and determine the element value in the matrix by expert scoring. Scoring is usually based on the knowledge, experience and judgment of experts. Commonly used scoring scales include 1-9 scales, 1-5 scales, etc., and mathematical methods are used to calculate the maximum eigenvalue of the judgment matrix and its corresponding eigenvector. The eigenvector is the weight of each element, and the judgment matrix is subjected to consistency test, usually using consistency index (Consistency Index, CI) and random consistency ratio (Random Index, RI) to test. If CR (CI / RI) is less than 0.1, it is considered that the consistency of the judgment matrix is acceptable. Finally, according to the characteristics of water conservancy projects, a comprehensive performance evaluation model is constructed.

[0011] Preferably, the mathematical method adopts the eigenvalue method to calculate, establish a judgment matrix, calculate the eigenvalues and eigenvectors of the judgment matrix, normalize the eigenvectors, perform consistency checks to ensure the rationality and consistency of the expert scores, and finally determine the weights by solving the eigenvalues and eigenvectors of the judgment matrix.

[0012] Preferably, the evaluation method module adopts a fuzzy comprehensive evaluation method, the steps of which include determining an evaluation factor set, establishing a comment set, determining a weight set, establishing a fuzzy relationship matrix, fuzzy synthesis, and finally defuzzifying the comprehensive evaluation result to obtain a specific evaluation result.

[0013] Preferably, the early warning model establishment and training module includes model establishment and model training. The model establishment adopts an autoregressive moving average model. When the autoregressive moving average model is trained, the processed data is input, and then the order (p and q) of the ARMA model is determined. The optimization algorithm (such as gradient descent, Newton method, etc.) is used to optimize the parameters to minimize the residual, ensure the convergence of the optimization algorithm, and stabilize the parameter estimation. Check whether the model residual meets the white noise assumption. The autocorrelation diagram of the residual and the Ljung-Box Q test can be used to check the degree of fit of the model, such as by calculating the adjusted R square value and using a part of the data as a test set to evaluate the predictive ability of the model. If the amount of data is large enough, cross-validation can be used to evaluate the performance of the verification model. The model can be used for prediction after verification, such as predicting a single value in the future and providing a confidence interval for the predicted value. Through the above steps, the training of the ARMA model can be completed and applied to actual time series prediction tasks.

[0014] Compared with the prior art, the present invention has the following beneficial effects:

[0015] During use, the comprehensive performance evaluation and early warning system for water conservancy projects of the present invention

[0016] Ensure project safety: Through comprehensive evaluation of the performance of water conservancy projects, potential safety hazards can be discovered in a timely manner, ensuring the stability of the project structure and preventing accidents;

[0017] Optimize resource allocation: The evaluation system can guide the rational allocation of water resources, improve resource utilization efficiency, and avoid resource waste;

[0018] Improve management level: Through evaluation and early warning, promote the modernization of water conservancy project management system and improve management efficiency and level;

[0019] Support decision-making: Provide scientific and accurate decision-making support to policy makers and managers to improve the pertinence and effectiveness of decisions;

[0020] In summary, the comprehensive performance evaluation and early warning system of water conservancy projects has important purposes and far-reaching significance in ensuring the safe operation of water conservancy projects, improving management efficiency, promoting the rational use of resources, and supporting scientific decision-making. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 It is a schematic diagram of the process of the present invention; DETAILED DESCRIPTION

[0022] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 making creative efforts are within the scope of protection of the present invention.

[0023] Example 1

[0024] like Figure 1 As shown, the present invention proposes a water conservancy project comprehensive performance evaluation and early warning system, including a data processing module, an evaluation index construction module, an evaluation method module and an early warning model establishment and training module. The data processing module processes the source data of the water conservancy project, and the processed data is input into the evaluation index construction module. Through the evaluation process and result analysis of the evaluation method module, the key factor data affecting the comprehensive performance of the water conservancy project is established. The data processed through the above steps is input into the early warning model establishment and training module to timely discover potential risks, make early warnings and take preventive measures, thereby ensuring the safe operation and long-term stability of the project.

