Water resource multi-dimensional efficiency evaluation and improvement method driven by multi-source data fusion

Abstract

The invention discloses a water resource multi-dimensional efficiency evaluation and improvement method driven by multi-source data fusion, and relates to the technical field of water resource management and data processing. The method comprises the following steps: acquiring multi-source data of an evaluation object through a data acquisition device, and performing time identification, space identification and index attribute identification on the multi-source data to form an original data set; and based on the time identifier and the space identifier, performing scale mapping on the original data set to form a unified data field. According to the water resource multi-dimensional efficiency evaluation and improvement method driven by multi-source data fusion, the problems of data islands and information asymmetry in traditional water resource management are solved. Through comprehensive integration and analysis of hydrological data, water environment data, engineering operation data and water resource management behavior data, accurate evaluation of the overall efficiency of the water resource system is realized, and scientificity and rationality of water resource configuration and management are improved.

Description

Technical Field

[0001] This invention belongs to the field of water resources management and data processing technology, and in particular relates to a method for multi-dimensional performance evaluation and improvement of water resources driven by multi-source data fusion. Background Technology

[0002] Currently, water resource management technologies revolve around hydrological monitoring, water use statistics, water environment monitoring, and engineering scheduling. Water resource efficiency evaluation is based on the analysis of rainfall, runoff, water level, water quality indicators, and water intake and consumption data. Relevant data sources include hydrological stations, remote sensing monitoring equipment, online water quality monitoring devices, and industry management systems. Some technologies aggregate the above data into an information platform for centralized storage and perform statistical calculations on an annual or scheduling cycle basis to generate evaluation results such as water resource utilization rate, water allocation rationality, and engineering operation status. In some application scenarios, multi-source data fusion is used to support regional water resource management, analyzing the effectiveness of water resource allocation through rule models or indicator systems to provide decision-making basis for management departments. Relatively stable application models have been formed in water resource management practice, covering multiple dimensions such as water quantity, water quality, and water project operation.

[0003] Existing technologies still have some problems in multi-dimensional water resource performance evaluation. For example, multi-source data fusion remains at the data aggregation level and has not formed a unified fusion mechanism for performance evaluation. Hydrological data, water environment data, engineering operation data, and management behavior data differ in time scale, spatial resolution, and indicator structure. Existing methods process them independently and then compare the results. The evaluation process is mainly based on a single dimension, which makes it difficult to reflect the overall operating status of the water resource system.

[0004] To address these issues, we provide a multi-source data fusion-driven method for evaluating and improving the multi-dimensional effectiveness of water resources. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a method for evaluating and improving the multi-dimensional effectiveness of water resources driven by multi-source data fusion. The technical problem this invention aims to solve is: how to achieve a comprehensive evaluation and optimization of the multi-dimensional effectiveness of water resources through the combination of multi-source data fusion and cross-scale collaborative mapping mechanisms, thereby improving the efficiency and accuracy of water resource management.

[0006] To solve the above-mentioned technical problems, the present invention is achieved through the following technical solution.

[0007] This invention is a method for multi-dimensional performance evaluation and improvement of water resources driven by multi-source data fusion, including S1. acquiring multi-source data of the evaluation object through a data acquisition device, and performing time identification, spatial identification and indicator attribute identification on the multi-source data to form an original data set.

[0008] S2. Based on the time identifier and the spatial identifier, scale mapping is performed on the original data set to form a unified data domain. The scale mapping adopts a cross-scale collaborative mapping mechanism.

[0009] S3. Based on the unified data domain, feature extraction is performed on the multi-source data to form a multi-dimensional performance-related feature vector.

[0010] S4. Perform classification calculation on the multidimensional performance-related feature vectors, and output the multidimensional performance evaluation results through the classification calculation.

[0011] S5. Based on the multi-dimensional performance evaluation results, adjust the parameters of the evaluation object to generate performance optimization suggestions.