[0025] Example 2

[0026] like Figure 1 As shown, the present invention proposes a water conservancy project comprehensive performance evaluation and early warning system. Compared with the first embodiment, this embodiment further includes: a data processing module including data collection and data processing, the data collection includes monitoring data such as water level, flow, rainfall, soil moisture content, structural stress, displacement and other real-time monitoring data, engineering data such as design data, construction records, operation and maintenance records, inspection reports, etc., socio-economic data such as population, economy, land use and other information of the affected area, and environmental data such as water quality, ecology, climate and other information of the reservoir area and upstream and downstream areas;

[0027] Data collection methods include sensor monitoring, such as using various sensors to automatically collect data; manual inspections, such as recording project status and related data through regular manual inspections; remote sensing technology, such as using satellite remote sensing, aerial remote sensing and other technologies to obtain a wide range of environmental data; and archive inquiries, such as querying historical archive materials from relevant management departments and units. The collection frequency is determined based on the type of data and importance. Real-time monitoring data may need to be collected continuously or at a high frequency, while other data may be collected monthly, quarterly or annually.

[0028] Data processing includes deleting obviously erroneous, abnormal or incomplete data, filling missing values through interpolation, averaging, regression and other methods, ensuring that all data use the same unit of measurement, normalizing or standardizing the data to make them comparable, combining data from different sources, such as monitoring data and remote sensing data, integrating data from different time and spatial scales into a unified space-time framework, and then calculating statistical quantities such as mean, variance, and correlation through statistical analysis. Time series analysis is used to analyze the changing trends of data over time, and spatial analysis is used to analyze the spatial distribution characteristics and mutual relationships of data to complete the data analysis. According to the characteristics of water conservancy projects, a comprehensive performance evaluation model, namely the fuzzy comprehensive evaluation method, is constructed.

[0029] The evaluation indicator construction module includes the selection of evaluation indicators and the determination of indicator weights. The evaluation indicators include technical performance indicators, economic performance indicators, environmental impact indicators, and social benefit indicators. When selecting indicators, appropriate adjustments and supplements should be made based on the specific project characteristics and evaluation objectives. The indicator weights are then determined to establish a judgment matrix. For each level, a judgment matrix is constructed based on the relative importance of the elements within that level. For example, at the criterion level, the impact of each criterion on the objective needs to be compared, and a corresponding judgment matrix is constructed. The values of the elements in the matrix are determined by expert scoring. Scoring is typically based on expert knowledge, experience, and judgment. Common scoring scales include 1-9 and 1-5. Mathematical methods are used to calculate the maximum eigenvalue of the judgment matrix and its corresponding eigenvector. The eigenvector represents the weight of each element. The judgment matrix is then tested for consistency, typically using the consistency index (CI) and random index ratio (RI). If the CR (CI / RI) is less than 0.1, the judgment matrix is considered acceptable for consistency.

[0030] The mathematical method uses the eigenvalue method to calculate and establish a judgment matrix. First, it is necessary to establish a judgment matrix A based on the relative importance of each indicator. Assuming there are n indicators, A is an n×n matrix, where a_ij represents the relative importance of indicator i relative to indicator j;

[0031] Calculate the eigenvalues and eigenvectors of the judgment matrix, and perform the following calculations on the judgment matrix A: calculate the eigenvalues and eigenvectors of the judgment matrix A, find the largest eigenvalue λ_max and its corresponding eigenvector, then normalize the eigenvectors, perform consistency tests to ensure the rationality and consistency of the expert scores, and calculate the following indicators: consistency index (CI): CI = (λ_max-n) / (n-1), random consistency ratio (CR): CR = CI / RI, where RI is the random consistency index, which is obtained by looking up the table according to the order of the matrix. If CR < 0.1, the consistency of the judgment matrix is considered acceptable, otherwise the judgment matrix needs to be adjusted.