[0012] The present invention is further configured such that the data acquisition device includes a water level sensor, a water quality sensor, and a data interface; the multi-source data includes hydrological data, water environment data, engineering operation data, and water resource management behavior data; the time identifier timestamps and sorts the multi-source data to form time-ordered data; the spatial identifier spatially labels the multi-source data to form spatially labeled data; and the indicator attribute identifier classifies the multi-source data by indicator category to form indicator classification data. The time-ordered data, the spatially labeled data, and the indicator classification data are integrated to form the original data set.

[0013] The present invention is further configured such that the cross-scale collaborative mapping mechanism performs time identifier consistency judgment on the original data set according to the time identifier, obtains time identifier inconsistent data items through the time identifier consistency judgment, the time identifier consistency judgment is based on a unified time benchmark, and maps the time identifier inconsistent data items to the unified time benchmark.

[0014] The present invention is further configured such that the cross-scale collaborative mapping mechanism performs spatial identifier consistency judgment on the original data set according to the spatial identifier, obtains spatial identifier inconsistent data items through the spatial identifier consistency judgment, the spatial identifier consistency judgment uses a unified spatial benchmark as the judgment basis, and maps the spatial identifier inconsistent data items to the unified spatial benchmark, and organizes the unified time benchmark and the unified spatial benchmark in a unified manner to form the unified data domain.

[0015] The present invention is further configured such that the feature extraction includes the following steps: S31. Based on the unified data domain, extract time dimension features from the multi-source data, and obtain a time feature set through the time dimension feature extraction.

[0016] S32. Based on the unified data domain, spatial dimension features are extracted from the multi-source data to obtain a spatial feature set.

[0017] S33. Based on the unified data domain, extract indicator attribute dimension features from the multi-source data, and obtain an attribute feature set through the indicator attribute dimension feature extraction.

[0018] S34. The time feature set, the spatial feature set, and the attribute feature set are associated and organized to form the multidimensional performance-related feature vector.

[0019] The present invention is further configured such that the classification calculation divides the feature components of the multidimensional performance correlation feature vector into hydrological feature components, water environment feature components, engineering operation feature components, and management behavior feature components, and performs numerical summary calculation on the feature components to output the multidimensional performance evaluation result.

[0020] The present invention is further configured such that the model formula for the numerical summarization calculation is: .

[0021] Where E represents the multidimensional performance evaluation result, which is dimensionless. The efficiency result of the hydrological characteristic components is dimensionless. The efficiency result of the water environment characteristic components is dimensionless. The efficiency sub-result of the characteristic components of the project operation is dimensionless. The effectiveness sub-result of the management behavior characteristic component is dimensionless, and K is the coordination adjustment coefficient, which is dimensionless and satisfies 0≤k≤1.

[0022] The present invention is further configured such that the parameter adjustment is based on the multidimensional performance evaluation results, and the operating parameters and management parameters of the evaluated object are modified by adjusting the values ​​of the parameters and management parameters. The modification of the values ​​and configuration is based on the relative differences of the various performance sub-results of the multidimensional performance evaluation results.

[0023] The beneficial effects of this invention are as follows: This invention solves the problems of data silos and information asymmetry in traditional water resource management by using a multi-source data fusion-driven method for multi-dimensional performance evaluation and improvement of water resources. Through comprehensive integration and analysis of hydrological data, water environment data, engineering operation data, and water resource management behavior data, it achieves accurate evaluation of the overall performance of the water resource system, thereby improving the scientificity and rationality of water resource allocation and management.

[0024] This invention employs a cross-scale collaborative mapping mechanism to uniformly organize and process data at different time and spatial scales, eliminating data inconsistencies and discrepancies. Through this mechanism, water resource efficiency is comprehensively assessed across multiple dimensions, and targeted optimization suggestions are provided based on the assessment results. This offers more accurate data support for water resource management and decision-making, thereby improving water resource utilization efficiency and environmental protection effectiveness. Attached Figure Description

[0025] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below.

[0026] Figure 1 This is a diagram of the cross-scale collaborative mapping mechanism of the present invention.

[0027] Figure 2 This is the multidimensional feature extraction and association diagram of the present invention.

[0028] Figure 3 This is a classification calculation and performance evaluation diagram for the present invention. Detailed Implementation

[0029] The technical solutions of the present invention will be described below with reference to the accompanying drawings. The described embodiments are only some embodiments of the present invention, and not all embodiments.

[0030] Example 1 Please see Figures 1-3 This invention provides a method for multi-dimensional performance evaluation and improvement of water resources driven by multi-source data fusion, comprising: S1. Acquiring multi-source data of the evaluation object through a data acquisition device, and performing time-stamping, spatial-stamping, and indicator attribute-stamping on the multi-source data to form an original data set. The data acquisition device includes a water level sensor, a water quality sensor, and a data interface. The multi-source data includes hydrological data, water environment data, engineering operation data, and water resource management behavior data. Time stamping sorts the multi-source data by timestamp to form time-ordered data. Spatial stamping marks the spatial location of the multi-source data to form spatially labeled data. Indicator attribute stamping classifies the multi-source data by indicator category to form indicator-classified data. The time-ordered data, spatially labeled data, and indicator-classified data are integrated to form the original data set.

[0031] Water level data collection: At 12:00 on December 20, 2024, the water level of a reservoir was 12.5 meters. Water level sensors monitored water level changes in real time and recorded the data. Water level data was collected again every 30 minutes. At 12:30, the water level was 12.7 meters, with a timestamp of 12:30 on December 20, 2024.

[0032] Water quality data acquisition: Water quality sensors record water quality parameters simultaneously. At 12:00, the pH value at a certain measuring point was 7.4, dissolved oxygen was 8.2 mg / L, and turbidity was 5.6 NTU. Data was recorded hourly. At 12:30, dissolved oxygen increased to 8.4 mg / L, while pH and turbidity remained unchanged.

[0033] Environmental data collection: Environmental monitoring equipment monitors meteorological changes in the area. At 12:00 on December 20, 2024, precipitation was 0 mm and the temperature was 18℃. At 12:30, the temperature rose to 19℃ and precipitation increased to 2 mm.

[0034] S2. Based on time and spatial identifiers, the original dataset is scaled to form a unified data domain. This scale mapping employs a cross-scale collaborative mapping mechanism. The mechanism performs time identifier consistency checks on the original dataset, identifying inconsistent data items based on a unified time reference, and mapping these inconsistent data items to that unified time reference. Similarly, the mechanism performs spatial identifier consistency checks on the original dataset, identifying inconsistent data items based on a unified spatial reference, and mapping these inconsistent data items to that unified spatial reference. Finally, the unified time and spatial references are organized to form a unified data domain.

[0035] Time stamp processing: The timestamps generated by all data acquisition devices will be unified to UTC time, and all data items will be sorted in chronological order. If the timestamp of the water level data is 12:00:30, while the timestamp of the water quality data is 12:00:00, then the time of the water level data will be adjusted to 12:00:00 to align with the water quality data using interpolation, and missing values ​​will be filled in using interpolation.

[0036] Spatial Identification Processing: Each data point is identified using the latitude and longitude recorded by GPS positioning. A water level sensor is located at longitude 34.0522°N, latitude 118.2437°W, while a water quality sensor may be located in different locations. Spatial identification consistency is determined; if the spatial error between the two data points is less than a certain threshold, they are considered to have the same spatial identification. For inconsistent spatial data items, the system applies a coordinate transformation algorithm to correct them, mapping them to a unified spatial coordinate system, such as the WGS-84 standard.

[0037] Indicator Attribute Labeling: Each data point is categorized and labeled according to its type. Water level data is labeled "Water Level Monitoring," water quality data is labeled "Water Quality Monitoring," and meteorological data is labeled "Meteorological Monitoring." This data type labeling will provide a basis for subsequent analysis.

[0038] S3. Feature extraction is performed on multi-source data based on a unified data domain to form a multi-dimensional performance-related feature vector. Feature extraction includes the following steps: S31. Based on a unified data domain, extract time-dimensional features from multi-source data to obtain a time feature set.

[0039] S32. Based on a unified data domain, spatial dimension features are extracted from multi-source data to obtain a set of spatial features.