[0032] The following is the specific mathematical calculation process: calculate the eigenvalue and eigenvector, find the largest eigenvalue λ_max, normalize the corresponding eigenvector to obtain the weight vector W, and perform consistency check;

[0033] Calculate the eigenvalues and eigenvectors of A;

[0034] Assume that the maximum eigenvalue is λ_max=6.237, and the corresponding eigenvector is [0.624, 0.281, 0.095].

[0035] Normalized feature vector: W = [0.624 / 1.000, 0.281 / 1.000, 0.095 / 1.000] = [0.624, 0.281, 0.095].

[0036] Calculated CI = (6.237-3) / (3-1) = 1.118.

[0037] From the table, we get RI (for a 3×3 matrix) = 0.58.

[0038] Calculation: CR = CI / RI = 1.118 / 0.58 ≈ 1.931.

[0039] Since CR>0.1, we need to adjust the judgment matrix to improve its consistency. Finally, the weights are determined by solving the eigenvalues and eigenvectors of the judgment matrix in the above way.

[0040] The evaluation method module adopts fuzzy comprehensive evaluation method, which includes determining the evaluation factor set and determining various factors that affect the evaluation object to form a factor set U:

[0041] The factor set U includes the following factors:

[0042] U={u1,u2,u3,u4,u5}

[0043] u1: Construction cost

[0044] u2: Project operation efficiency

[0045] u3: Environmental impact

[0046] u4: Social Impact

[0047] u5: risk resistance;

[0048] Create a review collection:

[0049] Review Set V includes the following levels:

[0050] V={v1,v2,v3,v4}

[0051] v1: Very good

[0052] v2: Good

[0053] v3: General

[0054] v4: Poor

[0055] Determine the level division of the evaluation results to form a comment set V;

[0056] Determine the weight set:

[0057] Based on expert opinions and data analysis, weight set A is determined:

[0058] A={a1,a2,a3,a4,a5}

[0059] a1=0.2(project construction cost)

[0060] a2=0.3(project operation efficiency)

[0061] a3=0.2(environmental impact)

[0062] a4=0.2 (social impact)

[0063] a5=0.1 (risk resistance) Assign weights according to the importance of each factor to form a weight set A; establish a fuzzy relationship matrix:

[0064] Through questionnaires, expert ratings and other methods, the fuzzy relationship matrix R is established. For example:

[0065]

[0066] Fuzzy synthesis:

[0067] Use fuzzy synthesis operators (such as multiplication and addition) to calculate the comprehensive evaluation result B:

[0068] B=A*R

[0069] Step 6: Processing of evaluation results

[0070] Perform defuzzification on B, such as using the maximum membership principle, to determine the final evaluation result. For example:

[0071]

[0072] According to the maximum membership principle, the evaluation result is "good" (v2), because 0.38 is the maximum membership to obtain a specific evaluation result.

[0073] The early warning model establishment and training module includes model establishment and model training. The model establishment adopts the autoregressive moving average model.

[0074] The autoregressive model assumes that the current value is related to a linear combination of past values, which can be expressed as

[0075]

[0076] where X t is the value of the time series at time point t, c is the constant term (intercept), φ i is the autoregressive coefficient, p is the order of the autoregressive term, ∈ t is the white noise error term;

[0077] The moving average model assumes that the current value is related to a linear combination of past forecast errors and can be expressed as:

[0078]

[0079] Where μ is the mean of the time series, θ i is the moving average coefficient, q is the order of the moving average term, ∈ t is the prediction error at time point t;

[0080] The autoregressive moving average model combines the characteristics of the AR and MA models and can be expressed as:

[0081]

[0082] Where p and q are the orders of the autoregressive term and the moving average term, respectively.