[0040] S33. Based on a unified data domain, extract indicator attribute dimension features from multi-source data, and obtain an attribute feature set through indicator attribute dimension feature extraction.

[0041] S34. Organize the time feature set, spatial feature set, and attribute feature set together to form a multidimensional performance-related feature vector.

[0042] S4. Classify and calculate the multidimensional performance correlation feature vectors, and output the multidimensional performance evaluation results through classification calculation. The classification calculation divides the feature components of the multidimensional performance correlation feature vectors into hydrological feature components, water environment feature components, engineering operation feature components, and management behavior feature components. Numerical summarization calculation is then performed on these feature components to output the multidimensional performance evaluation results. The model formula for numerical summarization calculation is: Where E represents the multidimensional performance evaluation result, which is dimensionless. The efficacy sub-results for hydrological characteristic components are dimensionless. The efficacy component of the water environment characteristic components is dimensionless. The efficiency sub-results of the engineering operation characteristic components are dimensionless. The effectiveness sub-result of the management behavior characteristic component is dimensionless, and K is the coordination adjustment coefficient, which is dimensionless and satisfies 0≤k≤1.

[0043] S5. Based on the multi-dimensional performance evaluation results, adjust the parameters of the evaluated object to generate performance optimization suggestions. Parameter adjustment involves modifying the values ​​of the operating and management parameters of the evaluated object according to the multi-dimensional performance evaluation results. The modification is based on the relative differences between the various performance sub-results of the multi-dimensional performance evaluation results.

[0044] Example 2 Please see Figure 2 Based on Example 1, by extracting features from multi-source data in terms of time, space and indicator attributes, a multi-dimensional performance correlation feature vector is formed, providing data support for the multi-dimensional performance evaluation of water resources.

[0045] 1. Time dimension feature extraction The temporal dimension reflects the trend of water resource system changes over time. Temporal features are extracted from hydrological and water quality data within a unified data domain.

[0046] The water level sensor recorded a water level of 12.5 meters at 12:00 on December 25, 2024, 12.7 meters at 12:30, and 13.0 meters at 12:45. Based on time-dimensional feature extraction, the following features were calculated: Water level change rate: Calculated by the difference in water level at different points in time. The water level change rate between 12:00 and 12:30 is (12.7-12.5) / 12.5=0.016, which means the water level rise rate is 1.6%.

[0047] Time series trend: Time series analysis methods are used to analyze the trend of water level changes.

[0048] 2. Spatial Dimension Feature Extraction Spatial dimensional features reflect the spatial distribution characteristics of data, including differences in water quality and water level data across different geographical locations. Spatial dimensional features are extracted by analyzing the spatial distribution of water quality and water level data.

[0049] Water level data collection points in a certain area are located in two different geographical locations: Water level monitoring point A: Longitude 34.0522°N, Latitude 118.2437°W, water level 12.5 meters. Water level monitoring point B: Longitude 34.0510°N, Latitude 118.2450°W, water level 13.0 meters.

[0050] Through spatial dimension feature extraction, the system calculates the following features: Water level difference: Water level difference = 13.0 meters - 12.5 meters = 0.5 meters, which represents the difference in water level between two locations.

[0051] Spatial correlation: By using spatial interpolation algorithms, the water level of unsampled points is predicted, reflecting the spatial characteristics of water level changes in the entire region.

[0052] 3. Feature extraction of indicator attributes The indicator attribute dimension features mainly focus on extracting indicator attributes related to water quality and water resource management behaviors. The extraction of water quality data and water resource management behavior data includes the following: Water quality characteristics: Water quality monitoring data shows that the water quality parameters of a certain water body at 12:00 on December 25, 2024 are as follows: pH value: 7.4, dissolved oxygen: 8.2 mg / L, turbidity: 5.6 NTU.

[0053] By extracting water quality data from different times and sampling points, the following characteristics were obtained: Water quality stability: The stability of water quality is reflected by calculating the pH fluctuation range at different time points. A pH fluctuation range of 6.9 to 7.5 within 24 hours indicates relatively low water quality stability.

[0054] Dissolved oxygen level: The oxygen content of a water body is reflected by calculating the average value and standard deviation of dissolved oxygen. The average dissolved oxygen value over the past 24 hours was 8.0 mg / L, and the standard deviation was 0.3 mg / L, indicating that the oxygen content of the water body is stable.

[0055] Water resource management behavior characteristics: Water resource management behavior data includes daily reservoir scheduling information. The scheduling data for December 25, 2024 is as follows: Reservoir scheduling: 10,000 cubic meters of water released; management behavior score: 85 points.

[0056] By extracting characteristics from water resource management behaviors, the following indicators were calculated: Dispatch efficiency: By analyzing the water resource dispatch efficiency at different time points, the rationality of water resource allocation is assessed. If the reservoir discharge volume increases daily over the past week, it indicates excessive management and adjustments are needed.

[0057] 4. Formation of Multidimensional Performance-Related Feature Vectors By extracting features from the time dimension, spatial dimension, and indicator attribute dimension, a multi-dimensional performance correlation feature vector is formed. The water resource performance evaluation feature vector for a certain region includes the following: Temporal characteristics: water level change rate and trend. Spatial characteristics: water level differences and spatial correlation. Indicator attribute characteristics: pH value, dissolved oxygen, and dispatch efficiency.

[0058] The feature vectors are aggregated and combined into a multidimensional performance-related feature vector, providing basic data for subsequent performance evaluation.

[0059] Example 3 Please see Figure 3 Based on Examples 1 and 2, by classifying, calculating and weighting the characteristic components such as hydrology, environment, engineering operation and management behavior, a comprehensive multi-dimensional performance evaluation result is obtained, providing a basis for the optimization of water resources management.

[0060] Water level data: Example data: At 12:00 on December 25, 2024, the water level was 12.5 meters; at 12:30 on December 25, 2024, the water level was 12.7 meters; at 13:00 on December 25, 2024, the water level was 13.0 meters.

[0061] Data acquisition method: Water level data acquired in real time through the reservoir monitoring system.

[0062] Water quality data: Example data: At 12:00 on December 25, 2024, the pH value was 7.4, the dissolved oxygen value was 8.2 mg / L, and the turbidity was 5.6 NTU. At 12:30 on December 25, 2024, the pH value was 7.5, the dissolved oxygen value was 8.4 mg / L, and the turbidity was 5.5 NTU.

[0063] Data acquisition method: Water quality data are provided by water quality monitoring stations or environmental protection departments.

[0064] Project operation data: Example data: Water release: 10,000 cubic meters, reservoir scheduling efficiency: 80%.

[0065] Data acquisition method: The engineering operation data is provided by the water conservancy project management department, covering the actual operating status of facilities such as reservoirs and dams, such as water release and pump station operation.

[0066] Management behavior data: Example data: Management behavior score: 85 points.

[0067] Data Acquisition Method: Management behavior scoring is typically conducted by relevant government or management departments. This scoring is based on factors such as water resource allocation, the rationality of management decisions, and water-saving effectiveness.

[0068] Calculation process: The effectiveness sub-result for hydrological characteristics is 0.85, the effectiveness sub-result for water environment characteristics is 0.75, the effectiveness sub-result for engineering operation characteristics is 0.80, and the effectiveness sub-result for management behavior characteristics is 0.90.

[0069] Overall performance calculation: Substitute the efficiency results of each characteristic component into the following formula for calculation: The adjustment coefficient k = 0.1.

[0070] Calculate the average value of each characteristic component: Calculate the difference between each characteristic component and the mean: Calculate the variance and take its square root: Substitute into the formula to calculate the final performance result The calculated efficiency evaluation result is 0.0413, indicating that the overall efficiency of the water resources management system is low.