[0083] When training the autoregressive moving average model, input the processed data, determine the order of the ARMA model (p and q), and observe the ACF graph to determine the order of the MA part;

[0084] If the ACF graph suddenly cuts off after a certain order, it may indicate that the order is the appropriate MA order. Observe the PACF graph to determine the order of the AR part. If the PACF graph suddenly cuts off after a certain order, it may indicate that the order is the appropriate AR order. Use an optimization algorithm (such as gradient descent, Newton's method, etc.) to optimize the parameters to minimize the residual and ensure that the optimization algorithm converges.

[0085] Parameter estimation is stable, check whether the model residuals meet the white noise assumption, check the degree of fit of the model, such as by calculating the adjusted R square value, use a part of the data as a test set, evaluate the predictive ability of the model, and once the model passes the verification, it can be used for prediction. Through the above steps, the training of the ARMA model can be completed and applied to the actual time series forecasting task.

[0086] Example 3

[0087] like Figure 1As shown, the present invention proposes a water conservancy project comprehensive performance evaluation and early warning system. Compared with the first embodiment, this embodiment also includes:

[0088] System architecture design

[0089] Requirements analysis: Clarify the functional requirements, performance requirements, and security requirements of the early warning system.

[0090] Module division: Divide the system into different modules, such as data collection, data processing, early warning analysis, early warning release, etc.

[0091] Architecture design: Design the overall architecture of the system, including hardware architecture and software architecture. The hardware architecture may include sensor networks, data acquisition equipment, servers, etc. The software architecture may include front-end display, back-end processing, database storage, etc.

[0092] Interface design: define the interface and communication method between modules within the system, and define the interface between the system and external systems (such as monitoring system, management system, etc.).

[0093] Security design: Ensure that the system has data security and network security measures, and design user authority management and access control mechanisms.

[0094] Software development and function implementation

[0095] Front-end development: Develop user interfaces, provide user-friendly human-computer interaction experience, and implement data display and operation functions.

[0096] Back-end development: implement data collection, data processing, early warning analysis and other functions, write business logic code, and process data flow and business logic.

[0097] Database design: Design the database structure to store the data required by the early warning system and implement operations such as adding, deleting, modifying, and checking data.

[0098] System integration: Integrate the front-end, back-end and database to realize the functions of the entire system, conduct system testing, and ensure the coordination between various modules.

[0099] System deployment: Deploy the system to the server or hardware device and conduct final testing before the system goes online to ensure stable system operation.

[0100] System maintenance: The system is monitored and maintained daily, and regularly updated and upgraded to meet new requirements and functions.

[0101] Through the above steps, the development and implementation of the early warning system can be completed, providing effective early warning and management tools for water conservancy projects.

[0102] The above specific embodiments are only several preferred embodiments of the present invention. Based on the technical solutions of the present invention and the relevant inspirations of the above embodiments, those skilled in the art can make various alternative improvements and combinations to the above specific embodiments.

[0103] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above and that the invention can be embodied in other specific forms without departing from the spirit or essential characteristics of the invention. Therefore, the embodiments should be considered in all respects as illustrative and non-restrictive, and the scope of the invention is defined by the appended claims, not the foregoing description, and all variations within the meaning and range of equivalents of the claims are intended to be included therein. Any reference sign in a claim should not be construed as limiting the claim to which it relates.

Claims

1. A water conservancy project comprehensive performance evaluation and early warning system, comprising a data processing module, an evaluation index construction module, an evaluation method module, and an early warning model establishment and training module, characterized in that: The data processing module processes the source data of the water conservancy project, and the processed data is input into the evaluation index construction module. Through the evaluation process and result analysis of the evaluation method module, the key factor data affecting the comprehensive performance of the water conservancy project is established. The data processed through the above steps is input into the early warning model establishment and training module to timely discover potential risks, make early warnings and take preventive measures, thereby ensuring the safe operation and long-term stability of the project.