[0071] The foregoing has only described certain exemplary embodiments of the present invention by way of illustration. Undoubtedly, those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the foregoing drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

Claims

1. A multi-source data fusion-driven method for multi-dimensional evaluation and improvement of water resource efficiency. Includes, characterized in that: S1. Acquire multi-source data of the evaluation object through a data acquisition device, and perform time identification, spatial identification and indicator attribute identification on the multi-source data to form an original data set; S2. Based on the time identifier and the spatial identifier, scale mapping is performed on the original data set to form a unified data domain. The scale mapping adopts a cross-scale collaborative mapping mechanism. S3. Based on the unified data domain, feature extraction is performed on the multi-source data to form a multi-dimensional performance-related feature vector; S4. Perform classification calculation on the multidimensional performance-related feature vectors, and output the multidimensional performance evaluation results through the classification calculation; S5. Based on the multi-dimensional performance evaluation results, adjust the parameters of the evaluation object to generate performance optimization suggestions.

2. The method for multi-source data fusion-driven multi-dimensional performance evaluation and improvement of water resources according to claim 1, characterized in that: The data acquisition device includes a water level sensor, a water quality sensor, and a data interface. The multi-source data includes hydrological data, water environment data, engineering operation data, and water resource management behavior data. The time identifier sorts the multi-source data by timestamp to form time-ordered data. The spatial identifier marks the spatial location of the multi-source data to form spatially labeled data. The indicator attribute identifier classifies the multi-source data by indicator category to form indicator classification data. The time-ordered data, the spatially labeled data, and the indicator classification data are integrated to form the original data set.

3. The method for multi-source data fusion-driven multi-dimensional performance evaluation and improvement of water resources according to claim 1, characterized in that: The cross-scale collaborative mapping mechanism performs time signature consistency judgment on the original data set according to the time signature, obtains data items with inconsistent time signatures through the time signature consistency judgment, the time signature consistency judgment is based on a unified time reference, and maps the data items with inconsistent time signatures to the unified time reference.

4. The method for multi-source data fusion-driven multi-dimensional performance evaluation and improvement of water resources according to claim 3, characterized in that: The cross-scale collaborative mapping mechanism performs spatial identifier consistency judgment on the original data set according to the spatial identifier, obtains spatial identifier inconsistent data items through the spatial identifier consistency judgment, the spatial identifier consistency judgment is based on a unified spatial benchmark, and maps the spatial identifier inconsistent data items to the unified spatial benchmark, and organizes the unified time benchmark and the unified spatial benchmark in a unified manner to form the unified data domain.

5. The method for multi-source data fusion-driven multi-dimensional performance evaluation and improvement of water resources according to claim 1, characterized in that: The feature extraction includes the following steps: S31. Based on the unified data domain, extract time dimension features from the multi-source data, and obtain a time feature set through the time dimension feature extraction; S32. Based on the unified data domain, spatial dimension features are extracted from the multi-source data to obtain a spatial feature set; S33. Based on the unified data domain, extract indicator attribute dimension features from the multi-source data, and obtain an attribute feature set through the indicator attribute dimension feature extraction; S34. The time feature set, the spatial feature set, and the attribute feature set are associated and organized to form the multidimensional performance-related feature vector.

6. The method for multi-source data fusion-driven multi-dimensional performance evaluation and improvement of water resources according to claim 1, characterized in that: The classification calculation divides the feature components of the multidimensional performance correlation feature vector into hydrological feature components, water environment feature components, engineering operation feature components, and management behavior feature components, and performs numerical summary calculation on the feature components to output the multidimensional performance evaluation results.

7. The method for multi-source data fusion-driven multi-dimensional performance evaluation and improvement of water resources according to claim 6, characterized in that: The formula for the numerical aggregation calculation model is as follows: ， Wherein, E represents the multidimensional performance evaluation result. The effectiveness sub-result of the hydrological characteristic components, The efficiency sub-results of the aforementioned water environment characteristic components, The efficiency sub-result of the aforementioned engineering operation characteristic components, K is the effectiveness sub-result of the management behavior characteristic component, where K is the coordination adjustment coefficient and satisfies 0≤k≤1.

8. The method for multi-source data fusion-driven multi-dimensional performance evaluation and improvement of water resources according to claim 1, characterized in that: The parameter adjustment is based on the multidimensional performance evaluation results, and the values ​​of the operating parameters and management parameters of the evaluated object are modified accordingly. The modification of the value configuration is based on the relative differences of each performance sub-result of the multidimensional performance evaluation results.

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