2. A water conservancy project comprehensive performance evaluation and early warning system according to claim 1, characterized in that: The data processing module includes data collection and data processing. The data collection includes monitoring data, engineering data, socio-economic data and environmental data. The data collection methods include sensor monitoring, manual inspection, remote sensing technology and archive query. The collection frequency is determined according to the data type and importance. Real-time monitoring data may need to be collected continuously or at a high frequency, while other data may be collected monthly, quarterly or annually.

3. A water conservancy project comprehensive performance evaluation and early warning system according to claim 2, characterized in that: The data processing includes deleting obviously erroneous, abnormal or incomplete data, filling missing values through interpolation, averaging, regression and other methods, ensuring that all data use the same measurement unit, normalizing or standardizing the data to make them comparable, combining data from different sources, such as monitoring data and remote sensing data, and integrating data from different time and space scales into a unified spatiotemporal framework.

4. A water conservancy project comprehensive performance evaluation and early warning system according to claim 1, characterized in that: The evaluation index construction module includes the selection of evaluation indicators and the determination of indicator weights. The selection of evaluation indicators includes technical performance indicators, economic performance indicators, environmental impact indicators, and social benefit indicators. When selecting indicators, appropriate adjustments and supplements should be made according to the characteristics of the specific project and the purpose of the evaluation. The indicator weights are determined to establish a judgment matrix. For each level, a judgment matrix needs to be established based on the relative importance of the elements within the level. For example, in the criterion layer, it is necessary to compare the degree of influence of each criterion on the target and construct a corresponding judgment matrix. The element values in the matrix are determined by expert scoring. Scoring is usually based on the knowledge, experience, and judgment of experts. Commonly used scoring scales include 1-9 scales, 1-5 scales, etc. The maximum eigenvalue of the judgment matrix and its corresponding eigenvector are calculated using mathematical methods. The eigenvector is the weight of each element. The judgment matrix is tested for consistency, usually using the consistency index (CI) and random consistency ratio (RI). If CR (CI / RI) is less than 0.1, the consistency of the judgment matrix is considered acceptable. Finally, a comprehensive performance evaluation model is constructed based on the characteristics of the water conservancy project.

5. A water conservancy project comprehensive performance evaluation and early warning system according to claim 4, characterized in that: The mathematical method adopts the eigenvalue method to calculate, establish a judgment matrix, calculate the eigenvalues and eigenvectors of the judgment matrix, normalize the eigenvectors, perform consistency checks to ensure the rationality and consistency of the expert scores, and finally determine the weights by solving the eigenvalues and eigenvectors of the judgment matrix.

6. A water conservancy project comprehensive performance evaluation and early warning system according to claim 1, characterized in that: The evaluation method module adopts a fuzzy comprehensive evaluation method, and its steps include determining an evaluation factor set, establishing a comment set, determining a weight set, establishing a fuzzy relationship matrix, fuzzy synthesis, and finally defuzzifying the comprehensive evaluation result to obtain a specific evaluation result.

7. A water conservancy project comprehensive performance evaluation and early warning system according to claim 1, characterized in that: The early warning model establishment and training module includes model establishment and model training. The model establishment adopts an autoregressive moving average model. When training the autoregressive moving average model, the processed data is input, and then the order (p and q) of the ARMA model is determined. The optimization algorithm (such as gradient descent, Newton method, etc.) is used to optimize the parameters to minimize the residual, ensure the convergence of the optimization algorithm, and stabilize the parameter estimation. Check whether the model residual meets the white noise assumption. The autocorrelation diagram of the residual and the Ljung-Box Q test can be used to check the degree of fit of the model, such as by calculating the adjusted R square value and using a part of the data as a test set to evaluate the prediction ability of the model. If the data volume is large enough, cross-validation can be used to evaluate the performance of the verification model. After verification, the model can be used for prediction, such as predicting a single value in the future and providing a confidence interval for the predicted value. Through the above steps, the training of the ARMA model can be completed and applied to actual time series prediction tasks.

Images